In an effort to facilitate increased online multimedia content, Transactions on Wireless Communications (TWC) has established a new Online Editorial Team. One to two articles will be selected each month. The team will be working with authors to create slides, scripts, and video clips, showcasing innovative and exciting ideas from these articles. Emil Bjornson, Michele Wigger, and Zhu Han will serve as TWC’s Inaugural Editors for this new team; please welcome them to the TWC team.

As compared with 2D wireless camera sensor networks (WCSNs), 3D WCSNs can capture more accurate and comprehensive information for exposure-path prevention in supervisory and military applications. However, 3D WCSNs impose many new challenges for energy-efficiency and interference-mitigation subject to required coverage rate constraint due to extensive power consumption over time-varying wireless channels. To overcome the above-mentioned problems, in this paper we propose the AQ-DBPSK/DS-CDMA (alternating quadratures differential binary phase shift keying/direct-sequence code division multiple access) based cooperative MIMO energy-efficient and interference-mitigating scheme under the constraint of optimal tradeoff between power consumption and coverage rate over multi-hop clustered WCSNs. In particular, we build sensing models to characterize the minimum coverage rate constraint and formulate the exposure-path prevention problem using the percolation theory. Then, we derive the critical density of camera sensors subject to minimum exposure-path prevention probability constraint. We develop the AQ-DBPSK/DS-CDMA scheme and also derive the optimal data rate to optimize transmit-power and data-rate trade-off. By deriving the critical density of camera sensors over 3D WCSNs, we apply the cooperative MIMO based NEW LEACH architecture for our multi-hop cooperative MIMO scheme. Also conducted is a set of simulations which show that our proposed scheme can outperform other existing schemes in terms of energy efficiency and interference-mitigation over multi-hop 3D clustered WCSNs.

This paper considers the uplink of a massive MIMO communication system using 5G New Radio-compliant multiple access, which has to co-exist with a radar system using the same frequency band. A system model taking into account the reverberation (clutter) produced by the radar system onto the massive MIMO receiver is proposed. In this scenario, several receivers for uplink channel estimation and data detection are proposed, ranging from the simple channel-matched beamformer to the zero-forcing and linear minimum mean square error receivers for clutter disturbance rejection, under the two opposite situations of perfectly known and completely unknown clutter covariance. A theoretical analysis is also provided, deriving a lower bound on the achievable uplink spectral efficiency and the mutual information between the input Gaussian-encoded symbols and the observables available at the communication receiver of the cellular massive MIMO system: regarding the latter, in particular, it is shown that, in the large antenna number regime, and under the assumption of perfect channel state information (CSI), the effect of radar clutter at the base station is suppressed and single-user capacity may be restored. Numerical results, illustrating the performance of the proposed detection schemes, confirm the findings of the theoretical analysis, and permit quantifying the system robustness to clutter effect for increasing number of antennas at the base station.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV’s flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

This paper analyzes the impact of non-Gaussian multipath component (MPC) amplitude distributions on the performance of Compressed Sensing (CS) channel estimators for OFDM systems. The number of dominant MPCs that any CS algorithm needs to estimate in order to accurately represent the channel is characterized. This number relates to a Compressibility Index (CI) of the channel that depends on the fourth moment of the MPC amplitude distribution. A connection between the Mean Squared Error (MSE) of any CS estimation algorithm and the MPC amplitude distribution fourth moment is revealed that shows a smaller number of MPCs is needed to well-estimate channels when these components have large fourth moment amplitude gains. The analytical results are validated via simulations for channels with lognormal MPCs such as the NYU mmWave channel model. These simulations show that when the MPC amplitude distribution has a high fourth moment, the well known CS algorithm of Orthogonal Matching Pursuit performs almost identically to the Basis Pursuit De-Noising algorithm with a much lower computational cost.

To meet the ever-increasing demands for mobile video streaming, proactive caching over the network edge has been proposed as a promising solution for next generation wireless networks. In this paper, we consider the trace-driven cache-enabled video streaming design in the scenario of a metropolis to boost the spectral efficiency on the system side and the quality of experience (QoE) on the user side. A novel scheme to jointly provide proactive caching, power allocation, user association and adaptive video streaming is designed via the formation of a QoE-aware throughput maximization problem. Specifically, the caches are refreshed in the content placement phase according to the resource status and expected traffic, which is obtained by exploring the traces collected over a big city. In addition, users need to be associated with a proper small base station (SBS) in the content delivering phase to provide the highest attainable rate. We demonstrate the effectiveness of the proposed scheme via experiments conducted over real user trace datasets.

Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements.

Rendezvous is a fundamental building block in distributed cognitive-radio networks (CRNs), in which pairs or groups of users must find a jointly available channel. Research on the rendezvous problem has focused so far on minimizing the time to rendezvous (to find a suitable channel) or on maximizing the rendezvous degree (percentage of channels on which rendezvous can take place). In this paper, we model the rendezvous problem in a more realistic way that acknowledges the fact that available channels may suffer from interference which varies from location to location as well as over time. In other words, channels are influenced by heterogeneous interference. In this setting, CRNs benefit from rendezvous methods that find a quiet channel, which supports high symbol rates and does not suffer much from dropped packets. In this paper, we propose three important rendezvous design disciplines to achieve bounded rendezvous time, full rendezvous degree, and to rendezvous on quiet channels that suffer little interference. We first present the Disjoint Relaxed Difference Set (DRDS) based rendezvous algorithm as a cornerstone, which ensures rendezvous on every channel (full rendezvous degree) in bounded time. When channels suffer from heterogeneous interference, we propose the Interference based DRDS (I-DRDS) algorithm which ensures rendezvous on channels with less interference, incorporating the interference normalization and interference mapping methods. We conduct extensive simulations to evaluate the proposed algorithms; compared with the state-of-the-art algorithms, the results show that I-DRDS has the best rendezvous performance on less interfered channels, with slightly larger rendezvous time.

Due to their low complexity, Minimum Mean Squared Error (MMSE) and Zero-Forcing (ZF) emerge as two appealing MIMO receivers. Although they provide asymptotically the same achievable rate as the signal-to-noise ratio (SNR) grows large, a non-vanishing gap between the signal to interference and noise ratio (SINR) obtained through the two receivers exists, affecting the error and outage probability, and the multiuser efficiency. Interestingly, both the SINR and the multiuser efficiency gaps can be compactly expressed as quadratic forms of random matrices, with a kernel that depends solely on the statistics of the interfering streams. By leveraging, we derive the closed-form distribution of such indefinite quadratic forms with random kernel matrix, which turns out to be proportional to the determinant of a matrix containing the system parameters. Then, specializing our result to different fading conditions, we obtain the closed-form statistics of both the SINR gap and the multiuser efficiency gap. Although the focus of this work is on the finite-size statistics, for completeness we also provide some results on the doubly-massive MIMO case. We validate all our derivations through extensive Monte Carlo simulations.

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

Amplitude-coherent (AC) detection is an efficient technique that can simplify the receiver design while providing reliable symbol error rate (SER). Therefore, this work considers AC detector design and SER analysis using M-ary amplitude shift keying (MASK) modulation with receiver diversity over Rician fading channels. More specifically, we derive the optimum, near-optimum and a suboptimum AC detectors and compare their SER with the coherent, phase-coherent, noncoherent and the heuristic AC detectors. Moreover, the analytical and asymptotic SER at high signal-to-noise ratios (SNRs) are derived for the heuristic detector using single and multiple receiving antennas. The obtained analytical and simulation results show that the SER of the AC and coherent MASK detectors are comparable, particularly for high values of the Rician K-factor, and small number of receiving antennas. In most of the considered scenarios, the heuristic AC detector outperforms the optimum noncoherent detector significantly, except for the binary ASK case at low SNRs. Moreover, the obtained results show that the heuristic AC detector is immune to phase noise, and thus, it outperforms the coherent detector in scenarios where the system is subject to considerable phase noise.

Centralized coded caching and delivery is studied for a radio access combination network (RACN), whereby a set of H edge nodes (ENs), connected to a cloud server via orthogonal fronthaul links with limited capacity, serve a total of K user equipments (UEs) over wireless links. The cloud server is assumed to hold a library of N files, each of size F bits; and each user, equipped with a cache of size $\mu _{\text {R}} {\it\text { N F}}$ bits, is connected to a distinct set of r ENs each of which equipped with a cache of size $\mu _{\text {T}} {\it\text { N F}}$ bits, where $\mu _{\text {T}}$ , $\mu _{\text {R}} \in [{0,1}]$ are the fractional cache capacities of the UEs and the ENs, respectively. The objective is to minimize the normalized delivery time (NDT), which refers to the worst case delivery latency when each user requests a single distinct file from the library. Three coded caching and transmission schemes are considered, namely the MDS-IA , soft-transfer and zero-forcing (ZF) schemes. MDS-IA utilizes maximum distance separable (MDS) codes in the placement phase and real interference alignment (IA) in the delivery phase. The achievable NDT for this scheme is presented for $\text {r}=2$ and arbitrary fractional cache sizes $\mu _{\text {T}}$ and $\mu _{\text {R}}$ , and also for arbitrary value of r and fractional cache size $\mu _{\text {T}}$ when the cache capacity of the UE is above a certain threshold. The soft-transfer scheme utilizes soft-transfer of coded symbols to ENs that implement ZF over the edge links. The achievable NDT for this scheme is presented for arbitrary r and arbitrary fractional cache sizes $\mu _{\text {T}}$ and $\mu _{\text {R}}$ . The last scheme utilizes ZF between the ENs and the UEs without the participation of the cloud server in the delivery phase. The achievable NDT for this scheme is presented for an arbitrary value of r when the total cache size at a pair of UE and EN is sufficient to store the whole library, i.e., $\mu _{\text {T}}+\mu _{\text {R}} \geq 1$ . The results indicate that the fronthaul capacity determines which scheme achieves a better performance in terms of the NDT, and the soft-transfer scheme becomes favorable as the fronthaul capacity increases.

In this paper, a new optimization framework is presented for the joint design of user selection, power allocation, and precoding in multi-cell multi-user multiple-input multiple-output (MU-MIMO) systems when imperfect channel state information at transmitter (CSIT) is available. By representing the joint optimization variables in a higher-dimensional space, the weighted sum-spectral efficiency maximization is formulated as the maximization of the product of Rayleigh quotients. Although this is still a non-convex problem, a computationally efficient algorithm, referred to as generalized power iteration precoding (GPIP), is proposed. The algorithm converges to a stationary point (local maximum) of the objective function and therefore it guarantees the first-order optimality of the solution. By adjusting the weights in the weighted sum-spectral efficiency, the GPIP yields a joint solution for user selection, power allocation, and downlink precoding. The GPIP can be extended to the multi-cell scenario where cooperative base stations perform joint user-cell selection and design their precodes by taking into account the inter-cell interference by sharing global imperfect CSIT. System-level simulations show the gains of the proposed approach with respect to conventional user selection and linear downlink precoding.

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIP) that can detach the users from their virtual world. To measure the BIP for wireless VR users, a novel model that jointly considers the VR application type, transmission delay, VR video quality, and users’ awareness of the virtual environment is proposed. In the developed model, base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to provide high data rates for the VR users, thus, reducing the number of BIP for each user. Since the body movements of a VR user may result in a blockage of its wireless link, the location and orientation of VR users must also be considered when minimizing BIP. The BIP minimization problem is formulated as an optimization problem which jointly considers the predictions of users’ locations, orientations, and their BS association. To predict the orientation and locations of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users’ locations and orientations. Using these predictions, the user association policy that minimizes BIP is derived. Simulation results demonstrate that the developed algorithm reduces the users’ BIP by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

In this paper, a novel opportunistic spectrum sharing scheme, based on orthogonal frequency division multiplexing with index modulation (OFDM-IM), is proposed for cognitive radio (CR) networks. In the considered OFDM-IM based CR (OFDM-IM-CR) model, the primary transmitter (PT) communicates with the primary receiver with the aid of an amplify-and-forward (AF) relay by transmitting OFDM-IM signals. Meanwhile, the secondary transmitter (ST) passively senses the spectrum and transmits its own information over those inactive subcarriers of the primary network to the secondary receiver if the signal-to-noise ratio of the PT $\to $ ST link is above a predefined threshold; otherwise, the ST stays in silent mode. Two different types of maximum-likelihood (ML) detectors are designed for the primary network, based on the knowledge of either the estimated channel state information or the statistical channel information of the secondary network. A complexity-reducing method, which is applicable to both types and achieves near optimal performance, is further proposed. To evaluate the performance, a tight upper bound on the bit error rate (BER) is derived, assuming the first type of ML detection. Simulation results corroborate the analysis and show that OFDM-IM-CR has the potential of outperforming OFDM-CR and OFDM-IM-AF in terms of BER with higher spectral efficiency.

We propose a unified approach to approximate the probability density function and the cumulative distribution function of a sum of independent channel envelopes following generalized, and possibly mixed, fading models. In the proposed approach, the approximate sum distribution can be chosen out of a broad class of statistical models. More fundamentally, given any chosen model, the parameters of the approximate sum distribution are calibrated by matching its asymptotic behavior around zero to that of the exact sum. In a subsidiary fashion, one or more moments of the exact and approximate sums are also matched to one another if the approximate distribution has three or more parameters to be adjusted, respectively. As illustrated through many numerical examples, our approach outperforms existing ones that are solely based on moment matching, by yielding statistical approximations that are remarkably accurate at medium to high signal-to-noise ratio — a paramount operational regime for communications systems. Our results find applicability in several wireless applications where fading sums arise, and can be readily extended to accommodate sums of fading (power) gains and, in a broader context, generic sums of non-negative random variables.

Proactive edge caching, as a promising approach to accommodate the explosively increased mobile data demand of Video on demand (VoD) service, has received extensive attention. However, although targeted to VoD service, existing caching policies are mainly designed for file downloading service while the unique requirement of quality of experience (QoE) for VoD service has been seldom considered. Aimed at maximizing the weighted average QoE of VoD service, this paper optimizes the proactive edge caching polices including the cached fraction and encoding bit rate of every video. We consider a two-tier network, where the helpers equipped with caching resource are deployed in the coverage area of traditional base stations. We formulate the caching optimization problems and show their hidden convexity properties, then we find that the optimal caching policy is determined by the weighted popularity-to-duration ratio of videos. Based on the result, we develop a low-complexity algorithm to find the optimal caching policy. Simulation results demonstrate evident performance gain of the proposed policy over the existing policy for VoD service.

Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (e.g., on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs’ energy consumption and task execution time. The problem is challenging due to the combinatorial nature of the offloading decisions among all tasks and the strong coupling with resource allocation. To tackle this problem, we first assume that the offloading decisions are given and derive the closed-form expressions of the optimal offloading transmit power and local CPU frequencies. Then, an efficient bi-section search method is proposed to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimal offloading decisions. We then extend the investigation to a general multi-user scenario, where the input of a task at one WD requires the final task outputs from multiple other WDs. Numerical results show that the proposed method can significantly outperform the other representative benchmarks and efficiently achieve low complexity with respect to the call graph size.

Cooperative transmission is a promising technology to expand communication range and improve system performance for underwater optical wireless communications (UOWCs). To adequately exploit the benefits of cooperative UOWCs, the relay selection and power allocation problems require to be explored. This paper investigates the relay selection and power allocation issues for the cooperative UOWC in the presence of solar radiation noise. Specifically, the cooperative UOWC based on amplify-and-forward (AF) relaying is modeled, where the effects of absorption, scattering, and solar radiation noise are all considered. We design two optimization schemes that minimize the energy consumption and maximize the overall signal-to-noise ratio (SNR), respectively. Both schemes are proved to be strictly quasi-convex and solved by Levenberg-Marquardt (LM) algorithm. The effectiveness of the proposed schemes is evaluated in line-of-sight (LOS) links and non-line-of-sight (NLOS) links, and the main factor affecting the relay selection and power allocation are derived for both LOS links and NLOS links.

We study in this paper the secrecy performance of the multiple-input multiple-output (MIMO) visible light communication (VLC) wiretap channel. The underlying system model comprises three nodes: one transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver and one eavesdropper, each equipped with multiple photo-diodes (PDs). The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel and the eavesdropper is assumed to be randomly located in the coverage area. We propose a low-complexity precoding scheme that aims at enhancing the secrecy performance of the system. Specifically, assuming discrete input signaling, we derive an average achievable secrecy rate for the underlying system in a closed-form, and the derived expression is a function of the precoding matrix and the input distribution using stochastic geometry. Then, we propose a low-complexity design of the precoding matrix based on the generalized singular value decomposition (GSVD) of the channel matrices of the system. We examine the resulting average achievable secrecy rate using the truncated discrete generalized normal (TDGN) distribution, which is the best-known discrete distribution available in the literature. Finally, we validate the proposed scheme through extensive simulations and we demonstrate its superiority when compared to other schemes reported in the literature.

This paper investigates coherent detection for physical-layer network coding (PNC) with short packet transmissions in a two-way relay channel (TWRC). PNC turns superimposed EM waves into network-coded messages to improve throughput in a relay system. To achieve this, accurate channel information at the relay is a necessity. Much prior work applies preambles to estimate the channel. For long packets , the preamble overhead is low because of the large data payload. For short packets , that is not the case. To avoid excessive overhead, we consider a set-up in which short packets do not have preambles. A key challenge is how the relay can estimate the channel and detect the network-coded messages jointly based on the received signals from the two end users. We design a coherent detector that makes use of a belief propagation (BP) algorithm to do so. For concreteness, we focus on binary frequency-shift-keying (FSK) modulation. We show how the BP algorithm can be simplified and made practical with Gaussian-mixture passing. In addition, we demonstrate that prior knowledge on the channel distribution is not needed with our framework. Benchmarked against the detector with prior knowledge of the channel distribution, numerical results show that our detector can have nearly the same performance without such prior knowledge.

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC.

We study multi-hop broadcast in wireless networks with one source node and multiple receiving nodes. The message flow from the source to the receivers can be modeled as a tree-graph, called broadcast-tree. The problem of finding the minimum-power broadcast-tree (MPBT) is NP-complete. Unlike most of the existing centralized approaches, we propose a decentralized algorithm, based on a non-cooperative cost-sharing game. In this game, every receiving node, as a player, chooses another node of the network as its respective transmitting node for receiving the message. Consequently, a cost is assigned to the receiving node based on the power imposed on its chosen transmitting node. In our model, the total required power at a transmitting node consists of (i) the transmit power and (ii) the circuitry power needed for communication hardware modules. We develop our algorithm using the marginal contribution (MC) cost-sharing scheme and show that the optimum broadcast-tree is always a Nash equilibrium (NE) of the game. Simulation results demonstrate that our proposed algorithm outperforms conventional algorithms for the MPBT problem. Besides, we show that the circuitry power, which is usually ignored by existing algorithms, significantly impacts the energy-efficiency of the network.

In this paper, we establish a foundation for the multi-layer aerial networks (MANs), which are modeled as $K$ layer aerial networks (ANs), where each layer has unmanned aerial vehicles (UAVs) with different densities, floating altitudes, and transmission power. To make the framework applicable for various scenarios in MAN, we consider the transmitter- and the receiver-oriented node association rules as well as the air-to-ground and air-to-air channel models, which form line of sight links with a location-dependent probability. We then newly analyze the association probability, the main link distance distribution, successful transmission probability (STP), and area spectral efficiency (ASE) of MAN. The upper bounds of the optimal densities that maximize STP and ASE are also provided. Finally, in the numerical results, we show the optimal UAV densities of each AN that maximize the ASE and the STP decrease with the altitude of the network. We also show that when the total UAV density is fixed for two layer AN, the use of single layer in higher(lower) altitude only for all UAVs can achieve better performance for low(high) total density case. Otherwise, distributing UAVs in two layers, i.e., MAN, achieves better performance.

In a cloud radio access network (C-RAN), distributed remote radio heads (RRHs) are coordinated by baseband units (BBUs) in the cloud. The centralization of signal processing provides flexibility for coordinated multipoint transmission (CoMP) of RRHs to cooperatively serve user equipments (UEs). We target enhancing UEs’ capacity performance, by jointly optimizing the selection of RRHs for serving UEs, i.e., CoMP selection, and resource allocation. We analyze the computational complexity of the problem. Next, we prove that under fixed CoMP selection, the optimal resource allocation amounts to solving a so-called iterated function. Towards user-centric network optimization, we propose an algorithm for the joint optimization problem, aiming at scaling up the capacity maximally for any target UE group of interest. The proposed algorithm enables network-level performance evaluation for quality of experience.

The paper introduces a cross-layer design for transmission of Successive Refinement (SR) source code interplayed with non-orthogonal layered code, deployed over a half-duplex multi-relay Non-orthogonal Amplify-Forward (NAF) network. Assuming the Channel State Information (CSI) is not available at the source node, firstly the achievable layered Diversity-Multiplexing Tradeoff (DMT) curve is derived. Then, by taking Distortion Exponent (DE) as the figure of merit, several achievable lower bounds are proved and the optimal expected distortion performance under high Signal to Noise Ratio (SNR) approximation is explicitly obtained. It is shown that the proposed coding can achieve the Multi-Input Single-Output (MISO) upper bound under certain regions of bandwidth ratios, by which the optimal performance in these regions can be explicitly characterized. Further the non-orthogonal layered coding scheme is extended to a multi-hop MIMO Decode-Forward (DF) relay network and set of DE lower bounds is derived. Simulation results confirm the effectiveness of the proposed scheme under general SNR values and compared with its counterparts.

In this paper, we study the energy efficiency of small-cell on/off control schemes in which small-cells turn on when the number of active users reaches a certain threshold value called on-threshold. In particular, we derive analytical formulas for various performance metrics such as expected small-cell power consumption, average consumed energy per transmitted bit, expected energy saving, and energy saving probability. Moreover, we show that the expected small-cell power consumption strictly decreases with increased on-threshold value and derive a mathematical expression for the optimal on-threshold value that minimizes the average consumed energy per transmitted bit. With simulation results, we not only verify the validity of the analytical results but also discuss how the theoretical results can be used in system performance evaluation and optimization. We believe that many of the results in this paper can be used in practical small-cell network design and operation.

To enable realistic studies of massive multiple-input multiple-output systems, the COST 2100 channel model is extended based on measurements. First, the concept of a base station-side visibility region (BS-VR) is proposed to model the appearance and disappearance of clusters when using a physically-large array. We find that BS-VR lifetimes are exponentially distributed, and that the number of BS-VRs is Poisson distributed with mean proportional to the sum of the array length and the mean lifetime. Simulations suggest that under certain conditions longer lifetimes can help decorrelating closely-located users. Second, the concept of a multipath component visibility region (MPC-VR) is proposed to model birth-death processes of individual MPCs at the mobile station side. We find that both MPC lifetimes and MPC-VR radii are lognormally distributed. Simulations suggest that unless MPC-VRs are applied the channel condition number is overestimated. Key statistical properties of the proposed extensions, e.g., autocorrelation functions, maximum likelihood estimators, and Cramer-Rao bounds, are derived and analyzed.

On-demand deployment of small cells plays a key role in augmenting macro-cell coverage for outdoor hotspots, where user devices are brought together and intensively upload self-generated data. In this paper, we study spectrum sharing among rapidly deployable small cells in the uplink, even without a priori global knowledge. We propose a hybrid multi-agent approach, which allows a leading macro-cell base station (MBS) and multiple following small base stations (SBSs) to take part in a user-centric, online joint optimization of small cell deployment and uplink resource allocation. Specifically, we propose a centralized mechanism for the MBS to solve the first subproblem of small cell deployment stage by stage, based on an adversarial bandit model. Furthermore, we propose a distributed mechanism for the group of SBSs to collectively solve the second subproblem of uplink resource allocation stage by stage, based on a stochastic game model. We prove that our approach is guaranteed to produce a joint strategy, which is built upon a mixed strategy with bounded regret on the first tier and an equilibrium solution on the second tier. Our approach is validated by simulations on the aspects of convergence behavior, strategy correctness, power consumption, and spectral efficiency.

In this work, we investigate the performance of a millimeter waves (mmWaves) cellular system with free space optical (FSO) backhauling. MmWave channels are subject to Nakagami-m fading while the optical links experience the Double Generalized Gamma including atmospheric turbulence, path loss and the misalignment between the transmitter and the receiver aperture (also known as the pointing errors). The FSO model also takes into account the receiver detection technique which could be either heterodyne or intensity modulation and direct detection (IM/DD). Each user equipment (UE) has to be associated to one serving base station (BS) based on the received signal strength (RSS) or Channel State Information (CSI). We assume partial relay selection (PRS) with CSI based on mmWaves channels to select the BS associated with the highest received CSI. Each serving BS decodes the received signal for denoising, converts it into modulated FSO signal, and then forwards it to the data center. Thereby, each BS can be viewed as a decode-and-forward (DF) relay. In practice, the relay hardware suffers from nonlinear high power amplification (HPA) impairments which, substantially degrade the system performance. In this work, we will discuss the impacts of three common HPA impairments named respectively, soft envelope limiter (SEL), traveling wave tube amplifier (TWTA), and solid state power amplifier (SSPA). Novel closed-forms and tight upper bounds of the outage probability, the probability of error, and the achievable rate are derived. Capitalizing on these performance, we derive the high SNR asymptotes to get engineering insights into the system gain such as the diversity order.

The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.

In this paper, the performance evaluation of TCP New Reno over satellite DVB-RCS2 links with random access is analyzed. Based on the satellite system consisting of a large number of source-destination TCP New Reno pairs, a Markov chain model for a TCP connection is conducted. According to the principles of TCP New Reno, two one-step transition probabilities for the value of congestion window in different data transmission phases (e.g. the slow start phase and congestion avoidance phase) are proposed. In order to analysis the throughput of a TCP connection, according to the packets lost events, three conditions (no packet lost, packets lost in the congestion avoidance phase, and packets lost in the slow start phase), are theoretically analyzed. We analytically derive the upper and lower throughput bounds, simulations and analysis are integrated together to indicate the effective ness and correctness of theoretical analysis. Moreover, we can clearly determine which factor plays role for the performance of TCP New Reno. The analysis not only includes the main algorithms of TCP protocol, but also is developed for general random access. Therefore, the Markov model and the theoretical analysis can be applied to many erasure channel, and the conclusions are extremely critical for the research of improving TCP performance over links with random access.

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What’s worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent , a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in real-world deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regression-based prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent ’s effectiveness in enabling on-demand low-latency edge intelligence.

This paper studies an unmanned aerial vehicle (UAV)-enabled multiple access channel (MAC), in which multiple ground users transmit individual messages to a mobile UAV in the sky. We consider a linear topology scenario, where these users locate in a straight line and the UAV flies at a fixed altitude above the line connecting them. Under this setup, we jointly optimize the one-dimensional (1D) UAV trajectory and wireless resource allocation to reveal the fundamental rate limits of the UAV-enabled MAC, under the users’ individual maximum power constraints and the UAV’s maximum flight speed constraints. First, we consider the capacity-achieving non-orthogonal multiple access (NOMA) transmission with successive interference cancellation (SIC) at the UAV receiver. In this case, we characterize the capacity region by maximizing the average sum-rate of all users subject to a set of rate profile constraints. To optimally solve this highly non-convex problem with infinitely many UAV location variables over time, we show that any speed-constrained UAV trajectory is equivalent to the combination of a maximum-speed flying trajectory and a speed-free trajectory, and accordingly transform the original speed-constrained trajectory optimization problem into a speed-free problem that is optimally solvable via the Lagrange dual decomposition. It is rigorously proved that the optimal 1D trajectory solution follows the successive hover-and-fly (SHF) structure, i.e., the UAV successively hovers above a number of optimized locations, and flies unidirectionally among them at the maximum speed. Next, we consider two orthogonal multiple access (OMA) transmission schemes, i.e., frequency-division multiple access (FDMA) and time-division multiple access (TDMA). We maximize the achievable rate regions in the two cases by jointly optimizing the 1D trajectory design and wireless resource (frequency/time) allocation. It is shown that the optimal trajectory solutions still follow the SHF structure but with different hovering locations for each scheme. Finally, numerical results show that the proposed optimal trajectory designs achieve considerable rate gains over other benchmark schemes, and the capacity region achieved by NOMA significantly outperforms the rate regions by FDMA and TDMA.

The average performance of the MISO downlink channel, with a large number of users compared to transmit antennas of the base station, depends on the interference management which necessitates the joint design of scheduling and precoding. Unlike the previous works which do not offer a truly joint design, this paper focuses on formulating a problem amenable for the joint update of scheduling and precoding. Novel optimization formulations are investigated to reveal the hidden difference of convex/ concave structure for three classical criteria (weighted sum rate, max-min signal-to-interference plus noise ratio, and power minimization) and associated constraints are considered. Thereafter, we propose a convex-concave procedure framework based iterative algorithm where scheduling and precoding variables are updated jointly in each iteration. Finally, we show the superiority in performance of joint solution over the state-of-the-art designs through Monte-Carlo simulations.

To leverage rich data distributed at the network edge, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low-latency multi-access scheme for edge learning. To this end, we consider a popular privacy-preserving framework, federated edge learning (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. It is proposed that the updates simultaneously transmitted by devices over broadband channels should be analog aggregated “over-the-air” by exploiting the waveform-superposition property of a multi-access channel. Such broadband analog aggregation (BAA) results in dramatical communication-latency reduction compared with the conventional orthogonal access (i.e., OFDMA). In this work, the effects of BAA on learning performance are quantified targeting a single-cell random network. First, we derive two tradeoffs between communication-and-learning metrics, which are useful for network planning and optimization. The power control (“truncated channel inversion”) required for BAA results in a tradeoff between the update-reliability [as measured by the receive signal-to-noise ratio (SNR)] and the expected update-truncation ratio. Consider the scheduling of cell-interior devices to constrain path loss. This gives rise to the other tradeoff between the receive SNR and fraction of data exploited in learning. Next, the latency-reduction ratio of the proposed BAA with respect to the traditional OFDMA scheme is proved to scale almost linearly with the device population. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results.

The Internet of things (IoT) is becoming more and more flexible and economical with the advancement in information and communication technologies. However, IoT networks will be ultra-dense with the explosive growth of IoT devices. Network function virtualization (NFV) emerges to provide flexible network frameworks and efficient resource management for the performance of IoT networks. In NFV-enabled IoT infrastructure, service function chain (SFC) is an ordered combination of virtual network functions (VNFs) that are related to each other based on the logic of IoT applications. However, the embedding process of SFC to IoT networks is becoming a big challenge due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this paper, we decompose the complex VNFs into smaller virtual network function components (VNFCs) to make more effective decisions since VNF nodes and IoT network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL) based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios in IoT. Our simulations consider different types of IoT network topologies. The simulation results present the efficiency of the proposed dynamic SFC embedding scheme.

There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

Cognitive radio and energy harvesting techniques have provided significant benefits in terms of spectrum reuse and lifetime prolongation for conventional wireless networks. We are thus motivated to consider the energy harvesting cognitive radio networks (CRNs) consisting of multiple primary users (PUs) and secondary users (SUs). We introduce two cooperation modes: the energy cooperation mode and joint cooperation mode. In the energy cooperation mode, there only exists energy cooperation between PUs and SUs, i.e., the SU transmits its own packets by using the energy harvested from primary signals. In the joint cooperation mode, the SU relays primary packets by using the energy harvested from primary signals. In each cooperation mode of three representational scenarios (the CRN with one pair of PUs and one pair of SUs, the CRN with two pairs of PUs and one pair of SUs, and the CRN with one pair of PUs and two pairs of SUs) and the general scenario, we exploit the optimal time allocation between PUs and SUs, and balance the tradeoff between energy harvesting and packet transmission to obtain the maximum total achievable throughput. To be specific, we first formulate the throughput maximization problems as non-linear optimization problems, and then prove them as convex problems by monotonicity analysis. Moreover, we obtain the closed-form optimal solution in the energy cooperation mode. We prove the existence of the optimal solution in the joint cooperation mode, obtain the upper and lower bounds, and provide numerical analysis for the optimal solution. Finally, we highlight the benefits of information cooperation and the impact of multi-user gain on the maximum of the total achievable throughput.

In this paper, we propose a series of novel coordination schemes for multi-cell downlink communication. Starting from full base station (BS) coordination, we first propose a fully-coordinated scheme to exploit beneficial effects of both inter-cell and intra-cell interference, based on sharing both channel state information (CSI) and data among the BSs. To reduce the coordination overhead, we then propose a partially-coordinated scheme where only intra-cell interference is designed to be constructive while inter-cell is jointly suppressed by the coordinated BSs. Accordingly, the coordination only involves CSI exchange and the need for sharing data is eliminated. To further reduce the coordination overhead, a third scheme is proposed, which only requires the knowledge of statistical inter-cell channels, at the cost of a slight increase on the transmission power. For all the proposed schemes, imperfect CSI is considered. We minimize the total transmission power in terms of probabilistic and deterministic optimizations. Explicitly, the former statistically satisfies the users’ signal-to-interference-plus-noise ratio (SINR) while the latter guarantees the SINR requirements in the worst case CSI uncertainties. Simulation verifies that our schemes consume much lower power compared to the existing benchmarks, i.e., coordinated multi-point (CoMP) and coordinated-beamforming (CBF) systems, opening a new dimension on multi-cell coordination.

Introducing mobile small cells to next generation cellular networks is nowadays a pervasive and cost-effective way to fulfill the ever-increasing mobile broadband traffic. Being agile and resilient, unmanned aerial vehicles (UAVs) mounting small cells are deemed emerging platforms for the provision of wireless services. As the residual battery capacity available to UAVs determines the lifetime of an airborne network, it is essential to account for the energy expenditure on various flying actions in a flight plan. The focus of this paper is therefore on studying the 3D deployment problem for a swarm of UAVs, with the goal of maximizing the total amount of data transmitted by UAVs. In particular, we address an interesting trade-off among flight altitude, energy expense and travel time. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. Afterwards, we prove the optimality of a special case derived from the convexification transformation. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can benefit from the proper treatment of the trade-off.

Massive multiple-input multiple-output (M-MIMO) is recognized as a promising technology for the next generation of wireless networks because of its potential to increase the spectral efficiency. In initial studies of M-MIMO, the system has been considered to be perfectly synchronized throughout the entire cells. However, perfect synchronization may be hard to attain in practice. Therefore, we study a M-MIMO system whose cells are not synchronous to each other, while transmissions in a cell are still synchronous. We analyze an asynchronous downlink M-MIMO system in terms of the coverage probability and the ergodic rate by means of the stochastic geometry tool. For comparison, we also obtain results for the corresponding synchronous system. In addition, we investigate the effect of the uplink power control and the number of pilot symbols on the downlink ergodic rate, and we observe that there is an optimal value for the number of pilot symbols maximizing the downlink ergodic rate of a cell. Our results also indicate that, compared to the synchronous system, the downlink ergodic rate is more sensitive to the uplink power control in the asynchronous mode.

This paper aims at investigating the problem of energy efficiency (EE) maximization for uplink multi-cell networks via a joint design of sub-channel assignment, power control, and antenna selection. We study the problem under two practical scenarios. In the first scenario, known as conventional antenna selection (CAS), there is only one radio frequency (RF) chain available at the mobile user and all the sub-channels for each user can be assigned to one of the antennas. For the second scenario, known as generalized antenna selection (GAS), the number of RF chains is equal to the number of antennas and the messages of each user can transmit over its assigned sub-channels via different antennas. The resource allocation design is formulated as a multi-objective optimization problem (MOOP) and then converted into a single objective optimization problem (SOOP) via the weighted Tchebycheff method. The considered problem is a mixed integer nonlinear programming (MINLP) which is generally intractable. To address this problem, a penalty function is introduced to handle the binary variable constraints. In order to obtain a computationally efficient suboptimal solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as the lower bound of the objective function. Furthermore, we propose another low-complexity practical algorithm to further reduce the computational cost. Simulation results demonstrate the superiority of the proposed method and unveil an interesting trade-off between EE and SE for two considered scenarios.

This paper proposes a novel spatial-modulated multicarrier sparse code-division multiple access (SM/MC-SCDMA) system for achieving massive connectivity in device-centric wireless communications. In our SM/MC-SCDMA system, the advantages of both MC signalling and SM are amalgamated to conceive a low-complexity transceiver. Sparse frequency-domain spreading is utilized to mitigate the peak-to-average power ratio (PAPR) of MC signalling, as well as to facilitate low-complexity detection using the message passing algorithm. We then analyze the single-user bit error rate performance of SM/MC-SCDMA systems communicating over frequency-selective fading channels. Furthermore, the performance of SM/MC-SCDMA systems is evaluated based on both Monte-Carlo simulations and analytical results. We demonstrate that our low-complexity SM/MC-SCDMA transceivers are capable of achieving near-maximum likelihood (ML) performance even when the normalized user-load is as high as two, hence constituting a variable solution to support massive connectivity in device-centric wireless systems.

This paper discusses incentive mechanism design for collaborative task offloading in mobile edge computing (MEC). Different from most existing work in the literature that was based on offline settings, in this paper, an online truthful mechanism integrating computation and communication resource allocation is proposed. In our system model, upon the arrival of a smartphone user who requests task offloading, the base station (BS) needs to make a decision right away without knowing any future information on i) whether to accept or reject this task offloading request and ii) if accepted, who to execute the task (the BS itself or nearby smartphone users called collaborators). By considering each task’s specific requirements in terms of data size, delay, and preference, we formulate a social-welfare-maximization problem, which integrates collaborator selection, communication and computation resource allocation, transmission and computation time scheduling, as well as pricing policy design. To solve this complicated problem, a novel online mechanism is proposed based on the primal-dual optimization framework. Theoretical analyses show that our mechanism can guarantee feasibility, truthfulness, and computational efficiency (competitive ratio of 3). We further use comprehensive simulations to validate our analyses and the properties of our proposed mechanism.

Radio frequency (RF) signals have been relied upon for both wireless information delivery and wireless charging to the massively deployed low-power Internet of Things (IoT) devices. Extensive efforts have been invested in physical layer and medium-access-control layer design for coordinating simultaneous wireless information and power transfer (SWIPT) in RF bands. Different from the existing works, we study the coding controlled SWIPT from the information theoretical perspective with practical transceiver. Due to its practical decoding implementation and its flexibility on the codeword structure, unary code is chosen for joint information and energy encoding. Wireless power transfer (WPT) performance in terms of energy harvested per binary sign and of battery overflow/underflow probability is maximised by optimising the codeword distribution of coded information source, while satisfying required wireless information transfer (WIT) performance in terms of mutual information. Furthermore, a Genetic Algorithm (GA) aided coding design is proposed to reduce the computational complexity. Numerical results characterise the SWIPT performance and validate the optimality of our proposed GA aided unary coding design.

To promote the revolution of Industrial Internet of Things, the next generation communication system is expected to provide latency critical services in industry. However, for the traditional downlink-centric cellular systems, the timely delivery of packets cannot be guaranteed by the default dynamic access scheme due to complex signaling procedure. A promising solution to low-latency access is the resource pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, however at the expense of low spectrum utilization. Aiming to make those pre-allocated resources more rewarding, a so-called DPre, a predictive pre-allocation scheme based on learning for low-latency uplink access in industrial wireless networks, is proposed in this paper. It intelligently explores the correlation of devices’ access behavior and device utility diversity through sequential learning. Thus, flexible and judicious per-allocation decisions in both time and frequency domains can be made in an on-demand manner. Moreover, with the proposed temporal-spatial utility metric, DPre is guaranteed to reserve for more informative devices. Both theoretical analysis and simulation validate its high spectrum utilization through accurate prediction and the potential to pre-allocate for valuable packets.

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, typically results in challenging mixed-integer nonlinear programming (MINLP) problems. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). In contrast to the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning . To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL , which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while providing significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

This paper investigates the impact of residual transceiver hardware impairments (RTHIs) on cooperative non-orthogonal multiple access (NOMA) networks, where generic $\alpha -\mu $ fading channel is considered. To be practical, imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) are taken into account. More particularly, two representative NOMA scenarios are proposed, namely non-cooperative NOMA and cooperative NOMA. For the non-cooperative NOMA, the base station (BS) directly performs NOMA with all users. For the cooperative NOMA, the BS communicates with NOMA users with the aid of an amplify-and-forward (AF) relay, and the direct links between BS and users are existent. To characterize the performance of the proposed networks, new closed-form and asymptotic expressions for the outage probability (OP), ergodic capacity (EC) and energy efficiency (EE) are derived, respectively. Specifically, we also design the relay location optimization algorithms from the perspectives of minimize the asymptotic OP. For non-cooperative NOMA, it is proved that the OP at high signal-to-noise ratios (SNRs) is a function of threshold, distortion noises, estimation errors and fading parameters, which results in 0 diversity order. In addition, high SNR slopes and high SNR power offsets achieved by users are studied. It is shown that there are rate ceilings for the EC at high SNRs due to estimation error and distortion noise, which cause 0 high SNR slopes and $\infty $ high SNR power offsets . For cooperative NOMA, similar results can be obtained, and it also demonstrates that the outage performance of cooperative NOMA scenario exceeds the non-cooperative NOMA scenario in the high SNR regime.

Recent developments in industrial wireless sensor networks (IWSNs) have revolutionized industrial automation systems. However, harsh industrial environment poses great challenges to a time-critical and reliable wireless communication. For instance, effects of multipath fading, noise and co-channel interference can have unpredictable and time-varying impacts on the propagation channel, leading to the failure of on-time packet delivery. To address this problem, in this paper we propose a channel-based Optimal Back-off Delay Control (OBDC) scheme which can minimize the total time a packet spends in the sensor node (TSN) by assessing the features of a generic wireless channel. Specifically, we first explore the channel impairments by investigating the probability density function (PDF) of the level crossing rate (LCR) of the received signal in the industrial wireless environment. Then, with the obtained channel assessment results, we develop a phase-type semi-Markov model to investigate the probability distribution of the back-off delay of a packet in the sensor node (SN). The probability distribution of the back-off delay can be further substituted with TSN according to the queuing theory. The proposed OBDC scheme examines the Kullback-Leibler (KL) divergence between the obtained distribution of TSN and the packet arrival rate, and reduces the TSN according to an objective function which is constantly renewed in every transmission round with regard to a delay constraint. The simulation results show that the OBDC scheme can reduce TSN and guarantee to keep the TSN in an acceptable range even though the wireless channel is impaired by interference effects. It also shows that the OBDC scheme can reduce the proportion of packets meeting their deadline to the total packets in transmission when the number of SN and LCR changes

Recent advances on WLAN technology have been focused mostly on boosting network capacity by means of a more efficient and flexible physical layer. A new concept is required at MAC level to exploit fully the new capabilities of the PHY layer. In this article, we propose a contention mechanism based on Repeated Contentions (ReCo) in frequency domain. It provides a simple-to-configure, robust and short-term fair algorithm for the random contention component of the MAC protocol. The throughput efficiency of ReCo is not sensitive to the number of contending stations, so that ReCo does not require adaptive tuning of the access parameters for performance optimization. Efficiency and robustness is gained through the power of repeated contention rounds. We also apply the ReCo concept to the emerging IEEE 802.11ax standard, showing how it can boost performance of random access with respect to the current version of IEEE 802.11ax OFDMA Back-Off (OBO). Our proposal is supported by an experimental test-bed that realizes ReCo by means of simultaneous transmission and reception of short tones, which is feasible on top of programmable OFDM PHY layers.

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In high mobility applications of millimeter wave (mmWave) communications, e.g., vehicle-to-everything communication and next-generation cellular communication, frequent link configuration can be a source of significant overhead. We use the sub-6 GHz channel covariance as an out-of-band side information for mmWave link configuration. Assuming: 1) a fully digital architecture at sub-6 GHz and 2) a hybrid analog–digital architecture at mmWave, we propose an out-of-band covariance translation approach and an out-of-band aided compressed covariance estimation approach. For covariance translation, we estimate the parameters of sub-6 GHz covariance and use them in theoretical expressions of covariance matrices to predict the mmWave covariance. For out-of-band aided covariance estimation, we use weighted sparse signal recovery to incorporate out-of-band information in compressed covariance estimation. The out-of-band covariance translation eliminates the in-band training completely, whereas out-of-band aided covariance estimation relies on in-band as well as out-of-band training. We also analyze the loss in the signal-to-noise ratio due to an imperfect estimate of the covariance. The simulation results show that the proposed covariance estimation strategies can reduce the training overhead compared to the in-band only covariance estimation.

Visible light communications (VLC) have emerged as a strong candidate for meeting the escalating demand for high data rates. In this paper, we consider a VLC network, where multiple access points (APs) serve both energy-harvesting users (EHUs), i.e., users who harvest energy from light emitted by diodes and information users (IUs), i.e., users who gather data information. In order to jointly balance the achievable sum rate at the IUs and the energy harvested by the EHUs, the paper considers maximizing a network-wide utility, which consists of a weighted sum of the IUs sum rate and the EHUs harvested energy, subject to individual IU rate constraint, individual EHU harvested-energy constraint, and AP power constraints, so as to jointly determine the direct current (DC) bias value at each AP, and the power of the alternating-current (AC) signals of the users. A difficult non-convex optimization problem is solved using an iterative approach which relies on inner convex approximations, and compensates for the used approximations using proper outer-loop updates. The paper further considers solving the special cases of the problem, i.e., maximizing the sum rate, and maximizing the total harvested-energy, both subject to the same constraints. Numerical results highlight the significant performance improvement of the proposed algorithms, and illustrate the impacts of the network parameters on the performance trade-off between the sum rate and harvested-energy.

Multi-antenna technologies have been widely used in modern wireless communication systems to achieve diversity gain, spatial multiplexing gain, and better interference suppression. Beamforming has been considered as a potential candidate for throughput maximization in MIMO cognitive radio networks. However, the efficient implementation of beamforming demands for accurate knowledge of channel estimate. Conventional spectrum sharing strategies treat the detection and estimation problems in uncoupled manner, which may not result in the overall optimum performance. In this paper, we propose a censored spectrum sharing strategy which considers both the detection and estimation performances simultaneously and is capable of improving the throughput of MIMO cognitive radio network. We derive analytical expressions for the critical parameters and provide simulation results to validate our derivations.

The development of modellings and analytical tools to structurise and study the allocation of resources through noble user competitions become essential, especially considering the increased degree of heterogeneity in application and service demands that will be cornerstone in future communication systems. Stochastic asymmetric Blotto games appear promising to modelling such problems, and devising their Nash equilibrium (NE) strategies by anticipating the potential outcomes of user competitions. In this regard, this paper approaches the generic energy efficiency problem with a new stochastic asymmetric Blotto game paradigm to enable the derivation of joint optimal bandwidth and transmit power allocations by setting multiple users to compete in multiple auction-like contests for their individual resource demands. The proposed modelling innovates by abstracting the notion of fairness from centrally-imposed to distributed-competitive, where each user’s pay-off probability is expressed as quantitative bidding metric, so as, all users’ actions can be interdependent, i.e., each user attains its utility given the allocations of other users, which eliminates the chance of low-valued carriers not being claimed by any user, and, in principle, enables the full utilisation of wireless resources. We also contribute by resolving the allocation problem with low complexity using new mathematical techniques based on Charnes-Cooper transformation, which eliminate the additional coefficients and multipliers that typically appear during optimisation analysis, and derive the joint optimal strategy as a set of linear single-variable functions for each user. We prove that our strategy converges towards a unique, monotonous and scalable NE, and examine its optimality, positivity and feasibility properties in detail. Simulation comparisons with relevant studies confirm the superiority of our approach in terms of higher energy efficiency performance, fairness index and quality-of-service provision.

Far-field wireless power transfer (WPT) and simultaneous wireless information and power transfer (SWIPT) have become increasingly important in radio frequency (RF) and communication communities recently. The problem of modulation design for SWIPT has however been scarcely addressed. In this paper, a modulation scheme based on asymmetric phase-shift keying (PSK) is considered, which improves the SWIPT rate-energy trade-off region significantly. The nonlinear rectifier model, which accurately models the energy harvester, is adopted for evaluating the output direct current (DC) power at the receiver. The harvested DC power is maximized under an average power constraint at the transmitter and a constraint on the rate of information transmitted via a multi-carrier signal over a flat fading channel. As a consequence of the rectifier nonlinearity, this work highlights that asymmetric PSK modulation provides benefits over conventional symmetric PSK modulation in SWIPT and opens the door to systematic modulation design tailored for SWIPT.

A new practical multiple-input multiple-output (MIMO) transceiver technique called spatially modulated integer-forcing (SM-IF) is proposed for hybrid beamforming array systems, which combines generalized spatial modulation (GSM) with integer-forcing (IF) MIMO multiplexing transmission to achieve improved spectral efficiency with low system complexity. In the proposed scheme, the transmitter first activates a subset of antennas using analog beamforming and delivers an SM information stream via the index of the activated antenna group. Then MIMO digital multiplexing is applied to send multiple MIMO information streams via multilevel coding (MLC) with binary channel codes through the activated antennas. At the receiver side, the receiver first estimates the index of the activated antenna group to recover the SM stream and then recovers the MIMO streams by IF sum decoding in conjunction with multi-stage decoding. The proposed scheme provides a unified coding framework to achieve a synergistic gain by integrating off-the-shelf practical binary codes and low complexity IF decoding into hybrid beamforming array systems. By extensive link-level numerical simulations, the proposed scheme is shown to outperform both the conventional plain IF MIMO and plain SM approaches. The results demonstrate that the proposed SM-IF can be a promising transceiver technique implementable in practical hybrid beamforming array systems.

Analog beamforming greatly reduces the implementation cost of massive antenna transceivers by using only one up/down-conversion chain. However, it incurs a large pilot overhead when used with conventional channel estimation (CE) techniques. This is because these CE techniques involve digital processing, requiring the up/down-conversion chain to be time-multiplexed across the antenna dimensions. This paper introduces a novel CE technique, called continuous analog channel estimation (CACE), that avoids digital processing, enables analog beamforming at the receiver and additionally provides resilience against oscillator phase-noise. By avoiding time-multiplexing of up/down-conversion chains, the CE overhead is reduced significantly and furthermore becomes independent of the number of antenna elements. In CACE, a reference tone is transmitted continuously with the data signals, and the receiver uses the received reference signal as a matched filter for combining the data signals, albeit via analog processing. We propose a receiver architecture for CACE, analyze its performance in the presence of oscillator phase-noise, and derive near-optimal system parameters and power allocation. Transmit beamforming and initial access procedure with CACE are also discussed. Simulations confirm that, in comparison to conventional CE, CACE provides phase-noise resilience and a significant reduction in the CE overhead, while suffering only a small loss in signal-to-interference-plus-noise-ratio.