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Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great expectation. They can be broadly divided into AI/ML techniques for network engineering and management, network designs for AI/ML applications, and system concepts. AI/ML techniques for networking and management improve the way we address networking. They support efficient, rapid, and trustworthy

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

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Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy

As a new standard traffic service for time-sensitive applications over beyond-5G (5G+) mobile wireless networks, ultra-reliable and low-latency communications (URLLC) have received tremendous research attention. The major design issue raised by URLLC is how to support latency-sensitive multimedia transmissions while guaranteeing the reliability bound over timevarying wireless channels. Along this direction

Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless

This paper investigates the passive beamforming and information transfer (PBIT) technique for multiuser multiple-input multiple-output (Mu-MIMO) systems with the aid of reconfigurable intelligent surfaces (RISs), where the RISs enhance the primary communication via passive beamforming (P-BF) and at the same time deliver additional information by the on-off reflecting modulation (in which the RIS information

Rate splitting (RS) is a potentially powerful and flexible technique for multi-antenna downlink transmission. In this paper, we address several technical challenges towards its practical implementation for beyond 5G systems. To this end, we focus on a single-cell system with a multi-antenna base station (BS) and $K$ single-antenna receivers. We consider RS in its most general form with $2^{K}-1$ streams

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5G is raising high expectations, offering new avenues for research, and paving the way to a safer and highperforming set of innovative use-cases. For use-cases related for instance to Internet of Things (IoT), self-driving vehicles, or unmanned aerial vehicles (UAVs), the programming and management of the underlying network resources will be carried out in a dynamic and scalable manner. In this vein

The prevailing wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Unfortunately, bugs, misconfigurations, faults or attacks can introduce inconsistencies between the network control and the data plane that can undermine the correct operation at runtime. Through our experiments

Software-Defined Networking (SDN) is an innovational network architecture which gives network administrators the ability to directly control the whole network by programming on a centralized controller. Due to network complexity, networks are unlikely to be bug-free. The ability to verify data plane properties will make network management easier for network administrators in SDN. In this paper, we

Software-Defined Networking (SDN) enables more dynamic and fine-grained network control. In particular, network operators can route traffic not only based on packet header fields, but also higher-level parameters such as user settings, traffic characteristics, and application-layer information extracted by virtualized network functions such as DPI, firewall and authentication servers. Integrating these

In this paper, we study the problem of data flow management in the presence of heterogeneous flows — elephant and mice flows — in software-defined networks (SDNs). Most of the researchers considered the homogeneous flows in SDN in the existing literature. The optimal data flow management in the presence of heterogeneous flows is NP-hard. Hence, we propose a game theory-based heterogeneous data flow

In this paper, we present an open source Software-Defined Networking (SDN) based emulation platform called Timber . We aim to provide the research community with an experimental tool for the design and evaluation of the new Quality of Experience (QoE) management and monitoring procedures for video streaming. To this aim, the main functionalities of Timber include: i) an SDN application for taking QoE-aware

Envisioned 5G applications and services, such as Tactile Internet, Industry 4.0 use-cases, remote control of drone swarms, pose serious challenges to the underlying networks and cloud platforms. On the one hand, evolved cloud infrastructures provide the IT basis for future applications. On the other hand, networking is in the middle of a momentous revolution and important changes are mainly driven

Programmable data planes, PDPs, enable an unprecedented level of flexibility and have emerged as a promising alternative to existing data planes. Despite the rapid development and prototyping cycles that PDPs promote, the existing PDP ecosystem lacks appropriate abstractions and algorithms to support these rapid testing and deployment life-cycles. In this paper, we propose P4Visor, a lightweight virtualization

5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality

Network function virtualization (NFV) of IP Multimedia Subsystem (IMS) pose promise to service increasing multimedia traffic demand. In this paper, we show that virtualized IMS (vIMS) is unable to provide session-level resilience under faults and becomes the bottleneck to high service availability. We propose a design to provide fault-tolerance for vIMS operations. In control-plane, our system decomposes

Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user’s personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this

Network slicing, as a key 5G enabling technology, is promising to support with more flexibility, agility, and intelligence towards the provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and large-dimensioned. This contradicts the dominant network slicing solutions that only customize immediate performance

As a novel sensing paradigm, crowdsensing has gained great attention due to large-scale user participation, low cost and wide data source, replacing traditional sensor based sensing in intelligent transportation, environmental monitoring, urban public management, etc. In crowdsensing, however, user privacy leakage is a common but fatal problem, where the participants in crowdsensing might not provide

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Network slices for delay-constrained applications in 5G systems require computing facilities at the edge of the network to guarantee ultra-low latency in processing data flows generated by connected devices, which is challenging with larger volumes of data, and larger distances to the edge of the network. To address this challenge, we propose to extend 5G network slices with Unmanned Aerial Vehicles

In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on

As a hybrid MIMO architecture, beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter wave (mmWave) massive MIMO systems without obvious performance loss, in which, however, the number of users supported cannot be larger than that of RF chains. To break this fundamental limit, we introduce the concept of code-domain

The article describes a numerical approach for Massive MIMO channel modeling that accounts for the effects of electromagnetic coupling between a user and the receiving device. The modeling is performed by a combination of the Finite-Difference Time-Domain and the Ray-Tracing methods, supplemented with a stochastic geometry model of the propagation environment. The influence of user-coupling on the

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy

Millimeter wave (mmWave) wideband channels in a multiple-input multiple-output (MIMO) transmission are described by a sparse set of impulse responses in the angle-delay, or space-time (ST), domain. These characteristics will be even more prominent in the THz band used in future systems. We consider two approaches for channel estimation: compressed-sensing (CS), exploiting the sparsity in the angular/delay

Multiple antenna technologies have attracted much research interest for several decades and have gradually made their way into mainstream communication systems. Two main benefits are adaptive beamforming gains and spatial multiplexing, leading to high data rates per user and per cell, especially when large antenna arrays are adopted. Since multiple antenna technology has become a key component of the

Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G). However, the underlying channels are not fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the outdoor, multiple-input multiple-output (MIMO), and vehicle-to-vehicle (V2V)

Multi-hop communication with the aid of large-scale antenna arrays will play a vital role in future emergence communication systems. In this paper, we investigate amplify-and-forward based and multiple-input multiple-output assisted multi-hop communication, in which all nodes employ hybrid transceivers. Moreover, channel errors are taken into account in our hybrid transceiver design. Based on the matrix-monotonic

Significant sampling is an adaptive monitoring technique proposed for highly dynamic networks with centralized network management and control systems. The essential spirit of significant sampling is to collect and disseminate network state information when it is of significant value to the optimal operation of the network, and in particular when it helps identify the shortest routes. Discovering the

Multipath transport protocols utilize multiple network paths (e.g., WiFi and cellular) to achieve improved performance and reliability, compared with their single-path counterparts. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths. However, state-of-the-art multipath schedulers face the challenge when dealing with heterogeneous paths

Joint pushing and caching are commonly considered an effective way to adapt to tidal effects in networks. However, the problem of how to precisely predict users’ future requests and push or cache the proper content remains to be solved. In this paper, we investigate a joint pushing and caching policy in a general mobile edge computing (MEC) network with multiuser and multicast data. We formulate the

In state-of-the-art deep learning, centralized deep learning forces end devices to pool their data in the cloud in order to train a global model on the joint data, while distributed deep learning requires a parameter server to mediate the training process among multiple end devices. However, none of these architectures scale gracefully to large-scale privacy and time-sensitive IoT applications. Therefore

Imagine an array with a massive (possibly uncountably infinite) number of antennas in a compact space. We refer to a system of this sort as Holographic MIMO. Given the impressive properties of Massive MIMO, one might expect a holographic array to realize extreme spatial resolution, incredible energy efficiency, and unprecedented spectral efficiency. At present, however, its fundamental limits have

This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks

Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS

Terahertz (THz) communications are envisioned as a key technology for 6G wireless systems, owing to an unprecedented promised multi-GHz bandwidth. While THz band suffers from huge propagation losses, large arrays of sub-millimeter wavelength antennas can be realized in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the received power and overcome the distance limitation.

Due to the severe pathloss experienced by electromagnetic waves in the millimeter wave (mmWave) band, a substantial challenge in their design is to have an adequate coverage area. With the objective of improving the coverage area and the sum rate attained, we conceive new full-duplex (FD) mmWave multiple-input multiple-output (MIMO) multiple-relay systems. Specifically, we propose a novel two-timescale

Terahertz (THz)-band (0.1 – 10 THz) communication is envisioned as a key wireless technology to satisfy the need for higher wireless data rates in denser networks. Several ongoing approaches are being considered to overcome the grand challenge of the THz band , i.e., the limited communication distance. Among others, the use of new 2D nanomaterials such as graphene to create novel plasmonic devices

We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems with a large number of antennas are power hungry when using conventional high-resolution analog-to-digital/digital-to-analog converters (A/Ds). To reduce the power consumption of mmWave MIMO systems, existing studies have considered hybrid structures with a reduced number of high-resolution or low-resolution A/Ds at

Efficient constellation design is important for improving performance in communication systems. The problem of multidimensional constellation design has been studied extensively in the literature in the context of multidimensional coded modulation and space-time coded MIMO systems. Such constellations are formally called as lattice codes, where a finite set of points from a certain high dimensional

Analog/digital hybrid beamforming is considered as a key enabling multiple antenna technology for implementing millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications since it can reduce the number of costly and power-hungry radio frequency (RF) chains while still providing for spatial multiplexing. In this paper, we introduce a novel hybrid beamforming architecture with dynamic

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user