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.

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

Advancements in intelligent transportation necessitate a dependable medium access control (MAC) protocol to enable high-priority safety broadcasts. Implementing cognitive radio-based vehicular ad-hoc networks (CR-VANETs) alleviates the significant growth in spectrum demand. The architecture must deploy multiple networks to make CR-VANET viable; however, the MAC design is a substantial challenge. The

A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder

In the mobile edge computing (MEC) networks, the heterogeneous demands can be met through device-to-device (D2D) technology due to the idle local resources from the neighbors. Although the overload of mobile edge computing servers can be relieved by sharing idle local resources among neighbors, the active impact of the cooperation in the close neighbors is ignored in the most existing works. In this

The 3D MIMO in 5G system requires adaptive and real-time adjustment of antenna azimuth angle, downtilt and beam combination according to the user distribution, which is a powerful technique to improve coverage and capacity. However, due to the complicated interactions between cells and complex environments, it is challengeable to jointly tune the antenna of multiple cells. Besides, the user distribution

In a cell-free network, a large number of mobile devices are served simultaneously by several base stations (BSs)/access points(APs) using the same time/frequency resources. However, this creates high signal processing demands (e.g., for beamforming) at the transmitters and receivers. In this work, we develop centralized and distributed deep reinforcement learning (DRL)-based methods to optimize beamforming

In this paper, we propose a novel Average autoencoder (AE)-based amplify-and-forward (AF) relay networks impacted by the I/Q imbalance (IQI) and additional hardware impairments (AHI), where the source and destination nodes are equipped with neural network (NN)-based encoder and decoder, while a conventional AF relay node assists the transmission. The average AE employs multiple small NN-based decoders

Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention

In order to efficiently compute the primary data and support the secondary quality-of-service (QoS) requirement, we propose a cooperative hybrid non-orthogonal multiple access (NOMA) scheme for mobile edge computing (MEC) assisted cognitive radio networks. In the proposed scheme, the primary computation task is securely offloaded to the secondary base station, and the hybrid NOMA technique is adopted

Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations:

Deep-learning (DL) has emerged as a powerful machine-learning technique for several problems encountered in generic wireless communications. Also, random Fourier Features (RFF) based DL has emerged as an attractive solution for several machine-learning problems; yet existing works lack rigorous analytical results to justify the viability of RFF based DL. To address this gap, we analytically quantify

In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge

With great potential to alleviate spectrum scarcity in the next generation wireless communication, cognitive satellite terrestrial networks (CSTNs) have attracted considerable attention recently. To realize efficient spectrum sharing between satellite and terrestrial networks, in this paper, we propose an energy efficient power control scheme for CSTNs, where a multibeam satellite network acts as the

Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of communication system is able to outperform the classical communication system. Unfortunately, this communication system cannot be trained by deep learning without known channel. To deal with this problem, a generative adversarial network (GAN) based training scheme has been recently proposed to imitate the real channel.

In this paper, we consider distributed heterogeneous multi-layer mobile edge computing (HetMEC) networks, where resource-poor edge devices (EDs) upload computing tasks for processing to the mobile edge computing (MEC) servers and a cloud center (CC). To reduce total energy consumption, computation offloading and resource allocation are independently performed by each device and each server. However

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.

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

The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple Radio Access Technologies (multi-RAT) and developing innovative network selection techniques to cope with such intensive demand while improving Quality of Service (QoS). Thus, we propose a distributed framework for dynamic

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii)

In this paper, one family of identification-(ID)-based and one family of non-ID-based asynchronous channel-hopping (CH) sequences are constructed for supporting cognitive-radio wireless networks (CRWNs) in a homogeneous channel setting. The novelty lies in the use of two-dimensional (2-D) algebraic algorithms to properly arrange the linear- and quadratic-congruence sequences in the prime field; channel

Densification and multi-band operation means inter-frequency handovers can become a bottleneck for mobile user experience in emerging cellular networks. The challenge is aggravated by the fact that there does not exist a method to optimize key inter-frequency handover parameters namely A5 time-to-trigger, A5-threshold1 and A5-threshold2. This paper presents a first study to analyze and optimize the

To meet the requirements of various emerging manufacturing applications, multiple Industrial Wireless Networks (IWNs) are employed to operate in the same region. However, the limited communication resources inevitably incur interference in the time and frequency domains, which is known as the coexistence problem. Existing centralized mechanisms suffer from a low computational efficiency in a large-scale

In this paper, we propose a cognitive capacity harvesting network (CCHN) based Internet of Things (IoT) architecture, which allows the lightweight IoT devices without spectrum monitoring/sensing capabilities to enjoy the benefits of cognitive radio networks (CRNs). We investigate the sum-rate maximization of IoT links in this proposed architecture. In particular, we formulate the considered problem

We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map

Wireless interference identification (WII) is critical for non-cooperative communication systems in both civilian and military scenarios. Recently, deep learning (DL) based WII methods have been proposed with impressive performances. However, these methods did not consider the quantization problem for DL based methods of WII, which is an indispensable process when deploying deep neural networks into

In wireless communication networks, the network provider serves certain licensed primary users who pay for a dedicated use of the frequency channels. However, not all the channels are occupied by the primary users at all times. For efficient spectrum utilization, in centralized cognitive radio networks (CRNs), a cognitive base station (CBS) dynamically identifies the spectrum holes and allocates the

Mobile edge computing (MEC) in cognitive radio networks is an optimistic technique for improving the computational capability and spectrum utilization efficiency. In this study, we developed an MEC system assisted by a cognitive unmanned aerial vehicle (CUAV), where a CUAV equipped with an MEC server can serve as a relay node and computing node. In such networks, a non-orthogonal multiple access scheme

Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption

Modern cellular infrastructure has grown in complexity and become an ecosystem that includes the host technologies, such as 4G Long Term Evolution (LTE) and 5G New Radio (NR), along with subsystems such as narrowband Internet of Things (NB-IoT), category M1 (Cat-M1/LTE-M), observed time difference of arrival (OTDOA) and support for 4G/5G coexistence using dynamic spectrum sharing (DSS). Such an increased

The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has been much work demonstrating the susceptibility of DL-based classification tasks to adversarial attacks, regression-based problems in the context of a wireless system have not been studied so

Large manufacturing sites with movable obstacles and dynamic network topology call for reliable and efficient strategies to transmit data through the industrial Internet of Things (IoT). Cooperative communications and relay selection have shown a great potential to improve throughput and energy efficiency at the expanse of high end-to-end transmission latency. To reduce this latency, we propose to

Spectrum scarcity and energy limitation are becoming two critical issues in designing Internet of Things (IoT). As two promising technologies, cognitive radio (CR) and radio frequency (RF) energy harvesting can be used together to improve both energy and spectral efficiency. In this paper, an optimal transmission problem in a cognitive IoT (CIoT) with RF energy harvesting capability is investigated

In this paper, we demonstrate that despite the power constraints on all transmit nodes caused by the interference temperature limit (ITL) imposed by the primary user, power domain cooperative non-orthogonal multiple access (NOMA) can ensure much higher throughput in underlay cognitive radio networks than conventional orthogonal multiple access (OMA), provided the network is adaptive. We use intelligent

Spectrum monitoring could improve spectral management and efficiency by enabling spectrum sharing, strengthening policy enforcement, and facilitating data-driven modeling of RF environments. This paper considers spectrum monitoring sensor networks in three dimensions to extend previous work on two-dimensional models. We derive a closed-form expression for the probability of emitter detection, which

Does Federated Learning (FL) work when both uplink and downlink communications have errors? How much communication noise can FL handle and what is its impact on the learning performance? This work is devoted to answering these practically important questions by explicitly incorporating both uplink and downlink noisy channels in the FL pipeline. We present several novel convergence analyses of FL over

Next-generation networks are endowed with enhanced capabilities thanks to software-defined networking and network function virtualization (NFV). There is a radical shift from device-centric to experience-driven environments of which data is the primary driver behind its running engines. In this paper, we consider joint topology design, traffic routing and NF placement for unicast NFV-enabled services

Radio spectrum is a limited and increasingly scarce resource, which motivates alternative usage methods such as dynamic spectrum allocation (DSA). However, DSA requires an accurate prediction of spectrum usage in both time and spatial domains with minimal sensing cost. In this paper, we propose NN-ResNet prediction model to address this challenge in two steps. First, in order to make the best use of

This paper considers a communication system whose source can learn from channel-related data, thereby making a suitable choice of system parameters for security improvement. The security of the communication system is optimized using deep neural networks (DNNs). More explicitly, the associated security vs reliability trade-off problem is characterized in terms of the symbol error probabilities and

Automatic modulation classification receives significant interest in the context of current and future wireless communication systems. Deep learning emerged as a powerful tool for modulation classification, as it allows for joint discriminative features learning and signal classification. However, the optimization of deep neural network architectures for modulation classification is a manual and time-consuming

In this paper, we propose a cognitive radio timing protocol (CRTP) to optimize the time allocation for different phases of cognitive radio (CR) operation to maximize the throughput of the secondary user (SU) while satisfying the interference constraint of the primary user (PU). To investigate it, first, we present a three-phase transmission approach for a multi-antenna SU, where, in the first phase

In 6G mobile systems, network slicing is an emerging technology to support services with distinct requirements by dividing a common infrastructure into multiple logical networks. However, as a network management method, it is difficult for network slicing to achieve a real-time resource allocation to satisfy the stringent requirement of services in 6G networks. This paper introduces a joint network

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