The medium access control (MAC) design in mobile networks with directional antennas is challenging due to the difficulty of defining the exact RF interference range between two neighboring directional links and the frequent changes of interference range due to node mobility. This research targets directional data reception (Rx) and transmission (Tx) coordination issues based on the computation of directional

Wireless federated edge learning (FEEL) has been recently proposed to support the mobile artificial intelligence (AI) applications. Instead of transmitting local data to the edge server, the local learning updates or model parameters are uploaded over the wireless channels to protect the data privacy and security. However, the unreliable wireless channel will cause random packet error, which eventually

The spectrum access system (SAS) designated by the FCC follows a centralized server-client model where a spectrum user registers with one SAS service provider for spectrum allocation and other spectrum management functions. This model, however, is vulnerable to adversarial influence on individual SAS servers, causing concerns over system reliability and trustworthiness, especially when the ecosystem

To accommodate Machine-type Communication (MTC) service, the wireless network needs to support low-delay and low-jitter data transmission, realizing delay deterministic wireless network. This paper analyzes the delay and jitter of the wireless network with and without spectrum sharing. When sharing the spectrum of the licensed network, the spectrum band of wireless network can be expanded, such that

For a virtual network operator (VNO) of a heterogeneous cognitive radio network (CRN), finding a channel allocation method that can maximize income while guaranteeing the quality of service (QoS) of secondary users (SU) is a complex but crucial problem. Aim to this problem, based on the probability distribution vector (PDV) designed to describe all possible channel allocation results, a queueing analysis

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without

Rational sharing of unlicensed spectrum and efficient use of device-to-device (D2D) communication are two effective ways to improve energy efficiency (EE) and spectrum efficiency (SE) in 5G new radio system. In this paper, we extend the D2D technology to unlicensed spectrum and study the EE of user terminals in unlicensed D2D (D2D-U) system. To cope with the inherent distributed structure of the D2D-U

This paper studies data collection in a wireless powered Internet of Things (IoT) network with a hybrid access point (HAP). A fundamental problem at the HAP is to determine the number of time slots over a given planning horizon that is used to charge and collect data from devices. To this end, we outline a mixed integer linear program (MILP) to determine (i) the mode (charge or data) of each slot,

A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its generalization capability is analyzed in different settings and the effect of sample length and anti-aliasing decimation is demonstrated. The embedding module serves as

In this work, performance of a simultaneous wireless information and power transfer (SWIPT) enabled cooperative cognitive radio sensor network (CRSN) is explored over generalized Nakagami- ${m}$ faded channels. A time switching (TS) protocol for SWIPT is used and the impact of TS factor on energy harvesting and information transmission phases, and information processing and broadcasting phases are

Today, a microwave-based solution is the one commonly used for wireless backhaul networks as it has high capacity and can be easily deployed. For such a microwave-based wireless backhaul network, a well-designed topology is important for efficient capacity utilization and high-quality mobile services. In an earlier work, we presented an approach for topology planning for microwave-based wireless backhaul

Recently, an origin fully-decoupled radio access network (FD-RAN) inspired by neurotransmission has been proposed for B5G/6G mobile communication networks, which achieves the potentials of improving the spectrum efficiency, reducing the energy consumption and meeting personalized user requirement through profound resource cooperation. To explore new radio and networking technologies, in this paper

The application of deep learning improves the processing speed and the accuracy of automatic modulation recognition (AMR). As a result, it realizes intelligent spectrum management and electronic reconnaissance. However, deep learning-aided AMR usually requires a large number of labeled samples to obtain a reliable neural network model. In practical applications, due to economic costs and privacy constraints

The satellite-ground integrated network (SGIN), consisting of satellites and ground base stations, is regarded as the trend to solve the problem of global coverage. However, highly dynamic scenarios pose great challenges to users’ mobility management, especially handover decisions. With the rapid development of machine learning, reinforcement learning (RL), such as Q-learning and deep Q-network (DQN)

In 5G and B5G networks, real-time and secure resource allocation with the common telecom infrastructure is challenging. This problem may be more severe when mobile users are growing and connectivity is interrupted by natural disasters or other emergencies. To address the resource allocation problem, the network slicing technique has been applied to assign virtualized resources to multiple network slices

To address the drastic increase in multimedia traffic volume, mobile edge caching (MEC) has been exploited to reduce redundant data transmissions by equipping computation and storage capacity at the edge network. Previous works on learning-based caching problems often only concern pre-storing popular contents to satisfy users’ demands. In this work, we investigate the cache strategy design problem

In this paper, we investigate the problem of channel estimation in a multi-user massive multiple-input multiple-output (MIMO) secondary network (SN) aiming to access the licensed spectrum of a multi-user massive MIMO primary network (PN) using the underlay spectrum sharing approach. We estimate the channels of the single-antenna primary users (PUs) and those of the secondary users (SUs) at the secondary

This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep learning techniques adopt a frequentist framework, and are known to provide poorly calibrated decisions that do not reproduce the true uncertainty caused by limitations in the size of the training data. Bayesian learning

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model’s input, during inference (i.e., the adversarial perturbation

Recently, non-orthogonal multiple access (NOMA)-enabled cognitive satellite-unmanned aerial vehicle (UAV)-terrestrial networks have attracted extensive attention for the advantages of enhancing spectrum efficiency and coping with the exponential growth of access users. In this paper, we propose a joint subchannel assignment and power allocation algorithm for NOMA-enabled cognitive satellite-UAV-terrestrial

Spectrum prediction is challenging due to its multi-dimension, complex inherent dependency, and heterogeneity among the spectrum data. In this paper, we first propose a stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM) based spectrum prediction method (SAEL-SP). Specifically, a SAE is designed to extract the hidden features (semantic coding) of spectrum data in an unsupervised

We inspect an opportunistic Cognitive Radio Network formed between a Primary Network Provider (PNP) and secondary IoT operators. In our system, spectrum-sensing duty is offloaded to a Sensing Agent (SA), which allows the IoT operators to improve data rate and reduce energy consumption otherwise needed for spectrum-sensing. IoT operators pay PNP for accessing licensed spectrum and SA for providing sensing

For Internet of Things (IoT) networks, it is important to develop energy-efficient communication schemes to extend the operating life of battery-powered IoT devices. Additionally, non-orthogonal multiple access (NOMA) can utilize frequency resources more efficiently than orthogonal multiple access, making it more suitable to support massive connectivity of IoT users. Motivated by these facts, we consider

With the evolutionary development of modern communications technology, automatic modulation classification (AMC) has played an increasing role in the complex wireless communication environment. Existing AMC schemes based on deep learning use a neural network to extract features and calculate feature maps, then feed them into fully connected layers for classification. However, existing schemes still

We consider an underlay spectrum sharing scheme, where a multi-user massive MIMO network (referred to as the primary network (PN)) allows another multi-user massive MIMO network (called the secondary network (SN)) to use the spectrum, owned by the PN, to communicate with the SN’s users. For such a scheme, we devise joint power allocation and beamforming schemes at the SN for both conventional time-division

The severe spectrum scarcity and the stringent requirements of Beyond 5G applications call for an integrated use of low frequency Sub-6 GHz and high frequency millimeter Wave bands. Focusing on future Internet of Things (IoT) Short-Packet Communications (SPC), this paper investigates the optimized usage of such diverse wireless interfaces. We propose an unifying framework devoted to SPC that jointly

Recently, machine learning (ML) has shown its effectiveness in improving communication efficiency by reinstating the semantics of bits. To understand its underlying principles, we propose a novel stochastic model of semantic communication, dubbed semantics-native communication (SNC). Inspired from human communication, we consider a point-to-point SNC scenario where a speaker has an intention of referring

Due to the high mobility, low cost and high robustness of line-of-sight (LoS) channels, unmanned aerial vehicles (UAVs) have begun to play an important role in assisting wireless communications. However, the broadcasting nature of wireless communication networks makes the electromagnetic spectrum vulnerable to jamming attacks. To ensure communication security, this paper investigates the jamming detection

This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive

In this paper, we investigate the effectiveness of an overlay cognitive radio (OCR) coupled with non-orthogonal multiple access (NOMA) system using a full-duplex (FD) cooperative spectrum access with a maximal ratio combining (MRC) scheme under the various non-ideal system imperfections. In view of practical realization, we ponder the impact of loop self-interference, transceiver hardware impairments

As the new Editor-in-Chief (EiC), it is my great honor and pleasure to present you the first 2023 issue of IEEE Transactions on Cognitive Communications and Networking (TCCN), which includes 18 interesting papers reporting the latest advances in spectrum sharing and artificial intelligence (AI) empowered communications and networking. My work just started in January, following Dr. Ying-Chang Liang

Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledge-based method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional

The growing need for electromagnetic spectrum to support the next generation (xG) communication networks increasingly generate unwanted radio frequency interference (RFI) in protected bands for radio astronomy. RFI is commonly mitigated at the Radio Telescope without any active collaboration with the interfering sources. In this work, we provide a method of signal characterization and its use in subsequent

Importance of user mobility has rapidly increased in 5G due to reduced cell sizes, management of Multi-access Edge Computing (MEC), and ultra-low latency services. Reactive nature of existing management systems is a bottleneck, and it can be solved by building proactive systems that exploit temporal characteristics of time-series mobility data to predict long-term user movement (i.e., path). However

Anomaly detection is an essential part of spectrum monitoring applications. Malicious users and malfunctioning nodes could be identified via anomaly detection methods. Meanwhile, the spectrum bands that would be utilized in future 6G or satellite communication system settings are going to be wider than ever. Acquiring Nyquist sampled data from such a spectrum would require components with a very high

Massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) has been considered as one of the promising components in the optical wireless communication system. However, the envisioned benefits may be limited due to the high computational complexity to estimate accurate channel state information (CSI). Besides, several propagation paths in the practical communication channels

The presence of correlated and imperfect channel state information (CSI) affects the secrecy performance of cognitive radio networks (CRN). In this paper, we propose a physical layer security analysis of underlay CRN in terms of secrecy outage probability (SOP), average secrecy rate (ASR), and amount of secrecy loss (ASL). Closed forms of SOP, ASR, and ASL are provided over imperfect CSI of Rayleigh

It is crucial to consider time-critical communications in cognitive-radio-based Internet of Things (CR-IoT), which increasingly rely on the exchange of short-packet information to efficiently monitor and control. Therefore, the age of information (AoI) is exploited as a timeliness metric in this paper to investigate the freshness of received information at the remote server with dual-hop short-packet

In dynamic spectrum access (DSA), unlicensed secondary users (SUs) estimate the idle probabilities of primary channels by using historical sensing results and opportunistically access the channel with the highest idle probability for transmission. Due to the rapid traffic changes and irregular user mobility, primary channels can be highly dynamic and their idle probabilities are generally time-varying

Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the “semantics” of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response

The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper considers the scene of completing video-based AI inference tasks in the MEC system. We formulate a mixed-integer nonlinear programming problem (MINLP) to

To overcome the spectrum scarcity issues, the citizens broadband radio service (CBRS) presents a centralized spectrum management solution. The efficiency of spectrum utilization could be further improved by introducing spectrum trading. Blockchain-based spectrum trading has been considered as a decentralized, flexible, and secure approach. However, current studies rarely investigate the interference

According to the regulations of the hybrid access strategy in cognitive radio network, the secondary user (SU) needs to identify the primary user’s (PU) specific transmit power level to avoid unacceptable interference with the PU. However, the conventional transmit power recognition methods cannot accurately identify the transmit power in conditions with low signal-to-noise ratio, fading channels and

This paper reformulates a worst-case sum-rate maximization problem for optimizing robust multi-user multiple-input multiple-output (MU-MIMO) downlink precoding under realistic per-antenna power constraints. We map the fixed number of iterations in the developed mean-square-error uplink-downlink duality iterative algorithm into a layer-wise trainable network to solve it. In contrast to black-box approximation

In this work, we address the problem of a joint robust transmission, reflection and reception strategy design at an active reconfigurable intelligent surface (RIS)-assisted underlay multiple input multiple output (MIMO) cognitive radio networks in which a secondary transmitter serves multiple secondary receivers simultaneously. As such, we study the impact of imperfection in channel state information

Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), which is a fundamental problem in Internet Service Provider (ISP) networks. Nowadays, state-of-the-art TE optimizers rely on traditional optimization techniques, such as Local search,

The performance of an underlay coordinated direct and relay transmission (CDRT) based non-orthogonal multiple access (NOMA) network is limited by the random nature of the transmit powers due to the interference temperature limit (ITL) imposed by the primary network. In this paper, we show that combining the signals at both near and far users can significantly enhance throughput. Considering the practical

This paper investigates Dynamic Spectrum Access (DSA) paradigm with imperfect feedback for multiuser wireless network. Each user selects an orthogonal channel in particular time slot to transmit packet with a certain transmission probability. In next time slot, the user who has transmitted a packet receives an ACK signal based on local observation. Bearing in mind the dynamic nature of wireless networks

The classification of error correcting code (ECC) types is an important task for intelligent wireless communication. Previous solutions to this problem usually ignored the time sequences feature and considered the incoming data as separate code words. In this paper, a novel framework for code type classification is proposed based on the Hidden Markov Model (HMM). The structural differences of different

The combination of Cognitive Radio with Nonorthogonal Multiple Access (CR-NOMA) has enormous potential interest to improve spectral efficiency and increase system capacity in future wireless communication networks. Hence, according to the broadcast nature of the CR-NOMA wireless channel, handling the confidentiality of information is one of the most important requirements of the network. In this paper

Fog-cloud computing is one of cores techniques in wireless networks, where fog and cloud nodes provide high-speed and large-scale computing services to mobile users through a collaborative method. For the conventional fog-cloud computing schemes, the computing abilities of nodes and the computation task offloading methods are the main factors affecting the latency and energy consumption. However, when

Multiplexing ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) are critical in the next generation mobile network. URLLC requires ultra-high reliability and extremely low latency, whereas eMBB demands high data rates. Thus, the coexistence system of URLLC and eMBB faces the challenge of sharing the spectrum efficiently and effectively. In this study, we comprehensively

Inter-datacenter networks take the role of connecting edge clusters and datacenters distributed globally. Nowadays, inter-datacenter network topologies are becoming more and more complicated with multiple links on each path, multiple paths between two datacenters, and overlaps between different paths. Hence, a link-grained scheduling method is necessary for making routing decisions and bandwidth allocation

Gradient-based iterative algorithms have been widely used to solve optimization problems, including resource sharing and network management. When system parameters change, it requires a new solution independent of the previous parameter settings from the iterative methods. Therefore, we propose a learning approach that can quickly produce optimal solutions over a range of system parameters for constrained

In recent years, deep learning has been widely applied in communications and achieved remarkable performance improvement. Most of the existing works are based on data-driven deep learning, which requires a significant amount of training data for the communication model to adapt to new environments and results in huge computing resources for collecting data and retraining the model. In this paper, we

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.

Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottleneck in cellular network and satisfy people’s requirements about latency, the network architecture, like the information-centric network (ICN) intends to proactively keep limited popular content at the edge of network based