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.

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.

We are delighted to introduce the readers to this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at reporting the latest most promising research advances in modeling, analysis, fabrication design, algorithms, and communication protocols of future wireless networks with reconfigurable intelligent surfaces (RISs), and at identifying new research

In this article, a novel multiple-input multiple-out (MIMO) transmission scheme, called generalized polarized enhanced spatial modulation (GPESM), is proposed for dual-polarized land mobile satellite (LMS) communications. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations

This paper deals with channel estimation in reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) time-division duplexing systems. In a typical RIS assisted communication, an RIS is deployed in the close proximity of communication devices, thus resulting in ill-conditioned low-rank channel matrices. To effectively estimate these channels, we propose a two-stage channel

Reconfigurable Intelligent Surfaces (RISs) are planner surfaces that include a large number of passively radiating elements. By changing the phase shifts of these passive elements, RISs can shape the propagation environment. This requires accurate channel state information. However, the Channel Estimation (CE) process is challenging because transmitting and/or signal processing means being unworkable

Smart and reconfigurable wireless communication environments can be established by exploiting well-designed intelligent reflecting surfaces (IRSs) to shape the communication channels. In this paper, we investigate how multiple IRSs affect the performance of multi-user full-duplex communication systems under hardware impairment at each node, wherein the base station (BS) and the uplink users are subject

In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing practical discrete intelligent reflecting surface (IRS) is proposed. The proposed channel model supports the scenario where both transceivers and environments move. The evolution of clusters in the space domain and the practical discrete phase

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

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for achieving high spectrum and energy efficiency in future wireless communication networks. In this paper, we investigate an RIS-aided single-cell multi-user mobile edge computing (MEC) system where an RIS is deployed to support the communication between a base station (BS) equipped with MEC servers and multiple single-antenna

Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide interest due to their capability of tuning wireless propagation environments in order to increase the system performance of wireless networks. In this paper, a multiuser wireless network assisted by a RIS is studied and resource allocation algorithms are presented for several scenarios. First of all, the problem of channel estimation

Due to the emergence of deep learning, signal recognition has made great strides in performance improvement. The success of most deep learning methods relies on the accessibility of abundant labeled training data. However, the annotation of signals is quite expensive, making it challenging to train deep learning models substantially. This calls for the development of semi-supervised learning (SSL)

Constructing an infrastructure for spectrum sensing and spectrum sharing on the cloud is a promising technology. In this paper, we propose an analysis framework based on spectrum data gathered by distributed sensors to construct the radio environment map (REM). To the best of our knowledge, this is the first attempt to leverage the power of Bayesian analysis and Markov chain Monte Carlo (MCMC) in REM

Internet of Things (IoT) is considered as the enabling platform for a variety of promising applications, such as smart transportation and smart city, where massive devices are interconnected for data collection and processing. These IoT applications pose a high demand on storage and computing capacity, while the IoT devices are usually resource constrained. As a potential solution, mobile edge computing

In this paper, we consider an Internet-of-Things (IoT) network that supports two types of devices, namely static devices (SDs) and dynamic devices (DDs), with multiple channels. The notion of power-domain non-orthogonal multiple access (NOMA) is applied so that DDs can not only dynamically change channels, but also transmit signals with a high power to reduce collisions with SDs. Thus, learning algorithms

The increasing complexity and dynamics of 5G mobile networks have brought revolutionary changes in its modeling and control, where efficient routing and resource allocation strategies become beneficial. Software-Defined Network (SDN) makes it possible to achieve the automatic management of network resources. Relying on the powerful decision-making capability of SDNs, network association can be flexibly

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.

We are delighted to introduce this special section of the IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (TCCN), which aims at exploring recent advances and addressing practical challenges in the applications of machine learning (ML) and artificial intelligence (AI) for physical layer design and optimization. We have received a total number of 36 submissions, and after a rigorous review

In the 5th generation (5G) networks, coordinated multiple point (CoMP) is one of key technologies to improve the quality of service (QoS) of edge users. To meet the requirement of growing data rates, millimeter-wave (mmWave) can be employed in the CoMP system. However, the QoS of users may be degraded if line-of-sight (LoS) mmWave channels are not guaranteed. In this article, an unmanned aerial vehicle

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

While millimeter-wave (mmWave) communications can enjoy abundant bandwidth resource, their high susceptibility to blockage poses serious challenges to low-latency services. In this paper, a novel intelligent reflecting surface (IRS)-assisted mmWave scheme is proposed to overcome the impact of blockage. The scheme minimizes the user power of a multi-user mmWave system by jointly optimizing the transmit

This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases

Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price reduction and the increased capabilities of drones. The drones in the swarm need to cooperatively explore an unknown area, in order to identify and monitor interesting targets, while minimizing their movements. In this work,

This paper investigates a hybrid beamforming design for dual-function radar-communication (DFRC) systems. Comparing with fully digital beamforming, much less radio frequency (RF) chains are needed and hence, both hardware cost and energy consumption can be reduced. The hybrid transmit/receive beamformers are designed by maximizing the sum-rate under constraints of power as well as similarity between

In this paper, we explore radio resource management for a time-varying 5G heterogeneous wireless access network that includes multi-RATs such as 5G new radio (NR) and long-term evolution (LTE). To cope with the practical challenges of a centralized approach such as signalling overhead and computational complexity, we decomposed the process of radio resource management into three parts, 1) RAT selection

The recent development in machine learning, especially in deep neural networks (DNN), has enabled learning-based end-to-end communication systems, where DNNs are employed to substitute all modules at the transmitter and receiver. In this article, two end-to-end frameworks for frequency-selective channels and multi-input and multi-output (MIMO) channels are developed, where the wireless channel effects

In this article, we investigate the performance of a reconfigurable intelligent surface (RIS)-assisted dual-hop mixed radio-frequency underwater wireless optical communication (RF-UWOC) system. An RIS is an emerging and low-cost technology that aims to enhance the strength of the received signal, thus improving the system performance. In the considered system setup, a ground source does not have a

Reconfigurable intelligent surface (RIS) becomes an increasingly important technology in sixth-generation (6G). The key aspect of RIS is smartly configuring the wireless propagation environment and providing supplementary links to enhance the signal transmission between the BS and the UE. In recent years, there has been a surge of interest in RISs in joint active and passive beamforming design to improve

Cell-free systems can effectively eliminate the inter-cell interference by enabling multiple base stations (BSs) to cooperatively serve users without cell boundaries at the expense of high costs of hardware and power sources due to the large-scale deployment of BSs. To tackle this issue, the low-cost reconfigurable intelligent surface (RIS) can serve as a promising technique to improve the energy efficiency

We consider an opportunistic cognitive radio (CR) system in which secondary transmitter (SU tx ) is equipped with a reconfigurable antenna (RA). Utilizing the beam steering capability of the RA, we regard a design framework for integrated sector-based spectrum sensing and data communication. In this framework, SU tx senses the spectrum and detects the beam corresponding to active primary user’s (PU)

The ever-growing popularity and rapid development of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since has rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and Internet of things (IoT) devices. In this

Wireless Ad Hoc Networks are self-organizing networks deployed without any centralized infrastructure. Flooding based Denial-of-Service (DoS) attacks were targeting the constrained resources of mobile nodes as well as the network. Route Request (RREQ) flooding attack is one of the prominent DoS attacks launched from the network layer in which the attacker sends a huge number of spoofed Route Request

In this article, we consider an overlay satellite-terrestrial network (OSTN) where an opportunistically selected terrestrial Internet-of-Things (IoT) network assists the primary satellite communications as well as accesses the spectrum for its own communications under hybrid interference received from extra-terrestrial sources (ETSs) and terrestrial sources (TSs). Herein, the IoT network adopts power-domain

In this study, we introduce a multi-unmanned aerial vehicle (multi-UAV) enabled mobile edge computing (MEC) system, with UAVs as the computing server for the task offloading of ground users. The energy consumption for ground users is minimized by jointly optimizing the UAV task scheduling, bit allocation, and UAV trajectory in a unified framework. To accomplish such goal, we propose a two-layer optimization

Automatic fault management is one of important components in self-organizing networks to mitigate and recover from failures of problematic cells. As a special case of cell outage, sleeping cell, which provides degraded service for subscribers without triggering alarms, brings challenges to fault detection. Machine learning provides effective tools for such a detection task. However, traditional algorithms

The future generation of wireless communication networks demands for high spectral efficiency to accommodate a large number of devices over the limited available frequency spectrum. Device to device (D2D) systems exploit channel reuse to offer high spectral efficiency and reduce the burden on the communication infrastructure by facilitating communication between devices without involving the base station

In a dynamic spectrum allocation (DSA) system, reliable prediction of spectrum occupancy based on a spectrum consumption model (SCM) is critical for system design, performance analysis, and evaluation. In this article, we focus on a low-level abstracted measured dataset from a massive campaign and investigate the occupancy of representative frequency bands. First, we apply an autoregressive-moving-average

This article investigates an unmanned aerial vehicle (UAV)-enabled wireless communication network with multiple pairs of source-destination ground users (GUs), where the rotary-wing UAV acts as an aerial mobile relay to transfer information from the source GUs to corresponding destination GUs when the direct terrestrial communication is blocked. On the premise of satisfying the communication requirements

Private fifth-generation mobile communication networks (5G) have garnered considerable attention for providing various communication services. In private 5G systems, such as those employed in smart factories, it is necessary to appropriately manage the interference power among diverse systems because each nonpublic operator deploys a gNodeB in the local area and utilizes a similar allocated frequency

The opportunistic sharing of frequency bands supported in the Dynamic Spectrum Access (DSA) paradigm resolves the spectrum scarcity issue in wireless communications. To this end, deep learning models such as Long Short-Term Memory (LSTM) are becoming a popular choice for predicting the spectrum for cognitive radio type applications. However, the computational complexity to train such models can be

Existing Long Term Evolution (LTE) networks have untapped radio frequency resources, also known as white space, that can be repurposed for cognitive radio applications. A secondary network can opportunistically interweave communication within the white space of an existing LTE signal by leveraging cognitive radio. In this article, we present CIAO-LTE, a novel framework for forming a cognitive interwoven

Routing is one of the main drivers of the end-to-end performance of bundle transmissions over a disruption tolerant network given the potentially large impact of the temporary but long-term partitioning that can occur at different sections of the network. A neuromorphic networking approach that defines an adaptive bundle routing for disruption-tolerant networks (DTN) is proposed where spiking neuronal

To meet the high demand for mobile data, the Third Generation Partnership Project (3GPP) established a set of standards known as 5G New Radio (5G NR). The architecture of 5G NR includes a flexible radio access network and a core network. 3GPP has also been working on a new radio access technology, called 5G NR Unlicensed (5G NR-U), which aims at extending 5G NR to unlicensed bands. In this article

Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to

In recent years, deep neural networks have achieved exciting results in a variety of tasks, and many fields try to introduce neural network techniques. In mobile edge computing, there are not many attempts that build neural network models in service recommendation or QoS (quality-of-service) prediction. The method proposed in this article is an attempt to employ neural network technique for QoS prediction

Decentralized edge computing techniques have been attracted strongly attentions in many applications of intelligent Internet of Things (IIoT). Among these applications, intelligent edge surveillance (INES) methods play a very important role to recognize object feature information automatically from surveillance video by virtue of edge computing together with image processing and computer vision. Traditional

Some Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative

Internet of Things (IoT) networks are increasingly used for edge data analytics, i.e., collecting and analyzing data at the network edge. However, the IoT devices are typically resource-constrained and cannot support fast and accurate execution of such tasks, while the involvement of distant cloud servers is often impractical and entails huge communication overheads. To address this problem, we develop

Edge computing extends the service resources of cloud computing to the edge of the network, providing less latency and higher bandwidth utilization. The relay edge computing wireless network (RECWN) with intelligent edge device can effectively solve the problem of wireless signal transmission distance and attenuation. However, the relay node’s forwarding brings greater risks to the leakage of private

In the Internet of Things (IoT), numerous low complexity and energy constrained devices are employed to collect and transmit data simultaneously, where the unmanned aerial vehicle (UAV) is an efficient means to relay the signals. Considering the limited power and computational capability of UAV, mobile edge computing (MEC) is carried out to enhance the usage of UAV-aided IoT networks. In order to develop

Due to the extensive use of unmanned aerial vehicles (UAVs) in civil and military environment, effective deployment and scheduling of a swarm of UAVs are rising to be a challenging issue in edge computing. This is especially apparent in the area of Internet of Things (IoT) where massive UAVs are connected for communications. One of the characteristics of IoT is that an operator can interact with more

In recent times, Unmanned-aerial-vehicles (UAVs) have grasped significant attentions for performing various operations without a constant intervention of the human users. Due to the power and computing constraints, however, it is difficult for an UAV to perform all the tasks independently. Hence, to achieve its goals, a UAV may share various sensitive data with the nearest edge servers through some

Mobile computing provides storage and computation resources of proximal devices to satisfy the real-time and low-energy communication demands of the Internet of Things (IoT). However, in the areas without terrestrial base station infrastructures, the IoT sensors have trouble implementing reliable and stable connections, which results in the difficulties of data gathering and data caching. In this paper

The heterogenous wireless services and exponentially growing traffic call for novel spectrum- and energy-efficient wireless communication technologies. Recently, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits and address the drawbacks of cognitive radio (CR) and ambient backscattering communications (AmBC), leading to mutualism spectrum sharing and highly reliable

In this paper, we investigate the impact of using improper Gaussian signaling (IGS) scheme on the interweave cognitive radio (CR) paradigm when the cognitive user has access to full or partial channel-state-information (CSI). Throughout this work, we analyze the performance of both the primary user (PU) and secondary user (SU) in terms of instantaneous achievable rate and outage probability, for given

Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference. Unfortunately, traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms in terms of latency