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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

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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

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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

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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

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

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

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

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

Due to the ever-expanding applications of the Internet-of-Things (IoT), designing energy- and spectrally-efficient transmission schemes to support massive connections and devices is inevitable and still challenging. Thus, energy-harvesting (EH) and cognitive-radio (CR) systems are becoming more inseparable for future IoT networks. This paper analyzes the performance of EH-CR-IoT networks, where closed-form

Due to the limited bandwidth of Roadside Units (RSUs) deployed in drive-thru networks, vehicles entering the network coverage with data requests have to contend for the access to the data service provided by RSUs. In order to maximize the vehicle utility, efficient access schemes are indispensable at the vehicles’ side. This paper studies the optimal access control of vehicles in multi-agent drive-thru

The ever-increasing data explosion, particular mobile video traffic, increases the backhaul load and makes it difficult for the centralized cloud to meet the requirements of various services. Accordingly, proactive caching at mobile devices gains more attentions, which is envisioned as a promising technology to relieve the backhaul traffic and cater to diverse quality of service in multi-access edge

Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems

This work addresses dynamic non-cooperative coexistence between a cognitive pulsed radar and nearby communications systems by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a policy for optimal radar performance. The radar learns to vary the bandwidth and center frequency of its linear frequency modulated (LFM) waveforms to mitigate interference

We present a novel method for centralized collaborative spectrum sensing for IoT network leveraging cognitive radio network. Based on an online learning framework, we propose an algorithm to efficiently combine the individual sensing results based on the past performance of each detector. Additionally, we show how to utilize the learned normalized weights as a proxy metric of detection accuracy and

Dynamic spectrum access based wireless networks and next-generation cognitive electronic warfare systems demand rapid identification and labelling of high data rate radio frequency (RF) information. This requires receiver front-end designs to distinguish numerous kinds of wireless signals of different standards over a relatively wide spectrum. This paper proposes a novel attempt at large scale, blind

Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is

In this article, kernelized-likelihood ratio tests (LRTs) for binary phase-shift keying (BPSK) signal detection based on the polynomial kernel function are proposed. Specifically, we kernelize the conventional LRT of BPSK signal detection using the so-called kernel trick, such that the inner product of the conventional LRT is replaced with proper polynomial kernel functions allowing for richer feature

5G network technology is growing fast, thus the number of devices and the traffic are likely to pose impressive challenges. A new paradigm called “Internet-of-People” (IoP) represents a valid approach to include the social aspect. Following an IoP perspective, we believe that the knowledge of social multiplex interactions and dynamics could drive more sustainable growth. By merging this with the Device-to-Device

As a typical scenario in future generation communication network applications, UAV-assisted communication can perform autonomous data delivery for massive machine type communication (mMTC), where the data generated from Internet of Things (IoT) devices can be carried and delivered to the corresponding locations with no direct communication channels to the IoT devices. Wireless energy transfer technique

In order to meet the dramatic wireless bandwidth demands of emerging multimedia applications, cognitive radio has been proposed as one of promising solutions to improve the spectrum efficiency. This article aims at pursuing high spectrum efficiency via accessing the idle spectrum intelligently without information exchange among users. Different from infrastructure-based wireless networks, users in

Regulatory bodies recognize the possibility of an unlicensed and dynamic access to the TV-white spaces. As a consequence, it is expected that heterogeneous and independently operated networks will be interested in sharing the same bandwidth within the same service area. Along this direction, this article addresses the coexistence among multiple heterogeneous secondary networks (SNs) operating in the

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.

Automatic modulation classification (AMC) is becoming increasingly important for its fundamental role in dynamic spectrum access, which can support 5G wireless communications to refarm the spectrum resource with low utilization. In order to achieve a better classification performance, several AMC methods based on prototype and variant of convolutional neural networks (CNNs) have been proposed. However

In this article, a learning-based proactive resource sharing scheme is proposed for the next-generation core communication networks, where the available forwarding resources at a switch are proactively allocated to the traffic flows in order to maximize the efficiency of resource utilization with delay satisfaction. The resource sharing scheme consists of two joint modules, estimation of resource demands

In recent years, unmanned aerial vehicles (UAVs) have attained significant interest in different applications including aerial surveillance, providing wireless coverage, precision agriculture, power lines & oil rigs monitoring and construction, etc. The UAVs implicit peculiarities, e.g., swift mobility, increase in payload capabilities and airborne time, place it as a potential candidate for many applications

The formation flights of multiple unmanned aerial vehicles (UAV) can improve the success probability of single-machine. Dynamic spectrum interaction solves the problem of the ordered communication of multiple UAVs with limited bandwidth via spectrum interaction between UAVs. By introducing reinforcement learning algorithm, UAVs can continuously obtain the optimal strategy by continuously interacting

In view of the scarcity of spectrum resources in wireless communication, it is studied in this paper over the two-hop cognitive secrecy transmission scheme of decoding and forwarding (DF) unmanned aerial vehicles (UAVs) assisted relay with energy harvesting under Nakagami- ${m}$ channel. It is worth noting that the terminal node is equipped with multiple antennas and the optimal antenna selection can

Edge computing has emerged as a promising solution for relieving the tension between resource-limited machine type devices (MTDs) and computational-intensive tasks. To realize successful task offloading with limited spectrum, we focus on the cognitive machine-to-machine (CM2M) paradigm which enables a massive number of MTDs to either opportunistically use the licensed spectrum that is temporarily available

With the rapid increase in communication technologies, shortage of spectrum will be a major issue faced in the coming years. Cognitive radio is a promising solution to this problem and works on the principle of sharing between cellular subscribers and ad-hoc Device to Device (D2D) users. Existing 5G spectrum sharing techniques work as per a fixed rule and are pre-established. Also, recent game theoretic

Due to the recent developments in the wireless mesh and ad-hoc networks, multi-hop cognitive radio networks (MCRNs) have attained the significant attention towards providing the reliable multimedia communications. However, in reliable multimedia communications each multimedia application observed a very stringent quality-of-service (QoS) requirements. Moreover, in MCRNs, channel allocated to the multimedia

In this paper, two novel spectrum resource allocation schemes, based on orthogonal frequency division multiplexing with index modulation (OFDM-IM) and dual-mode OFDM-IM, are proposed for a three-node cooperative non-orthogonal multiple access (C-NOMA) system. In the first scheme, we allocate IM bits to serve the cell-edge user, and save the transmit power to assist the delivery of constellation symbols