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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

Deep learning has recently been successfully applied in automatic modulation classification by extracting and classifying radio features in an end-to-end way. However, deep learning-based radio modulation classifiers are lacking interpretability, and there is little explanation or visibility into what kinds of radio features are extracted and chosen for classification. In this article, we visualize

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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Due to imperfections in transmitters’ hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, existing work has mainly focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this article

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV’ UE-load and the overall energy consumption of UEs. The above optimization problem includes

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

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

The rapid development of communication systems poses unprecedented challenges, e.g., handling exploding wireless signals in a real-time and fine-grained manner. Recent advances in data-driven machine learning algorithms, especially deep learning (DL), show great potential to address the challenges. However, waveforms in the physical layer may not be suitable for the prevalent classical DL models, such

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

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In this paper, we investigate the energy efficiency of conventional collaborative compressive sensing (CCCS) scheme, focusing on balancing the tradeoff between energy efficiency and detection accuracy in cognitive radio environment. In particular, we derive the achievable throughput, energy consumption and energy efficiency of the CCCS scheme, and then formulate an optimization problem to determine

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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

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

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

Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to mmWave signals may degrade the system performance. Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the channel gain and mitigate interference

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

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

A canonical wireless communication system consists of a transmitter and a receiver. The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal

Recently, channel sparsity has been considered as a nature of wireless channels in many researches of intelligent communications, and an increasing number of investigations are conducted by exploiting sparsity of wireless channels, such as deep-learning-based channel estimation and compressive sensing. Growing evidence from channel measurements show that the sparse or approximate sparse distribution

Improving the accuracy of channel estimation is a significant topic in the context of wireless communications. For training-based channel estimations, increasing the length of a training sequence may improve the accuracy of channel estimation but causes a higher overhead. Nevertheless, this paper shows that benefiting from generative adversarial networks (GANs), which is an emerging deep learning framework

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

In the edge computing paradigm, mobile devices offload the computational tasks to an edge server by routing the required data over the wireless network. The full potential of edge computing becomes realized only if a smart device selects the most appropriate server in terms of the latency and energy consumption, among many available ones. The server selection problem is challenging due to the randomness

The spoofing attack is critical to bypass physical-layer signal authentication. This paper presents a deep learning-based spoofing attack to generate synthetic wireless signals that cannot be statistically distinguished from intended transmissions. The adversary is modeled as a pair of a transmitter and a receiver that build the generator and discriminator of the generative adversarial network, respectively

Ultra Reliable and Low Latency Communications (URLLC) play a key role in 5G vertical markets, but pose many technical challenges especially when sharing the spectrum with Enhanced Mobile Broadband (eMBB) customers. This study aims to overcome the spectrum inefficiency issue of fully separate (FS) approach and the contention issue of the fully overlap (FO) approach. We present a user-initiated probability

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

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Learning from massive network data to produce cognitive knowledge for efficient resource management in 5G and beyond 5G (B5G) is still challenging. We are delighted to introduce the readers to 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 intelligent resource management

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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

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

The General Authorized Access (GAA) users in the Citizens Broadband Radio Service (CBRS) band are the lowest priority users. They must make sure that they do not cause harmful interference to the higher tier users while cooperating with each other to minimize potential interference among themselves. Thus, efficient GAA coexistence scheme is essential for the operation of GAA users and to obtain high

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

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

The rapid increase of air traffic demand and complexity of radio access network motivate developing scalable wireless communications by adopting system intelligence. The lack of adaptive reconfiguration in radio transmission systems may cause dramatic impacts on the traffic management concerning congestion and demand-capacity imbalances. This has driven the industry to jointly access licensed and unlicensed

Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search methods with a deep learning network. However, in practice, sufficient and well-calibrated

With the prevalence of intelligent mobile applications, edge learning is emerging as a promising technology for powering fast intelligence acquisition for edge devices from distributed data generated at the network edge. One critical task of edge learning is to efficiently utilize the limited radio resource to acquire data samples for model training at an edge server. In this paper, we develop a novel

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

We a practical multiunit heterogeneous spectrum market in which each buyer may request multiple channels with different bid prices at different geographical regions and each channel is associated with a reserve price indicating the desired revenue of the seller. The degree-of-freedom brought by multiunit trading and (reserve and bid) price diversity in such a market can be exploited to break the truthfulness

We present a novel game-theoretic, Bayesian reinforce-ment learning (RL) and deep learning (DL) framework to represent interactions of miners in public and consortium blockchains with mobile edge computing (MEC). Within the framework, we formulate a stochastic game played by miners under incomplete information. Each miner can offload its block operations to one of the base stations (BSs) equipped with

We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deployment and dynamic environment. The available spectrum is divided into several orthogonal sub-channels, and the donor base station (DBS) and all IAB nodes have the same spectrum resource for allocation, where

Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system

Hybrid beamforming (HB) is a promising technology for the millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) system, which supplies high data capacity with low complexity for next-generation communication systems. However, the joint design of digital and analog beamformer is a non-convex optimization problem due to the hardware constraints of analog shifter arrays. To address this

Sparse code multiple access (SCMA) and polar codes are key techniques for 5G systems. Towards to the realization of massive machine type communications (mMTC) in Internet of Things (IoT), we propose a joint channel estimation and decoding (JCD) scheme to enhance the performance of polar coded SCMA system. There are three parts in our JCD scheme. First, a sparse Bayesian learning (SBL) algorithm is

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

In this paper, we investigate the problem of opportunistic UAV transmission in D2D communication networks. UAVs are supposed to help transmissions of D2D users when they are employed to perform flying missions with given trajectories. On one hand, users can select appropriate UAVs as real-time relays according to the topology in the sky at different moments. On the other hand, due to flight characteristics

Internet-of-Things (IoT) devices that are limited in power and processing capabilities are susceptible to physical layer (PHY) spoofing (signal exploitation) attacks owing to their inability to implement a full-blown protocol stack for security. The overwhelming adoption of multicarrier techniques such as orthogonal frequency division multiplexing (OFDM) for the PHY layer makes IoT devices further

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

A new approach for blind channel equalization and decoding, variational inference, and variational autoencoders (VAEs) in particular, is introduced. We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, without using pilot symbols. We derive an approximate maximum likelihood estimate to

Modern transportation infrastructures are considered as one of the main sources of the greenhouse gases emitted into the atmosphere. This situation requires the decision-making players to enact the mass use of electric vehicles (EVs) which, in turn, highly demand novel secure communication technologies robust to various cyber-attacks. Therefore, in this paper, a novel jamming-robust communication method

Software defined networking (SDN) and network function virtualization (NFV) are the key enabling technologies for service customization in next generation networks to support various applications. In such a circumstance, virtual network function (VNF) scheduling plays an essential role in enhancing resource utilization and achieving better quality-of-service (QoS). In this paper, the VNF scheduling

In order to extend the communication coverage and improve system performance, the applications of unmanned aerial vehicles (UAVs) in wireless communications have attracted a lot of attention in the industry. In this paper, we propose a power control algorithm in energy harvesting (EH)-based cognitive mobile relay networks where an UAV is equipped with a decode-and-forward (DF) relay to cooperate the

Cooperative spectrum sensing (CSS) systems use underlay or overlay strategies to identify underused or unused bands to achieve higher spectrum utilization. In hybrid underlay and overlay systems, spectrum sensing results may be sparse and a general reporting strategy is required to take multiple spectrum usage status into account. In this paper, we propose a general quantized soft decision (QSD) based