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In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches. [ABSTRACT FROM AUTHOR]

Random interleavers are widely employed in digital communication systems to combat channel fading and burst errors. In applications such as grant-free access by Internet of Things (IoT) devices, accurately identifying a specific irregular interleaving pattern within an interleaver period is vital to both terminal recognition and data recovery. In this work, we investigate effective approaches for random interleaving pattern identification in Reed-Solomon (RS) coded data streams. We first propose an algorithm of low computational complexity to detect positions of code symbols belonging to the same RS codeword group (RSCG) under modest bit error rate. We further develop another low-complexity algorithm to successfully identify random interleaving patterns for RS code symbols within each RS codeword under moderate to high error rate applications. Our theoretical analysis and simulation results corroborate to demonstrate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

It is crucial for legitimate wireless surveillance to enhance the continuity of the monitor. In this letter, we investigate a non-outage probability maximization problem for a jamming-assisted eavesdropping scenario, where MRC and MRT schemes are applied to receive and transmit antennas at a full-duplex monitor, respectively. We first take QoS to a suspicious receiver into non-outage probability to formulate continuous eavesdropping. A closed-form expression of continuous eavesdropping non-outage probability is derived subject to the desired minimum SINR at a suspicious receiver. Then we prove the quasiconcavity of this expression and show the optimal solution to maximize non-outage probability by a simple bisection search. Numerical results demonstrate the continuous eavesdropping performance is optimal when the jamming signals are sent with the optimal power. The number of receive and transmit antennas at the legitimate monitor is positively correlated with the continuity of eavesdropping. [ABSTRACT FROM AUTHOR]

The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes. [ABSTRACT FROM AUTHOR]

Power control in massive multiple input multiple output (MIMO) systems is an appealing technique to improve network performance and reliability. The traditional methods to solve such problems are based on the convex optimization theory, which incurs high computational complexity. In contrast, this work leverages deep neural networks to maximize the minimum data rate of the downlink users in the massive MIMO network. Based on the different setups in the practical systems, namely in-distribution case, weakly out-of-distribution case, and out-of-distribution case, we propose three approaches to solve them. Specifically, first, we establish a densely connected neural network for solving the power control problem. Then, for the case where the test data and training data come from the same distributions (the in-distribution case), we use the established neural network to obtain an approximately optimal power control strategy, and propose a self-training-based algorithm to use the unlabeled samples collected in the actual system to further improve the system performance. For the case that the test data and training data come from different distributions and a small amount of labeled target domain data can be obtained (the weakly out-of-distribution case), we propose a pre-training and fine-tuning algorithm to solve the problem. For the issue that we can not obtain the labels of the target domain data (the out-of-distribution case), we transform the problem into a covariate shift problem and propose an algorithm by weighting the loss function. Numerical results demonstrate that the proposed schemes not only match the optimal solution well but also have low online inference complexity. [ABSTRACT FROM AUTHOR]

With increasing traffic congestion and accidents, the Internet of Vehicles (IoV) has become a focus of fifth generation (5G) heterogeneous networks (HetNets) and intelligent transportation systems (ITS). IoV has been studied extensively to improve vehicles, road safety, and efficiency. However, the limited spectrum efficiency and interference caused in the 5G HetNets may require robust technology and architecture. The enabling requirements of IoV in 5G HetNets are elaborated and conceptualized in the article. The deep learning algorithms proposed and used in IoV in 5G HetNets can be achieved to reduce delay and improve transmission reliability. The research progress, advantages, and challenges of deep learning in 5G HetNets IoV are illustrated and explored in detail. Moreover, the overall framework of IoV in 5G HetNets is outlined and compared with the related research, which can become the Foundation for future development of intelligent IoV. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV’s maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, due to the bandwidth constraint, the distributed nodes (DNs) might only provide binary representatives of the source signal, and then transmit them to the central node (CN). In this paper, we consider the joint estimation of signal amplitude and background noise variance from binary observations over decentralized channels. We first analyze the Cramér–Rao lower bounds (CRLBs) of the parameters of interest and develop a quasilinear estimator (QLE), in which the desirable estimates can be obtained from several intermediate parameters linearly. Next, we consider a more realistic situation where the decentralized channel is noisy during the data transmission. Based on the error propagation model, the asymptotic analysis shows that the performance of the proposed QLE is mainly dominated by the thresholds of the quantizers, which encourages us to adopt a correlated quantization (CQ) scheme by exploiting the spatial correlation among background noises/channel noises. To ease the implementation of QLE in practice, an adaptive quantization (AQ) scheme is also proposed so as to obtain reasonable selections of the required thresholds. Finally, numerical simulations are provided to validate our theoretical findings. [ABSTRACT FROM AUTHOR]

To meet the demand of green communications of ultradense mobile devices (MDs), an energy-efficient mechanism, jointly considering the computation offloading and resource management, is designed to minimize the network-wide weighted energy consumption under the delay constraints of MDs for ultradense heterogeneous networks. Such a mechanism tightly integrates with the adjustment of computation capability and transmission power of MDs. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we design an effective algorithm to solve it. Specifically, by utilizing the powerful global searching capability of genetic algorithm (GA) and the accurate local searching capability of particle swarm optimization (PSO), the adaptive GA with diversity-guided is firstly used for coarse-grained search, and then adaptive PSO is utilized for fine-grained search. After that, some detailed analyses on the convergence, computation complexity and parallel implement are provided for algorithms. Simulation results show that the designed algorithm can achieve a lower network energy consumption than other offloading algorithms in general. At the same time, the numerical simulation also reveals that the designed algorithm may be more suitable for ultradense networks than existing algorithms. [ABSTRACT FROM AUTHOR]

The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G. [ABSTRACT FROM AUTHOR]

This paper studies an uplink nonorthogonal multiple access (NOMA)-enabled mobile-edge computing network. Specifically, we focus on the practical design of secure offloading without knowing the eavesdropper's channel state information. The aim is to maximize the minimum antieavesdropping ability (AEA) for uplink NOMA users subject to the worst-case secrecy rate requirements and limited transmission power budgets. The formulated problem is nonconvex and difficult to be solved directly. In order to tackle this issue, we propose an efficient iterative algorithm by jointly designing the secrecy rate, local computing bits, and power allocation. The local computing bits and the power allocation are derived in closed form. Numerical results are provided to demonstrate the efficiency of our proposed scheme in terms of AEA. [ABSTRACT FROM AUTHOR]

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is the connections of embedded sensing technologies that can be integrated with intelligent transportation systems (ITS), which can take advantage of the advanced sensing fusion to implement the intelligent interaction. Moreover, the environmental wireless signals was harvested and utilized to power the charging sensors, which can be used to assist and enhance the ITS and have attracted extensive attentions in industries. The simultaneous wireless information and power transfer (SWIPT) enabled ITS was designed and proposed with the advanced sensing fusion, where the sensor nodes of the source and destination were all equipped with multiple antennas and the maximum ratio transmission (MRT) precoding and maximum ratio combination (MRC) were adopted respectively in the proposed system, where the sensing nodes can be embedded into the data addressing equipment to fusion the sensing information. For the established massive sensing node transmission system, the transmission power, the number of source antennas and destination antennas were considered to fuse and optimize to obtain the maximum energy efficiency (EE) of the ITS. Since the original fusion problem was non-convex and difficult to solve, which can be transformed to convex problem via the large SNR approximation method and thereby the optimal solution of the sensing fusion problem was derived through the Lagrange algorithm and the Lambert function W. The simulation results showed that the proposed fusion algorithm can achieve remarkable EE compared with other algorithms and be utilized to the massive sensors to improve the intelligent predication and transportation for vehicle network. [ABSTRACT FROM AUTHOR]

This article studies the confidential transmission performance of an electric vehicle (EV) in heterogeneous network when it communicates with vehicle to grid (V2G). Based on the relay selection strategy that maximizes the signal-to-noise ratio(SNR), the electric vehicle as a legitimate user in this article uses a multi-antenna maximum ratio combining method for signal reception. Among them, a single antenna is configured for the power grid sender, relay nodes and illegal eavesdropping users. The wireless channel adopts Nakagami- ${m}$ fading channel and the relay adopts decode and forward (DF) method. First, based on the stochastic geometric analysis method, statistical characteristics such as probability density function(PDF) and cumulative distribution function(CDF) of the received SNR are obtained at legitimate users and illegal eavesdropping users, respectively. Then, a functional analysis method is used to derive closed expressions for the secrecy outage probability (SOP) and non-zero security capacity probability in multi-eavesdropping user systems. Finally, the effects of the system’s related parameters on SOP and non-zero security capacity probability are verified through simulations. The simulation results prove the correctness of the theoretical analysis, which can guarantee the privacy and security of electric vehicle users in heterogeneous network. [ABSTRACT FROM AUTHOR]

In this article, we propose a cognitive relaying (CR) scheme with both the secondary source (SS) and the secondary relay (SR) capable of harvesting wireless energy. Along with the data transmission of primary system, a power beacon actively transfers wireless power to SS and SR. The interference from the primary data transmission can increase the amount of harvested energy at SS and SR. After harvesting and accumulating energy for a number of successive time blocks, the CR will be performed in two continuous time blocks. In CR-Block-One, SS will transmit the secondary data to SR by exhausting its available energy. In CR-Block-Two, SR will forward the secondary data to SD if it has correctly decoded the data from SS; otherwise, it will stay silent. We properly model the energy accumulation process of SS and SR by defining a group of discrete energy levels. We analyze the approximate throughputs of both systems by considering various transmit powers of SS and SR. The optimal number of time blocks used for the wireless power transfer is determined through maximizing the secondary throughput while slightly degrading the primary performance. Numerical results show that the secondary data transmission can be well accommodated. [ABSTRACT FROM AUTHOR]

This letter studies robust multi-input multi-ouput (MIMO) transmission strategies, where the transmitter only has imperfect channel state information (CSI) and the transmission covariance matrix is designed to maximize the worst system capacity. We first extend Danskin’s Theorem to complex spaces by the aid of Wirtinger’s subgradients. Then, with the extended theorem, we propose a subgradient projection (SP) based algorithm to solve the covariance matrix, in which finding the worst channel error is key and can be formulated as a nonconvex minimization problem. We deploy the successive convex approximation (SCA) technique and create a sequence of convex problems to solve this problem. Simulation results demonstrate the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Thanks to comprehensive potential with new access technology, massive bandwidths and ultrahigh reliability, it is highly expected that recent 5G network and beyond 5G network (B5G) will fundamentally influence various autonomous systems, applications and devices. Also, Artificial intelligence (Ai) is expanding its applicability to numerous existing and next generation systems and research branches. Conversely, Ai can be utilized to threaten 5G-enabled components as well as to attack Ai-based systems and machine learning algorithms. So, it is indispensable to investigate new Ai-based security threats and solutions in 5G-enabled smart cities. in this article, we introduce Ai-driven 5G integrated virtual emotion system, called 5G-i-VEmoSYS, which covers Ai-VEmoBAR, Ai-VEmoFLOW, and Ai-VEmoMAP as applications in 5G environments. First, we explain how to operate those Ai-driven promising applications in 5G-enabled components including autonomous vehicles, smart UAVs or drones. Also, we describe how to perform those applications in public areas and private areas with different perspectives. Then, we carefully deal with research challenges, security threats and issues according to the proposed 5G-i-VEmoSYS. [ABSTRACT FROM AUTHOR]

Direct-conversion receivers (DCRs) have been adopted in wideband communication systems owing to their simple structure and low cost, however, their operation is affected by amplitude and phase mismatches between their analog inphase (I) and quadrature (Q) branches, as well as the discrepancy of low-pass filter coefficients between these two channels. In this paper, a blind adaptive frequency-dependent I/Q imbalance compensator is proposed, which exploits the complex properness (second-order circularity) of ideal constellation mappings to provide more enhanced insight into the problem setting within the proposed compensator. This serves as a basis for a novel full second-order performance assessment framework, which is established through a joint consideration of the weight error covariance and complementary covariance in both the transient and steady-state stages. This conjoint analysis is further shown to facilitate accurate quantification of the overall mirror-frequency interference attenuation capability of the proposed compensator. Simulation results in an orthogonal frequency division multiplexing (OFDM) transmission system demonstrate the excellent performance of the proposed compensator. [ABSTRACT FROM AUTHOR]

Complex noncircularity is the manifestation of a rotation-dependent probability distribution, and can arise from the properties of the input signal and/or system noise. In contrast to the standard mean-square error (MSE), it is now well understood that the minimum Gaussian entropy criterion provides statistically optimal complex-valued adaptive filters, even when the estimation error is noncircular Gaussian. However, a rigorous analysis of the class of information-theoretic adaptive filtering algorithms based on the entropy cost function is still under investigation. To this end, we provide physical insights into the full second-order performance of the least stochastic entropy (LSE) adaptive filtering algorithm. This is achieved by considering the coupled evolution of both the weight error covariance matrix and complementary covariance matrix. A mean-square stability bound on the step-size is established to guarantee convergence, and to allow for a closed-form evaluation of the weight error variance of the LSE at the steady-state. Simulations in the system identification setting support the analysis. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation. [ABSTRACT FROM AUTHOR]

Proposed by Maddah-Ali and Niesen, a coded caching scheme has been verified to alleviate the load of networks efficiently. Recently, a new technique called placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, we consider a caching system in the scope of ultra dense networks (UDNs). Each base station (BS) has a finite cache and stores some contents. We propose an efficient coded content caching scheme called double coded caching to make the transmission robust to in-and-out wireless network quality. Then the dynamic caching and multicast scheduling are considered to jointly minimize the average delay and power of the content-centric wireless networks. This stochastic optimization problem can be formulated as a Markov decision process (MDP) with unknown transition probabilities and large state space. We propose a deep reinforcement learning approach to deal with the decision problem. Our algorithm uses a variational auto-encoder (VAE) neural network to approximate the state sufficiently, and uses a weighted double Q-learning scheme to reduce variance and overestimation of the Q function. Numerical results demonstrate that the proposed double coded caching scheme increases the probability of the successful transmission, and the caching and scheduling policy can effectively reduce the delay and the power consumption. [ABSTRACT FROM AUTHOR]

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks. [ABSTRACT FROM AUTHOR]

User-centric (UC) clustering, an innovative design principle for ultradense networks (UDNs), supports dynamically fluctuating adaptive network topologies. In this article, we introduce the UC-UDN architecture and provide a tutorial on UC clustering design by generalizing the problem under practical constraints. In the context of UC clustering, we briefly present various promising methods that represent constraint options and also provide a pair of case studies focused on design tradeoffs. Finally, the salient future directions of UCUDNs are identified. [ABSTRACT FROM AUTHOR]