Massive multiple-input multiple-output (MIMO) architecture is promising to be adopted in the 5G/6G communication systems to combat the severe attenuation of millimeter-wave and terahertz bands by implementing precoding and combining at the transmitter and receiver jointly. Nevertheless, such massive MIMO-based systems are highly susceptible to pilot spoofing attacks (PSAs) because of the openness of wireless channel and the public pilot sequences. These PSAs will not only render significant decrease in system capacity, but also make the transmitted information leak to the attackers. In this paper, a novel multi-cue detection and identification network (MCDI-net) is proposed to conduct spoofing detection and identify the attacked user simultaneously. The MCDI-net integrates a dual-branch convolutional neural network with squeeze-and-excitation (SE) attention modules to extract the complementary features from two distinct cues, which are in-phase/quadrature (I/Q) and statistical features derived from pilot correlation and projected average power. This multi-cue fusion approach can enhance the network’s ability to detect pilot spoofing and identify the attacked legitimate user accurately. Numerical results are presented to validate the superior performance of the MCDI-net framework in detection and identification accuracy compared to the existing ones.

For 5G/B5G communication systems, to cater the explosive demands in communication rates, extending communication band to millimeter wave (mmWave) seems to be an essential solution, and hence massive multiple-input-multiple-output (MIMO) architecture is usually adopted to combat the severe path-loss of mmWave signals. However, the traditional fully digital precoding manner is inapplicable owing to its high hardware cost and power consumption rendered by the individual requirements on radio frequency (RF) chains of each antenna. In this paper, for massive MIMO systems with multiple users, a novel hybrid precoding scheme is proposed based on the concept of equivalent channel. Specifically, for each user in the system, its analog combiner and analog precoder are designed jointly aiming to maximize the achievable rate with its equivalent channel. After the analog precoder/combiner phase-shifters for each user has been obtained, see them as a part of the channel and form a comprehensive channel of the multi-user system, and then the total baseband digital precoding at the BS is implemented with the block diagonalization method to delete the interference among users. Simulation results show that the proposed scheme can achieve higher spectral efficiency with low complexity compared to the existing schemes.

For large-scale multiple-input multiple-output (MIMO) systems, antenna selection (AS) schemes are usually used to reduce hardware cost and energy consumption, in which only the antennas with low correlation each other and high gain are activated. However, such existing AS schemes may not necessarily ensure the security of information transmission when eavesdroppers are involved as the channels between legitimate users and eavesdroppers may correlate each other due to the inadequate scattering conditions in the propagation. To this end, a novel AS and artificial noise (AN) injection strategy is proposed to enhance system security. Specifically, integrating the correlation between the paths of the legitimate user (LU) and the eavesdropper with channel gains, a comprehensive metric is defined, based on which the optimal antennas used to transmit signals are obtained. Then, based on a deep Q-network (DQN), the subspaces for precoding confidential messages and injecting AN are alternately optimized to maximize the system secrecy rate. Simulation results illustrate that the proposed scheme can obtain much higher secrecy rate compared to the existing ones.

Owing to the openness of wireless channels, ensuring the security of communication for massive multiple-input multiple-output (MIMO) systems faces severe challenges. For the massive MIMO system in the presence of a full-duplex (FD) multi-antenna eavesdropper, a dual artificial-noise (AN) injection scheme is proposed to enhance communication security in this paper. Specifically, AN is superimposed onto the transmitted pilot signals to interfere with the eavesdropper from estimating the legitimate user channel in pilot training phases. In the phase of data transmission, the AN generated in the null space of the legitimate user is introduced to prevent the eavesdropper from receiving the confidential messages. Meanwhile, an iterative optimization algorithm is presented to obtain the optimal power allocation between the information-bearing signals and the AN for the potential countermeasures taken by the eavesdropper. Numerical results are presented to validate the superior performance of the proposed scheme in security ensurance compared to the conventional ones with AN injection schemes.

Product-like codes are widely used in high-speed communication systems since they can be decoded with low-complexity hard decision decoders (HDDs). To meet the growing demand of data rates, enhanced HDDs are required. In this paper, we propose a novel soft-aided HDD (SA-HDD), termed iterative bounded distance decoding with random flipping (iBDD-RF), for product-like codes. In iBDD-RF, the soft reliability of a bit is a weighted sum of the output of bounded distance decoder (BDD) and the channel log-likelihood ratio (LLR). When the amplitude of the soft reliability of a bit is less than a given threshold, it is flipped with a given probability. This random flipping may make the decoder escape from the local optimum. To optimize the threshold and the flipping probability, we derive the density evolution (DE) equations of iBDD-RF for product codes (PCs) and staircase codes (SCs). Our extensive numerical results show that iBDD-RF outperforms iBDD with scaled reliability (iBDD-SR) over the binary-input additive white Gaussian noise (Bi-AWGN) channel. Particularly, for a PC with (255,239,2) Bose-Chaudhuri-Hocquenghem (BCH) code and an SC with (254,230,3) BCH code, iBDD-RF performs about 0.25 dB and 0.28 dB better than iBDD-SR, respectively.

Ensuring secrecy information transmission with enhanced energy efficiency is one of the critical issues in unmanned aerial vehicles (UAVs)-assisted wireless systems owing to the open nature of wireless channels and the limited battery power. In this article, the secrecy energy efficiency (SEE) is investigated for a more critical scenario, where an UAV acts as a bidirectional relay exchanging information between the two ground users and a malicious terminal attempts to implement eavesdropping and interfering on the confidential information simultaneously. Multiple parameters, such as time scheduling, power allocation, UAV trajectory within a given flight cycle, and both multiple access (MA) and broadcast (BC) phases are optimized jointly to maximize the system SEE. To address this nonconvex optimization problem involved with coupled variables tightly, we employ the block coordinate descent (BCD) method and develop an iterative algorithm that leverages successive convex approximation (SCA) and the Dinkelbach algorithm to obtain the optimal solution. Meanwhile, simulation results are presented to demonstrate the superiority of our proposed scheme in terms of SEE.

The correlation filter(CF)-based tracker is a classic and effective model in the field of visual tracking. For a long time, most CF-based trackers solved filters using only ridge regression equations with$l_{2}$-norm, which can make the trained model noisy and not sparse. As a result, we propose a model of adaptive sparse spatially-regularized correlation filters (AS2RCF). Aiming to suppress the noise mixed in the model, we improve it by introducing an$l_{1}$-norm spatial regularization term. This converts the original ridge regression equation into an Elastic Net regression, which allows the filter to have a certain sparsity while maintaining the stability of model optimization. The entire AS2RCF model is optimized using alternating direction method of multipliers(ADMM), and quantitative evaluations through extensive experiments on OTB-2015, TC128 and UAV123 demonstrate the tracker's effectiveness.

As subclasses of spatially coupled turbo-like codes (SC-TCs), hybrid coupled serially concatenated codes (HC-SCCs) and spatially coupled serially concatenated codes (SC-SCCs) are attractive for streaming applications. However, it is a long-standing problem to estimate the error floors of HC-SCCs and SC-SCCs. To tackle this problem, we present a genie-aided approach in this paper. Specifically, we first show that the performance of a given HC-SCC or SC-SCC can be lower bounded by a hybrid concatenated code which is obtained by assuming the coupled sub-sequences are known or partially known. Second, we derive the average input-output weight enumerating functions (IOWEF) of the hybrid concatenated code ensembles corresponding to SC-SCC and HC-SCC. Third, the obtained IOWEFs are used to estimate the error floors. The numerical results show the tightness of the proposed method in estimating the error floors of SC-SCCs and HC-SCCs. We then use the proposed method to analyze the impact of the memories of the component convolutional codes on error floor. Particularly, we show that, for a given total memory order$v$, the lowest error floor is achieved by selecting the memories of the outer and inner component convolutional codes as$\lceil \frac {v}{2} \rceil $and$\lfloor \frac {v}{2} \rfloor $, respectively. In addition, for both SC-SCCs and HC-SCCs, reduced error floor can be achieved by increasing the outer coupling memory.

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones.

Due to overshadow and channel fading, many mobile users are unable to receive the signal transmitted from satellite directly. Hence, some relay stations should be set to help this type of users to receive signals reliably. In this paper, we present a novel cognitive hybrid satellite-terrestrial model, where two cognitive relays forward their received signal for a mobile user successively. Furthermore, we address its achievable rate maximization. We first convert the co-channel interference threshold into transmit power constraints, and then formulate the maximization of the achievable rate as an optimization problem. Based on Karush-Kuhn-Tucker conditions, the optimization problem is decomposed into four cases, each of which is solved in closed form. Simulation study with different system settings is presented, and the efficiency of the proposed power allocation scheme is shown.

For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way to obtain the available spectrum for objects. Wide-band spectrum sensing plays an important role in building such CR networks of IoT. In this paper, we propose a novel dynamic compressive wide-band spectrum sensing method based on channel energy reconstruction. After a bank of wide-band random filters is employed to measure the channel energy, rather than to recover all the channel energy in the whole spectrum, only the channel energy with a changing occupancy status in consecutive time slots is recovered. Furthermore, it is unnecessary to use reconstruction algorithm unless there are two or more channels changing their occupancy status. Compared to the existing methods, our proposed schemes bear significant improvements in the probability of detection and reduction of probability of false alarms. Simulation results also show its fast speed and robustness to noise.

Because of its outstanding performance in extending coverage and reducing transmit power, wireless relay cooperation is deemed as one of the promising techniques to realize green-broadband communication in the future. In such relay cooperative systems, optimal power allocation not only can prolong the lifetime of users, but also is an effective way to reduce radiation in the environment. In this paper, for amplify-and-forward-relay cooperative networks, where multiple communication pairs share a common relay, we propose a novel power allocation scheme, which has considerably lower complexity compared with the existing solution schemes. The objective of this paper is to minimize sum-source-power consumption under the conditions that the transmit power of all nodes in the network is constrained and their predetermined target signal-to-noise ratios are satisfied. By providing sufficient analytical evidence, we study the optimality, convergence, and computational complexity of our proposed scheme. Through numerical simulation, we further justify the effectiveness and efficacy of our scheme compared with the existing works.