This paper focuses on securing confidential communication in multiple intelligent reflecting surfaces (IRS) assisted terahertz (THz) systems, where a potential eavesdropper can intercept either the base station (BS)-IRS link or the IRS-user link. Notably, the secure transmission may be intercepted and blocked by the eavesdropper due to the blockage-prone nature in THz bands. To characterize the blocking effects of the eavesdropper, the blocking-based path loss is first investigated. With the imperfect eavesdropper channel state information (ECSI), the worst-case secrecy rate (WCSR) is derived, and a joint optimization problem of hybrid beamforming at the BS and reflecting beamforming at the IRS is formulated. For the BS-IRS link eavesdropping, the zero-forcing (ZF) principle-based hybrid beamforming and the closed-form phase shifts of multiple IRSs are respectively proposed. For the IRS-user link eavesdropping, an iterative algorithm is proposed to tackle the non-convex optimization problem with a given information leakage threshold. Finally, a robust secure transmission strategy for multi-eavesdropper systems is further investigated. Simulation results demonstrate that compared with blockage-unaware scenarios, our proposed scheme can resist the adverse effects of the blockage-prone nature of THz waves on information security, and significantly boost secrecy performance.

This paper studies the joint transceiver design and power splitting (JTDPS) for a downlink multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. Aiming to minimize the transmit power by jointly optimizing the transmitter at a base station, the power splitting (PS) factors, and information decoding receivers at mobile stations (MSs) subject to both the predefined mean-square error (MSE) and the given energy harvesting (EH) constraints. The formulated problem is a nonconvex JTDPS optimization problem, which cannot be solved directly. To solve it, an iterative optimization framework composed of joint transmitter and PS factor optimization (JTxPS) subproblem and a receiver side minimum mean-square error (MMSE) minimization subproblem is proposed. By iteratively solve the two subproblems, the original optimization problem is solved. The sufficient and necessary conditions for the feasibility of the formulated JTDPS and the JTxPS problems are analyzed. Theoretical analysis shows that the JTxPS subproblem is also nonconvex and can be solved by using convex semidefinite programming (SDP). Considering the high computational complexity of the SDP-based scheme, a low-complexity suboptimal scheme based on the traditional MMSE transceiver design is also proposed. Simulations are done to validate the theoretical analysis, and results show the effectiveness of the proposed schemes.

Ultra-reliable low-latency communication (uRLLC) emerges as one of the three service categories in fifth-generation (5G) wireless networks, with a potential to support multiple advanced services for latency-sensitive communications. The inherent openness of the wireless environment makes security in 5G unprecedentedly urgent to be addressed. Accordingly, the physical layer security (PLS) technique exploits the randomness characteristics of the wireless channel to fulfill security and confidentiality of information transmission. Different from many current works, which ignored the significance of security in uRLLC, this paper studies secrecy improvement strategy design in uRLLC scenarios. First, we propose suitable performance metrics for evaluating PLS of uRLLC and make a compromise between reliability, latency, and security, while maximizing the network throughput. Next, we identify the key challenging issues for achieving PLS in the context of uRLLC, and discuss the role of cooperative communication and mobility in terms of possible solutions. To the best of our knowledge, this is the first paper to make a trade-off among reliability, security and network throughput. Simulations validate the theoretical analyses.

To reduce the influence of both the baseline wander (BW) and noise in the electrocardiogram (ECG) is much important for further analysis and diagnosis of heart disease. This paper presents a convex optimization method, which combines linear time-invariant (LTI) filtering with sparsity for the BW correction and denoising of ECG signals. The BW signals are modeled as low-pass signals, while the ECG signals are modeled as a sequence of sparse signals and have sparse derivatives. To illustrate the positive of the ECG peaks, an asymmetric function and a symmetric function are used to punish the original ECG signals and their difference signals, respectively. The banded matrix is used to represent the optimization problem, in order to make the iterative optimization method more computationally efficient, take up the less memory, and apply to the longer data sequence. Moreover, iterative majorization-minization (MM) algorithm is employed to guarantee the convergence of the proposed method regardless of its initialization. The proposed method is evaluated based on the ECG signals from the database of MIT-BIH Arrhythmia. Simulation results show the advantages of the proposed method compared with wavelet and median filter.

This paper studies joint beamforming problems for an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system, and the goal is to maximize the sum rate by jointly optimizing the transmit beamforming vectors at BSs and the reflective beamforming vector at the IRS, subject to the individual maximum transmit power constraints at BSs, and the reflection constraints at the IRS. Due to the formulated optimization problem is highly non-convex, we propose an alternating optimization (AO) algorithm based on successive convex approximation (SCA) such that the transmit and reflective beamforming vectors can be optimized alternately. We further consider the SINR balancing beamforming design scheme by maximizing the minimum SINR among all users to enhance the fairness among users, in which the transmit and reflective beamforming vectors are optimized in an alternating manner. The transmit beamforming vectors are optimized by the second-order-cone programming (SOCP) based on bisection method and the reflective beamforming vector is updated based on the technique of semidefinite relaxation (SDR). Simulation results show that the two proposed algorithms considerably outperform the benchmark zero-forcing (ZF) scheme. Moreover, the AO algorithm based on SCA has good communication performance than the other two schemes. And the AO algorithm based on bisection search guarantees the fairness for all users.

An interference alignment (IA) transceiver design scheme for multiple-input–multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI).