Reconfigurable intelligent surface (RIS) has become a crucial technology for the next generation wireless communication due to its good performance on low power consumption and high energy efficiency. In this paper, we investigate maximizing the sum of achievable data rates (SADR) of all the users by considering hardware impairments in the RIS-aided multiple-input multiple-output (MIMO) wireless communication system. Owing to the coupling effect of the multiple variables of the optimization problem (OP), we adopt the block coordinate descent algorithm (BCDA) to decouple the original problem into two sub-problems where the transmitting precoding matrix (TPM) and phase shifts are alternately optimized. Specifically, the upper bound optimal solution of the TPM can be obtained in closed form through the Lagrangian multiplier method, and majorization-minimization algorithm (MMA) is applied to solve the sub-problem of optimizing phase shifts. Simulation results demonstrate the necessity of considering hardware impairments into beamforming optimization and the effectiveness of introducing RIS into the MIMO system.

As an essential means of privacy protection technology, zero-knowledge proof has gradually been applied into various fields with the development of blockchain technology, such as the Internet of Vehicles and Bitcoin. Personal comprehensive credit score as a measure to promote social governance is closely related to personal privacy. Although there are currently credit score calculation systems for various application scenarios, these systems almost ignore user privacy protection, which leads to user information leakage or abuse. The combination of zero-knowledge proof and personal credit score calculation has been studied by a number of researchers at the present stage. However, all data are provided by users directly in the current schemes, which did not consider the data authenticity under the situation that users provided false data. In this paper, we utilize zero-knowledge proof to design a novel privacy protection scheme for personal credit score calculation, taking into account the authenticity verification of multi-dimensional user data. In addition, our scheme also proposes the concept of a universal verification platform based on blockchain for personal credit scores. This platform has more substantial applicability and versatility for any qualified institution that requires querying and verifying user’s credit scores. At the end of the paper, we conducted a security analysis and performance evaluation for the overall scheme.

Intelligent reflecting surface (IRS) is a promising technology for its capability of enhancing the desired signals and suppressing the interference. In this paper, we introduce IRS into a device-to-device (D2D) underlaying cellular system and investigate its application on physical layer security. The secrecy rate is maximized by jointly designing the received beamforming vector, power allocation and phase shift matrix. We apply the block coordinate descent (BCD) algorithm to decouple the optimization problem, and the semidefinite relaxation (SDR) technique is adopted to optimize the phase shift matrix. Simulation results show that the IRS is able to improve the system secrecy performance of the D2D underlying cellular system.

We investigate the worst-case robust beamforming for simultaneous wireless information and power transfer in a multiuser beamspace massive multiple-input multiple-output (MIMO) system. The objective is to minimize the transmit power of the base station subject to the individual signal-to-interference-plus-noise ratio and the energy-harvesting constraints under imperfect channel state information. Instead of directly resorting to semi-definite relaxation, we convert the initial non-convex optimization to a power allocation problem, which greatly reduces the computational complexity. The beamforming vectors are proven to be scaled versions of the estimated channels. The optimal scaling factors are then derived in closed-form. The simulations demonstrate that the proposed robust beamforming method achieves the globally optimal point for the initial design when the channel estimation errors are small while leads to satisfactory performance when the channel estimation errors are large.

This paper investigates multi-antenna beamforming and time allocation to maximize the network energy-efficiency (EE) in a wireless powered communication network (WPCN). Since the EE optimization problem has an inherent fractional form, it is difficult to obtain the optimal value directly due to the lack of convexity in the objective function. To overcome this challenge, we first convert the original problem into a more tractable one by the fractional programming. Then, two schemes are proposed to find the optimal value. In the first scheme, the iterative value is updated according to the EE based on energy beamforming and time allocation derived in the current iteration. In the second one, the optimal value is obtained by consecutively shrinking the region in which it is located. Simulation results show that the proposed two schemes can improve the network EE significantly compared with the algorithm that only pursues high throughput. In addition, it is shown that the two schemes have simlilar performance in the network EE. However, the computation complexity of the first one is lower than that of the second one.

In this paper, we design robust beamforming to guarantee the physical layer security for a multiuser beam division multiple access (BDMA) massive multiple-input multiple-output (MIMO) system, when the channel estimation errors are taken into consideration. With the aid of artificial noise, the proposed design are formulated as minimizing the transmit power of the base station, while providing legal users and the eavesdropper with different signal-to-interference-plus-noise ratio. It is strictly proved that, under BDMA massive MIMO scheme, the initial non-convex optimization can be equivalently converted to a convex semi-definite programming problem and the optimal rank-one beamforming solutions can be guaranteed. In stead of directly resorting to the convex tool, we make one step further by deriving the optimal beamforming direction and the optimal beamforming power allocation in closed-form, which greatly reduces the computational complexity and makes the proposed design practical for real world applications. Simulation results are then provided to verify the efficiency of the proposed algorithm.

In this paper, we propose a joint design of MIMO-PNC (multiple-input multiple-output physical layer network coding) and channel decoding in two way relay channels. The paper shows that if we adopt the same Repeat Accumulate (RA) channel code at the two end nodes, there would be a compatible decoder at the relay which can transform the received superimposed packet to the network coding form of the two packets efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the MIMO PNC channel. The simulation results show that our new scheme outperforms the previously proposed schemes which combined MIMO NC with channel coding/decoding significantly in terms of BER with little added complexity.