Extremely large-scale reconfigurable intelligent surface (XL-RIS) has been a prospective capacity-enhancing technique. Faced with XL-RIS-aided millimeter wave (mmWave) communications, we propose a low-complexity channel estimation strategy with the near-field spherical wavefront assumption. It separates the whole channel estimation procedure into two phases to decrease pilot use and computational complexity: 1) estimation of angular-domain parameters of the base station (BS) and users equipped with the hybrid beamforming architecture, and 2) estimation of cascaded angular- and polar-domain parameters of the XL-RIS. Particularly, in the first and second phases, the three-dimensional multiple measurement vector compressive sensing (3D-M-CS) framework and the 3D distributed CS (3D-D-CS) framework are proposed for enhanced parameter estimation, respectively. Then the variants of Look Ahead Orthogonal Match Pursuit (LAOMP), i.e., 3D-M-LAOMP and 3D-D-LAOMP, are developed for solving our proposed CS frameworks.

In this paper, we consider a reconfigurable intelligent surface (RIS) aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) system. The system can obtain the huge gain via joint active beamforming at the base station (BS) and passive beamforming at the RIS. However, due to weather and atmospheric effects, outdoor RIS antenna elements are subject to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. These blockages can cause an approximate squared power/SNR loss for the system. Different from the conventional array diagnosis, the RIS has no signal processing capability. Thus, we propose the joint array diagnosis and channel estimation techniques containing two stages to solve the problem. At the first stage the channel parameters at user equipment (UE) and BS are estimated using an iterative reweighted (IR) method. At the second stage, the array blockage coefficient vector and the effective sparse channel parameters at RIS are jointly estimated via solving a two-timescale non-convex optimization problem. We propose two algorithms, i.e., a batch algorithm (BA) and a two-timescale online joint array diagnosis and channel estimation (TOJADCE) algorithm to solve the problem and compare the performance of these two algorithms. Finally, to speed up the convergence of long-term variable and improve estimation performance, we propose a noise reduction (NR) algorithm. The simulations verify the effectiveness of our proposed algorithms.

Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We first aim to maximize the sum energy harvested by all the energy harvesters subject to constraints on rate, transmit power, interference, and subchannel assignment. Due to the non-convexity of the formulated problem, we relax the integer variable and introduce an auxiliary variable to transform the original problem into a convex problem. Then, the Lagrangian and subgradient methods are adopted to obtain the optimal solutions. However, this scheme may lead to a severe fairness issue among different links. In view of this fact, we further propose an energyharvesting scheme for max-min fairness. In particular, we aim to maximize the energy harvested by the worst case individual link. We show that this problem can be solved in a similar manner to the problem of sum harvested energy maximization. Simulation results are presented to verify the convergence and fairness performance of the proposed algorithm, and to reveal a novel tradeoff between the network harvested energy and sensing time.

A new volume-based user selection algorithm with low complexity is proposed for a multiuser multiple-input and multiple-output downlink system based on block diagonalisation precoding. The new algorithm achieving this reduced computational complexity is compared with other user selection algorithms, such as semi-orthogonal user selection (SUS) and capacity-based user selection algorithms. The proposed algorithm stems from the new volume-based user selection method that uses the product of the diagonal elements in the upper-triangular matrix obtained via Householder reduction procedure of QR factorisation to the selected users channel matrix. The computational effort of the new algorithm is reduced by one-fourth compared with SUS algorithm. Compared with the capacity-based algorithm, the proposed algorithm does not need to perform the singular value decomposition operation and water-filling algorithm during each user selection step, and hence, significantly reduces the computational time. If the maximum number of supportable users is K̂, the calculation results show that the capacity-based algorithm has 4K̂ times the complexity of the proposed algorithm. Furthermore, the simulation results demonstrate that the volume-based algorithm displays better capacity performance than the SUS algorithm, and the sum-rate capacity of the volume-based algorithm is comparable with that of the capacity-based algorithm but with much less computational complexity.

Prior works on performance analysis for cognitive radio networks often assume perfect channel state information (CSI), which may not be readily available. This paper investigates the effects of the outdated CSI on the performance of a dual-hop multi-antenna multi-relay spectrum sharing system. To exploit the benefits of available multiple antennas, collaborative zero-forcing beamforming is considered at the secondary user (SU) source node with the outdated CSI and maximum ratio combining diversity reception at the destination node. The $N$ th best relay selection strategy is adopted at the relays. Analytical outage probability and bit-error rate expressions are derived for the SU. Moreover, we examine the high signal-to-noise ratio regime and present closed-form expressions for the asymptotic outage probability and asymptotic bit-error rate with and without feedback delay. In addition, we derive the closed-form ergodic capacity expression for the special case of our considered system, and analyze the effects of delay on the capacity. Furthermore, the closed-form outage probability expression for the primary user is obtained and the impact of delay on the interference on the primary user is investigated. Monte Carlo simulations are carried out to verify our analysis.

This paper is concerned with the secrecy rate based optimization problems in a mixed radio frequency (RF) / free space optical (FSO) spectrum sharing network, wherein multiple single-antenna secondary users (SUs) are connected to a decode and forward (DF) single-antenna relay through RF links and the relay is connected with the secondary destination through an FSO link. The RF and FSO channels are assumed to follow Rayleigh and Gamma-Gamma distributions with the effect of pointing errors, respectively. The three schemes, i.e., direct transmission, single-user cooperative jamming and multi-user cooperative beamforming jamming, are proposed to improve the physical-layer information security in the presence of a single-antenna eavesdropper. The mutual interference between the primary and secondary networks is considered. The problems are to jointly optimize the SU transmit power and jamming power for the best secrecy performance under both the RF and FSO dominant cases. Such problems are nonconvex. The Dinkelbach approach is adopted to solve the optimization in the direct transmission and single-user cooperative jamming schemes and a two-level optimization approach with semidefinite relaxation (SDR) is applied to obtain the solution for the multi-user cooperative beamforming scheme. Moreover, the SDR optimality is proved by Karush-Kuhn-Tucker conditions analysis. Simulation results are provided, and the results show that the proposed multi-user cooperative beamforming jamming can attain the higher achievable secrecy rate than the zeroforcing beamforming scheme and other two suboptimal designs.