In this paper, a hybrid IRS-aided amplify-andforward (AF) relay wireless network is put forward, where the hybrid IRS is made up of passive and active elements. For maximum signal-to-noise ratio (SNR), a low-complexity method based on successive convex approximation and fractional programming (LC-SCA-FP) is proposed to jointly optimize the beamforming matrix at AF relay and the reflecting coefficient matrices at IRS. Simulation results verify that the rate achieved by the proposed LC-SCA-FP method surpass those of the benchmark schemes, namely the passive IRS-aided AF relay and only AF relay network.

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For massive multiple-input multiple-output (MIMO) systems, linear precoding is preferable to nonlinear precoding for better performance-complexity trade-off. However, linear precoding is still difficult to implement in practice for such large systems, because the precoding matrix involves the complicated matrix inversion that must be rapidly computed in real-time. In this paper, we use the large-scale property of massive MIMO systems, the excellent characteristics of the weighted Neumann series (WNS) matrix, and steepest descent (SD) method to devise a new iterative precoding for massive MIMO systems. First, by exploiting the characteristics of WNS iteration, we proposed a weighted Neumann series-steepest descent (WNS-SD) iterative algorithm to perform precoding with low-complexity, and the convergence condition is always met in underloaded scenarios. Second, by devising a novel first iterative step of the aforementioned WNS-SD iterative algorithm, we proposed an accelerated iterative algorithm, named the accelerated weighted Neumann series-steepest descent (AWNS-SD) algorithm. Furthermore, in the first iterative step of the AWNS-SD algorithm, we designed a promising preconditioning matrix for the SD algorithm based on the WNS matrix. Subsequently, via merger with the WNS iterative method, the AWNS-SD iterative precoding not only greatly improves the convergence rate of WNS-SD and other competitive precoding algorithms while maintaining low-complexity, but also guarantees a wide range of convergence. Finally, simulation results verify the validity of the theoretical analysis and show that without additional iteration steps, the proposed AWNS-SD precoding achieves near-optimal performance of the exact zero forcing (ZF) precoding in just one iterative step for typical massive MIMO system configurations. Furthermore, the diagonal matrix concept is applied to the preconditioning technique to further reduce the overall complexity. INDEX TERMS Massive MIMO, matrix inversion, iterative methods, convergence improvement, lowcomplexity.

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In this paper, we consider a cognitive unmanned aerial vehicle network (CUAVN), which consists of a leading unmanned aerial vehicle (LUAV) and a group of following unmanned aerial vehicles (FUAVs), and where the FUAVs fly irregularly from a data collection point to the next data collection point and transmit collected data to the LUAV. Our goal is to maximize the achievable throughput of the CUAVN by jointly adapting the FUAVs sensing duration, the trajectory of the FUAVs, and the transmit power of the FUAVs under the constraints of maximum interference, maximum UAV speed, UAV collision avoidance and the total flying periods. In the challenging scenarios, the original non-convex problem is addressed by iteratively optimizing three decoupled subproblems: sensing duration optimization, FUAVs trajectory optimization, and FUAVs power allocation optimization. Since there are highly non-convex objective function and constraints in the FUAV trajectory optimization subproblem, we convert it into a more tractable convex problem by utilizing difference-of-convex (DC) functions. Furthermore, a three dimensional (3D) spatial-temporal sensing scheme based on UAVs is introduced into cognitive UAV systems with multiple primary transmit-receive pairs (PTRs) to further improve spectrum efficiency. Simulation results show that the achievable throughput of the FUAVs with the proposed scheme rises by approximately 53% compared with the FUAV fixed trajectory flight scheme and by approximately 58% in comparison with the only timedimension spectrum sensing scheme.