In this paper, we investigate the Access Point (AP) selection problem in Cell-Free Massive multiple-input multiple-output (MIMO) system. Firstly, we add a connecting coefficient to the uplink data transmission model. Then, the problem of AP selection is formulated as a discrete combinatorial optimization problem which can be dealt with by the particle swarm algorithm. However, when the number of optimization variables is large, the search efficiency of the traditional particle swarm algorithm will be significantly reduced. Then, we propose an ‘user-centric’ cooperative coevolution scheme which includes the proposed probability-based particle evolution strategy and random-sampling-based particle evaluation mechanism to deal with the search efficiency problem. Simulation results show that proposed algorithm has better performance than other existing algorithms.

Thanks to the development of the 6th generation mobile network that makes it possible for us to move towards an intelligent ubiquitous information society, among which some novel technologies represented by cell-free network has also attracted widespread academic attention. Cell-free network has brought distinguished gains to the network capacity with its strong ability against inter-cell interference. Unfortunately, further improvement demands more base stations (BSs) to be settled, which incurs steep cost increase. To address this issue, reconfigurable intelligent surface (RIS) with low cost and power consumption is introduced in this paper to replace some of the trivial BSs in the system, then, a RIS-aided cell-free network paradigm is formulated. Our objective is to solve the weighted sum-rate (WSR) maximization problem by jointly optimizing the beamforming design at BSs and the phase shift of RISs. Due to the non-convexity of the formulated problem, this paper investigates a joint optimizing scheme based on block coordinate descent (BCD) method. Subsequently, on account of the majority of the precious work reposed perfect channel state information (CSI) setup for the ultimate performance, this paper also extends the proposed algorithm to the case wherein CSI is imperfect by utilizing successive convex approximation (SCA). Finally, simulation results demonstrate that the proposed scheme shows great performance and robustness in perfect CSI scenario as well as the imperfect ones.

Effective communication strategies with a properly designed source precoding matrix (PM) and a properly designed relay beamforming matrix (BM) can significantly improve the spectral efficiency of multiple-input multiple-output (MIMO) relaying broadcast channels (RBCs). In the present paper, we first propose a general communication scheme with non-regenerative relay that can overcome the half-duplex relay constraint of the general MIMO-RBC. Based on the proposed scheme, the robust source PM and relay BM are designed for imperfect channel state information at the transmitter (CSIT). In contrast to the conventional non-regenerative relaying communication scheme for the MIMO-RBC, in the proposed scheme, the source can send information continuously to the relay and users during two phases. Furthermore, in conjunction with the advanced precoding strategy, the proposed scheme can achieve a full-degree-of-freedom (DoF) MIMO-RBC with that each entry in the related channel matrix is considered to an i.i.d. complex Gaussian variable. The robust source PM and relay BM designs were investigated based on both throughput and fairness criteria with imperfect CSIT. However, solving the problems associated with throughput and fairness criteria for the robust source PM and relay BM designs is computationally intractable because these criteria are non-linear and non-convex. In order to address these difficulties, we first set up equivalent optimization problems based on a tight lower bound of the achievable rate. We then decompose the equivalent throughput problem into several decoupled subproblems with tractable solutions. Finally, we obtain the suboptimal solution for the throughput problem by an alternating optimization approach. We solve the fairness problem by introducing an adjusted algorithm according to the throughput problem. Finally, we demonstrate that, in both cases of throughput and fairness criteria, the proposed relaying communication scheme with precoding algorithms outperforms existing methods.