Wireless Communication Ecosystem Research Unit, Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, Thailand.
Optical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan 316021, China.
Sensors (Basel). 2023 Jan 12;23(2):903. doi: 10.3390/s23020903.
Cell-free (CF) networks are proposed to suppress the interference among collocated cells by deploying several BSs without cell boundaries. Nevertheless, as installing several base stations (BSs) may require high power consumption, cooperative CF networks integrated with a reconfigurable intelligent surface (RIS)/metasurface can avoid this problem. In such cooperative RIS-aided MIMO networks, efficient beamforming schemes are essential to boost their spectral and energy efficiency. However, most of the existing available beamforming schemes to maximize spectral and energy efficiency are complex and entail high complexity due to the matrix inversions. To this end, in this work we present a computationally efficient stochastic optimization-based particle swarm optimization (PSO) algorithm to amplify the spectral efficiency of the cooperative RIS-aided CF MIMO system. In the proposed PSO algorithm, several swarms are generated, while the direction of each swarm is tuned in each iteration based on the sum-rate performance to obtain the best solution. Our simulation results show that our proposed scheme can approximate the performance of the existing solutions for both the performance metrics, i.e., spectral and energy efficiency, at a very low complexity.
无蜂窝 (CF) 网络通过部署几个没有小区边界的基站来抑制同频小区之间的干扰。然而，由于安装多个基站 (BS) 可能需要高功耗，因此可以与可重构智能表面 (RIS)/超表面集成的协作 CF 网络可以避免这个问题。在这种协作的 RIS 辅助 MIMO 网络中，有效的波束赋形方案对于提高其频谱和能量效率至关重要。然而，大多数现有的最大化频谱和能量效率的波束赋形方案由于矩阵求逆而非常复杂，并且需要很高的复杂度。为此，在这项工作中，我们提出了一种基于随机优化的粒子群优化 (PSO) 算法，以提高协作 RIS 辅助 CF MIMO 系统的频谱效率。在提出的 PSO 算法中，生成了多个群体，而在每次迭代中，根据和速率性能调整每个群体的方向，以获得最佳解决方案。我们的仿真结果表明，我们提出的方案可以在非常低的复杂度下接近现有解决方案在这两个性能指标（即频谱效率和能量效率）上的性能。
