Capacity Enhancement in Irregular RIS-Assisted Wireless Networks Using Standard and Improved Particle Swarm Optimization

Reconfigurable Intelligent Surface (RIS) enhances the performance of wireless networks by smartly reconfiguring the wireless propagation environment. This enhancement is highly dependent on the number of RIS elements, but practical challenges like high channel acquisition overhead and power consumption limit the scalability of traditional RIS systems. Recently proposed irregular RIS has been shown to tackle these challenges effectively. However, its potential advantages are not fully realized since the topology and precoding designs are alternatively optimized, which does not consider the impact of topology changes on precoding. In this paper, we leverage the benefits of irregular RIS and propose new approaches to exploit its potential fully. The key idea is to enhance the system capacity by enabling simultaneous optimization of the irregular RIS topology and precoding design. Specifically, we propose the standard particle swarm optimization (PSO) algorithm to jointly optimize the RIS topology, beamforming, and reflection coefficients. Unlike the conventional approach, PSO simultaneously evaluates potential solutions via particle updates that enhance the system capacity significantly. Next, we introduce an improved variant of PSO, designed to improve the exploration-exploitation tradeoff and speed up the convergence. Specifically, the improved PSO approach enables the dynamic adjustments of PSO parameters throughout the process, reducing the risk of premature convergence and significantly improving the overall performance compared to the standard PSO. The effectiveness of the proposed improved PSO is validated through a complex multi-modal Michalewicz function benchmark test, which illustrates that the obtained results are virtually the same as the expected results for different dimensional spaces. Besides, we also provide convergence and complexity analysis of the improved PSO. Numerical results and analysis demonstrate that the proposed algorithms significantly enhance the system capacity with low complexity by simultaneously optimizing the topology and precoding design.