In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching for an optimal LED for each UE. Due to the non-convex and non-continuous nature of this security maximization problem, we propose an LED selection algorithm based on tabu search to avoid getting trapped in local optima and expedite the search process by managing trial vectors from previous iterations. Moreover, we introduce three LED selection strategies with a low computational complexity. The simulation results demonstrate that the proposed algorithm achieves a secrecy performance very close to the global optimal value, with a gap of less than 1%. Additionally, the proposed strategies exhibit a performance gap of 28% compared to the global optimal.

In this letter, the scalable cache-aided cell-free (SCCF) massive multiple input multiple output systems are proposed to reduce the downlink transmit energy consumption (EC) via fronthaul and backhaul links. On the premise of ensuring the scalability of cell-free (CF) systems, we establish the total EC (TEC) models of SCCF systems with caching on both access points and the central processing unit. Through the proposed successive convex approximation algorithms, we obtain the near-optimal cache placements for the TEC minimization problems in various forms resulting from different caching strategies (CSs). Simulation results demonstrate that the proposed SCCF systems can significantly reduce the TEC of scalable CF systems while maintaining scalability, especially when utilizing the CSs based on proposed transmit caching scheme.

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced.