We propose an alternating optimization (AO) approach to enhance the performance of energy-efficient multi-user visible light communication (MU-VLC) systems, which integrates both lighting and communication functions using multi-input multi-output (MIMO) technology and sub-array architecture. The rate-splitting multiple access (RSMA) technique is used to fully exploit the VLC channel as well as flexibly handle the interference and noise. Numerical results exhibit the superiority of the proposed scheme over the minimum mean squared error (MMSE) and successive interference cancellation (SIC) methods under various user quality of service (QoS) requirements. Moreover, the energy efficiency (EE) achieved by the proposed scheme surpasses that of existing VLC MIMO systems, highlighting its potential for green communication applications.

This paper proposes a novel downlink precoding method for a cell-free massive multiple-input multiple-output (CF-mMIMO) network, requiring no channel state information sharing between the access points via fronthaul links. By drawing analogies between a CF-mMIMO network and an artificial neural network, the proposed algorithm borrows the idea of backpropagation to train the precoders and the combiners through over-the-air ping-pong signaling between the access points and user equipments. It utilizes manifolds optimization to meet the per-AP power constraint and is named as distributed quasi-neural network precoding on manifold (DQNPM). The DQNPM algorithm can accommodate a large category of objective functions for fully distributed implementation. Numerical simulations show that our method outperforms the state-of-the-art approaches, and is robust against pilot contamination.

Paving the way toward next-generation communications, reconfigurable intelligent surface (RIS) based free-space optical (FSO) links have emerged as a strong candidate in the unlicensed spectrum for enhanced capacity and signal quality. However, existing modulation schemes constrain the FSO links’ performance and are unsuitable for RIS-induced multipath fading. To address the issue, we firstly introduce the optical orthogonal time frequency space (O-OTFS) modulation scheme into such links, with orbital angular momentum (OAM) optical beams combined to enhance the performance. Moreover, we propose two combination systems with separate and integrated OAM-OTFS respectively, and further propose a novel quad-mode (QM)-OAM-OTFS scheme within the integrated system, demonstrating significant performance enhancement over counterparts. Numerical results indicate that the separate OAM-OTFS system enhances performance in terms of power and spectral efficiencies but reduces system robustness to turbulence with increasing OAM modes, the proposed QM-OAM-OTFS scheme effectively mitigates atmospheric turbulence effects and achieves efficient power and spectral utilization.

Cell-free massive multiple-input multiple-output (MIMO) can resolve the inter-cell interference issue in cellular networks through cooperative beamforming of the distributed access points (APs). This paper focuses on an uplink cell-free massive MIMO network and investigates novel methods to train the central processing unit (CPU), the APs, and the users in the network. To reduce the communication burden posed on the fronthaul, each AP applies receive beamforming to compress the vector signals into scalar ones before passing them to the CPU for centralized processing. By drawing analogies between an uplink cell-free network and a quasi-neural network and borrowing the idea of backpropagation algorithm, we propose a novel scheme named the distributed learning for uplink cell-free massive MIMO beamforming (DLCB), which can achieve the multi-AP cooperation without explicit estimation of their channel state information (CSI). The DLCB has low computational complexity and is applicable to various objective functions, such as the minimum mean squared error criterion and the maximum sum rate criterion. Extensive simulations verify that the proposed scheme achieves superior performance over the state-of-the-art methods.