Spatial and Wavelength Division Joint Multiplexing System Design for Visible Light Communications

The low-pass characteristics of front-end elements including light-emitting diodes (LEDs) and photodiodes (PDs) limit the transmission data rate of visible light communication (VLC) and Light Fidelity (LiFi) systems. Using multiplexing transmission techniques, such as spatial multiplexing (SMX) and wavelength division multiplexing (WDM), is a solution to overcome bandwidth limitation. However, spatial correlation in optical wireless channels and optical filter bandpass shifts typically limit the achievable multiplexing gain in SMX and WDM systems, respectively. In this paper, we consider a multiple-input multiple output (MIMO) joint multiplexing VLC system that exploits available degrees-offreedom (DoFs) across space, wavelength and frequency dimensions simultaneously. Instead of providing a new precoder/post-detector design, we investigate the considered joint multiplexing system from a system configuration perspective by tuning system parameters in both spatial and wavelength domains, such as LED positions and optical filter passband. We propose a novel spatial clustering with wavelength division (SCWD) strategy which enhances the MIMO channel condition. We propose to use a state-of-the-art black-box optimization tool: Bayesian adaptive direct search (BADS) to determine the desired system parameters, which can significantly improve the achievable rate. The extensive numerical results demonstrate the superiority of the proposed method over conventional SMX and WDM VLC systems.

Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks

Load balancing (LB) is a challenging issue in the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets), due to the nature of heterogeneous access points (APs). Machine learning has the potential to provide a complexity-friendly LB solution with near-optimal network performance, at the cost of a training process. The state-of-the-art (SOTA) learning-aided LB methods, however, need retraining when the network environment (especially the number of users) changes, significantly limiting its practicability. In this paper, a novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed, which conducts AP selection for one target user upon the condition of other users. Also, an adaptive mechanism is developed to map a smaller number of users to a larger number through splitting their data rate requirements, without affecting the AP selection result for the target user. This enables the proposed method to handle different numbers of users without the need for retraining. Results show that A-TCNN achieves a network throughput very close to that of the testing dataset, with a gap less than 3%. It is also proven that A-TCNN can obtain a network throughput comparable to two SOTA benchmarks, while reducing the runtime by up to three orders of magnitude.