ViRSMALNet: A Twin-Tier LSTM-Based Deep Learning Network for Indoor MIMO RSMA VLC Systems

Abstract

In this article, we present a twin-tiered deep learning (DL) symbol detector architecture for a multi-user multiple-input multiple-output (MIMO) visible light communication (VLC) system, based on rate-splitting multiple access (RSMA). The DL architecture exploits long short-term memory (LSTM), by utilizing two identical LSTM-based deep neural networks (DNNs) to concurrently decode the common and private streams, within a multicarrier framework. Termed as VLC with RSMA and LSTM-based DNN (ViRSMALNet), this detector overcomes the limitations in successive interference cancellation (SIC) receivers, and decodes the RSMA messages from the combined signal in a single step. Extensive simulations based on Monte Carlo methods are executed to assess the symbol error rate (SER) performance of ViRSMALNet across diverse modulation schemes. The results establish that ViRSMALNet outperforms SIC-based least squares (LS) and minimum mean square error (MMSE) detectors, and closely attains the performance of the optimal maximum likelihood (ML) detector. Further, this study also investigates the influence of factors such as the number of light-emitting diodes per fixture, the number of photodetectors per user, and the DNNs’ hyperparameters on the SER performance. Moreover, the impact of the distance between the PDs on the system’s sum rate performance is studied.

Keywords

• Deep learning (DL)
• long short-term memory (LSTM)
• maximum likelihood (ML) detection
• multiple-input multiple-output (MIMO)
• rate-splitting multiple access (RSMA)
• visible light communications (VLC)