TY - JOUR
T1 - ViRSMALNet
T2 - A Twin-Tier LSTM-Based Deep Learning Network for Indoor MIMO RSMA VLC Systems
AU - Kowshik, Anagha K.
AU - Gurugopinath, Sanjeev
AU - Muhaidat, Sami
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Deep learning (DL)
KW - long short-term memory (LSTM)
KW - maximum likelihood (ML) detection
KW - multiple-input multiple-output (MIMO)
KW - rate-splitting multiple access (RSMA)
KW - visible light communications (VLC)
UR - http://www.scopus.com/inward/record.url?scp=85179046354&partnerID=8YFLogxK
U2 - 10.1109/OJCOMS.2023.3336772
DO - 10.1109/OJCOMS.2023.3336772
M3 - Article
AN - SCOPUS:85179046354
SN - 2644-125X
VL - 5
SP - 2735
EP - 2747
JO - IEEE Open Journal of the Communications Society
JF - IEEE Open Journal of the Communications Society
ER -