Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Kowshik, Anagha K.; Raghavendra, Ashwini H.; Gurugopinath, Sanjeev; Muhaidat, Sami

doi:10.1109/ojvt.2023.3238034

Cited by 4 publications

(2 citation statements)

References 98 publications

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“…In [17], the LSTM deep learning (DL) scheme is implemented to detect the signal in a multiple-access multi-carrier modulation scenario with generalized Gaussian noise and fading. Signal detection in the uplink and downlink modes is realized without the use of the Successive Interference Cancellation Unit.…”

Section: ░ 2 Previous Workmentioning

confidence: 99%

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

M.N.,

R. Kounte,

Ravindra

2024

IJEER

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In communication systems, deep learning techniques can provide better predictions than model-based methods when the hidden features of the problem are prone to deviating substantially from the formulated assumptions. Severe signal impairments due to multipath fading and higher channel noise levels degrade the performance of conventional receivers. To overcome this, a novel intelligent receiver based on a deep learning network is presented, achieving better performance in terms of reduced bit error rate than a standalone conventional receiver. The experimental result shows that the relative decrement in the symbol error ratio due to the proposed method is about 9 percent compared to the traditional receiver when the Rician channel fading is relatively high.

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Section: ░ 2 Previous Workmentioning

confidence: 99%

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

M.N.,

R. Kounte,

Ravindra

2024

IJEER

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“…Numerous studies have investigated the potential of DL for signal detection in RF systems [30]- [35], [36], [37], and channel estimation [38], [39]. However, there is limited literature available on the design of LSTM-based RSMA receivers in an RF setup [23], [40]. Now, focusing on VLC, research efforts in the area of signal detection using DL techniques is quite limited.…”

Section: Motivationmentioning

confidence: 99%

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

Kowshik,

Gurugopinath,

Muhaidat

2024

IEEE Open J. Commun. Soc.

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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.INDEX TERMS 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).

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Joint Terminal-UAV Association and Power Allocation for Hybrid Satellite-UAV Relay Network With Rate Splitting Multiple Access

Wang,

Chen,

2024

IEEE Access

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Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Cited by 4 publications

References 98 publications

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

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

Joint Terminal-UAV Association and Power Allocation for Hybrid Satellite-UAV Relay Network With Rate Splitting Multiple Access

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