Deep Recurrent Neural Network Based Detector for OFDM With Index Modulation

Abdul Aziz, Md; Rahman, Md Habibur; Abrar Shakil Sejan, Mohammad; Tabassum, Rana; Hwang, Duck-Dong; Song, Hyoung-Kyu

doi:10.1109/access.2024.3419150

IEEE Access

2024

DOI: 10.1109/access.2024.3419150

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Deep Recurrent Neural Network Based Detector for OFDM With Index Modulation

Md Abdul Aziz,

Md Habibur Rahman,

Mohammad Abrar Shakil Sejan

et al.

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2024

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Cited by 3 publications

References 45 publications

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Bidirectional Deep Learning Decoder for Polar Codes in Flat Fading Channels

Aziz,

Rahman,

Abrar Shakil Sejan

et al. 2024

IEEE Access

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Bidirectional Deep Learning Decoder for Polar Codes in Flat Fading Channels

Aziz,

Rahman,

Abrar Shakil Sejan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

A Hybrid Deep Learning Framework for OFDM with Index Modulation Under Uncertain Channel Conditions

Aziz,

Rahman,

Tabassum

et al. 2024

Mathematics

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Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically adjusting index assignments. Deep learning (DL) offers a potential solution by improving detection performance and resilience through the learning of intricate patterns in varying channel conditions. In this paper, we introduce a robust detection method based on a hybrid DL (HDL) model designed specifically for orthogonal frequency-division multiplexing with IM (OFDM-IM) in challenging channel environments. Our proposed HDL detector leverages a one-dimensional convolutional neural network (1D-CNN) for feature extraction, followed by a bidirectional long short-term memory (Bi-LSTM) network to capture temporal dependencies. Before feeding data into the network, the channel matrix and received signals are preprocessed using domain-specific knowledge. We evaluate the bit error rate (BER) performance of the proposed model using different optimizers and equalizers, then compare it with other models. Moreover, we evaluate the throughput and spectral efficiency across varying SNR levels. Simulation results demonstrate that the proposed hybrid detector surpasses traditional and other DL-based detectors in terms of performance, underscoring its effectiveness for OFDM-IM under uncertain channel conditions.

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Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems

Aziz,

Rahman,

Tabassum

et al. 2024

Mathematics

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In a multi-carrier (MC) system, the transmitted data are split across several sub-carriers as a crucial approach for achieving high data rates, reliability, and spectral efficiency. Deep learning (DL) enhances MC systems by improving signal representation, leading to more efficient data transmission and reduced bit error rates. In this paper, we propose an MC system supported by DL for operation on fading channels. Deep neural networks are utilized to model the modulation block, while a gated recurrent unit (GRU) network is used to model the demodulation blocks, acting as the encoder and decoder within an autoencoder (AE) architecture. The proposed scheme, known as MC-AE, differs from existing AE-based systems by directly processing channel state information and the received signal in a fully data-driven way, unlike traditional methods that rely on channel equalizers. This approach enables MC-AE to improve diversity and coding gains in fading channels by simultaneously optimizing the encoder and decoder. In this experiment, we evaluated the performance of the proposed model under both perfect and imperfect channel conditions and compared it with other models. Additionally, we assessed the performance of the MC-AE system against index modulation-based MC systems. The results demonstrate that the GRU-based MC-AE system outperforms the others.

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Deep Recurrent Neural Network Based Detector for OFDM With Index Modulation

Cited by 3 publications

References 45 publications

Bidirectional Deep Learning Decoder for Polar Codes in Flat Fading Channels

Bidirectional Deep Learning Decoder for Polar Codes in Flat Fading Channels

A Hybrid Deep Learning Framework for OFDM with Index Modulation Under Uncertain Channel Conditions

Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems

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