Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System
The “Non-Orthogonal Multiple Access (NOMA)” strategies has been recently identified as a successful way to increase spectrum effectiveness and reliability of the system. NOMA enables numerous users to share identical blocks of resources with varying power distribution factors....
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2024-01-01
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| author | Vijayakumar Kondepogu Budhaditya Bhattacharyya |
| author_facet | Vijayakumar Kondepogu Budhaditya Bhattacharyya |
| author_sort | Vijayakumar Kondepogu |
| collection | DOAJ |
| description | The “Non-Orthogonal Multiple Access (NOMA)” strategies has been recently identified as a successful way to increase spectrum effectiveness and reliability of the system. NOMA enables numerous users to share identical blocks of resources with varying power distribution factors. Thus, everyone can utilize equal resource structures, resulting in improved spectral effectiveness. “Orthogonal Frequency-Division Multiplexing (OFDM)” represents a well-known multi-carrier broadcast technique that provides a high data prevalence, excellent spectral effectiveness, and resilience against network band selection for present and future wideband wireless connections. However, channel estimation remains one of the most important challenges in OFDM based communication. Pilot-aided estimation of channels is less difficult and performs better than blind channel estimation; therefore, it is commonly employed in real-world systems. However, inadequate Successive Interference Cancellation (SIC) may have an impact on NOMA effectiveness. To help with channel estimates and signal identification in NOMA structures, deep learning approaches were developed. In this paper, we introduce a new approach called Adaptive Dilated Convolutional Neural Networks with Gated Recurrent Unit Layer (ADCNN-GRU). Initially, the required signals are collected from the standard data resource. The input signal is then subjected to the developed ADCNN-GRU approach, which is the combination of the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) with an additional dilated layer. Here the input signals are extracted and infer the signal at the receiver terminal. The loss functions in the model are optimized by using the Improved Pelican Optimization Algorithm (IPOA). The performance of the developed approach is determined by conducting the simulation experiment. The result showed that the developed approach outperformed than traditional models. |
| format | Article |
| id | doaj-art-43566fd46b334126953557e54b108c2b |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-43566fd46b334126953557e54b108c2b2025-08-20T02:50:30ZengIEEEIEEE Access2169-35362024-01-011218445618447610.1109/ACCESS.2024.348786610737349Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM SystemVijayakumar Kondepogu0Budhaditya Bhattacharyya1https://orcid.org/0000-0001-6507-763XSchool of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, IndiaSchool of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, IndiaThe “Non-Orthogonal Multiple Access (NOMA)” strategies has been recently identified as a successful way to increase spectrum effectiveness and reliability of the system. NOMA enables numerous users to share identical blocks of resources with varying power distribution factors. Thus, everyone can utilize equal resource structures, resulting in improved spectral effectiveness. “Orthogonal Frequency-Division Multiplexing (OFDM)” represents a well-known multi-carrier broadcast technique that provides a high data prevalence, excellent spectral effectiveness, and resilience against network band selection for present and future wideband wireless connections. However, channel estimation remains one of the most important challenges in OFDM based communication. Pilot-aided estimation of channels is less difficult and performs better than blind channel estimation; therefore, it is commonly employed in real-world systems. However, inadequate Successive Interference Cancellation (SIC) may have an impact on NOMA effectiveness. To help with channel estimates and signal identification in NOMA structures, deep learning approaches were developed. In this paper, we introduce a new approach called Adaptive Dilated Convolutional Neural Networks with Gated Recurrent Unit Layer (ADCNN-GRU). Initially, the required signals are collected from the standard data resource. The input signal is then subjected to the developed ADCNN-GRU approach, which is the combination of the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) with an additional dilated layer. Here the input signals are extracted and infer the signal at the receiver terminal. The loss functions in the model are optimized by using the Improved Pelican Optimization Algorithm (IPOA). The performance of the developed approach is determined by conducting the simulation experiment. The result showed that the developed approach outperformed than traditional models.https://ieeexplore.ieee.org/document/10737349/Channel estimationnon-orthogonal multiple accessorthogonal frequency-division multiplexingadaptive dilated convolutional neural network with gated recurrent unitimproved pelican optimization algorithm |
| spellingShingle | Vijayakumar Kondepogu Budhaditya Bhattacharyya Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System IEEE Access Channel estimation non-orthogonal multiple access orthogonal frequency-division multiplexing adaptive dilated convolutional neural network with gated recurrent unit improved pelican optimization algorithm |
| title | Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System |
| title_full | Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System |
| title_fullStr | Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System |
| title_full_unstemmed | Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System |
| title_short | Heuristic Optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System |
| title_sort | heuristic optimization assisted dilated convolution neural network with gated recurrent unit for channel estimation in noma ofdm system |
| topic | Channel estimation non-orthogonal multiple access orthogonal frequency-division multiplexing adaptive dilated convolutional neural network with gated recurrent unit improved pelican optimization algorithm |
| url | https://ieeexplore.ieee.org/document/10737349/ |
| work_keys_str_mv | AT vijayakumarkondepogu heuristicoptimizationassisteddilatedconvolutionneuralnetworkwithgatedrecurrentunitforchannelestimationinnomaofdmsystem AT budhadityabhattacharyya heuristicoptimizationassisteddilatedconvolutionneuralnetworkwithgatedrecurrentunitforchannelestimationinnomaofdmsystem |