Examination of the Non-Orthogonal Multiple Access System Using Long Short Memory Based Deep Neural Network
Keywords:NOMA, recurrent neural network, QPSK, RNN, deep neural network
This paper investigates deep learning (DL) non-orthogonal multiple access (NOMA) receivers based on long short-term memory (LSTM) under Rayleigh fading channel circumstances. The performance comparison between the DL NOMA detector and the traditional NOMA method is established, and results have shown that the DL-based NOMA detector performance is far better in comparison with conventional NOMA detectors. Simulation curves are compared with the performance of the DL detector in terms of minimum mean square estimate (MMSE) and least square error (LSE) estimate, taking all realistic circumstances, except the cyclic prefix (CP), and clipping distortion into account. The simulation curves demonstrate that the performance of the DL-based detector is exceptionally good when it equals 1 when the noise signal ratio (SNR) is more than 15 dB, assuming that the DL method is more resilient to clipping distortion.
Pandya S, Wakchaure MA, Shankar R, Annam JR. “Analysis of NOMA-OFDM 5G wireless system using deep neural network”, The Journal of Defense Modeling and Simulation, March 2021. doi:10.1177/ 1548512921999108
Bhardwaj L, Mishra RK, Shankar R. “Sum rate capacity of non-orthogonal multiple access scheme with optimal power allocation”, The Journal of Defense Modeling and Simulation. January 2021. doi:10. 1177/1548512920983531
Chaudhary BP, Shankar R, Mishra RK. “A tutorial on cooperative non-orthogonal multiple access networks”, The Journal of Defense Modeling and Simulation. February 2021. doi:10.1177/1548512920986627
Ravi Shankar, Ritesh Kumar Mishra, “An investigation of S-DF cooperative communication protocol over keyhole fading channel”, Physical Communication, vol. 29, pp. 120–140, 2018. https://doi.org/10.1016/j.phycom.2018.04.027.
Shankar R. “Examination of a non-orthogonal multiple access scheme for next generation wireless networks.” The Journal of Defense Modeling and Simulation. September 2020. doi:10.1177/1548512920951277
Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara, “Recurrent Neural Networks for Time Series Forecasting: Current status and future directions”, International Journal of Forecasting, vol. 37, pp. 388–427, 2021. https://doi.org/10.1016/j.ijforecast.2020.06.008
Dey, Prasanjit, Chandan Kumar, Mitrabarun Mitra, Richa Mishra, S. K. Chaulya, G. M. Prasad, S. K. Mandal, and G. Banerjee. “Deep convolutional neural network based secure wireless voice communication for underground mines.” Journal of Ambient Intelligence and Humanized Computing (2021): 1–20.
Elwekeil M, Jiang S, Wang T, Zhang S. “Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems,” in IEEE Wireless Communications Letters, vol. 8, no. 3, pp. 665–668, June 2019, doi: 10.1109/LWC.2018.2881978.
Nyländen T, Janhunen J, Silvén O, Juntti M. “A GPU implementation for two MIMO-OFDM detectors,” 2010 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, 2010, pp. 293–300, doi: 10.1109/ICSAMOS.2010.5642054.
Wang S, Yao R, Tsiftsis TA, Miridakis NI, Qi N. “Signal Detection in Uplink Time-Varying OFDM Systems Using RNN With Bidirectional LSTM,” in IEEE Wireless Communications Letters, vol. 9, no. 11, pp. 1947–1951, Nov. 2020, doi: 10.1109/LWC.2020.3009170.
Kojima S, Maruta K, Ahn C. “Adaptive Modulation and Coding Using Neural Network Based SNR Estimation,” in IEEE Access, vol. 7, pp. 183545–183553, 2019, doi: 10.1109/ACCESS.2019.2946973.
Chikha HB, Almadhor A, Khalid W. “Machine Learning for 5G MIMO Modulation Detection.” Sensors (Basel). 2021;21(5):1556. Published 2021 Feb. 24. doi:10.3390/s21051556
Sai-Chandra-Kumari Kalla, Christian Gagné, Ming Zeng, and Leslie A. Rusch, “Recurrent neural networks achieving MLSE performance for optical channel equalization,” Opt. Express 29, 13033–13047 (2021).
Zhu X, Sheng Z, Fang Y, et al. “A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications.” J. Wireless Com. Network 2020, 94 (2020). https://doi.org/10.1186/s13638-020-01714-4
Ashish IK, Mishra RK. “Performance Analysis For Wireless Non-Orthogonal Multiple Access Downlink Systems,” 2020 International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), pp. 1–6, 2020. doi: 10.1109/ICEFEET49149.2020.9186987.
Singh S, Mitra D, Baghel RK. “Analysis of NOMA for Future Cellular Communication,” 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 389–395, 2019. doi: 10.1109/ICOEI.2019.8862527.
Erpek T, O’Shea TJ, Sagduyu YE, Shi Y, Charles Clancy T. “Deep learning for wireless communications”, Development and Analysis of Deep Learning Architectures, pp. 223–266. Springer, Cham, 2020.
Shoeibi, Afshin et al. “Epileptic Seizures Detection Using Deep Learning Techniques: A Review.” International Journal of Environmental Research and Public Health vol. 18,11, 5780. 27 May 2021, doi:10.3390/ijerph18115780
Zappone A, Di Renzo M, Debbah M. “Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?,” IEEE Transactions on Communications, vol. 67, no. 10, pp. 7331–7376, Oct. 2019. doi: 10.1109/TCOMM.2019.2924010.
Thota S, Kamatham Y, Paidimarry CS. “Analysis of Hybrid PAPR Reduction Methods of OFDM Signal for HPA Models in Wireless Communications,” IEEE Access, vol. 8, pp. 22780–22791, 2020, doi: 10.1109/ACCESS.2020.2970022.
Khansa AA, Yin Y, Gui G, Sari H. “Power-Domain NOMA or NOMA-2000?,” 2019 25th Asia-Pacific Conference on Communications, pp. 336–341, 2019. doi: 10.1109/APCC47188.2019.9026468.
Simon MK, Alouini M. “Digital Communications Over Fading Channels (M.K. Simon and M.S. Alouini; 2005) [Book Review],” IEEE Transactions on Information Theory, vol. 54, no. 7, pp. 3369–3370, July 2008. doi: 10.1109/TIT.2008.924676.