Enhancements to the Deep Learning Signal Detection Model in Non-Orthogonal Multiple Access Receivers and Noisy Channels
Main Article Content
Keywords
NOMA, Deep Learning, LSTM, hyperparameter tuning, SIC
Abstract
This paper presents an enhanced deep learning-based Non-Orthogonal Multiple Access (NOMA) receiver that can mainly be used in low signal-to-noise channels. We show how a better dataset generation strategy for training Deep Learning (DL) could result in better generalization capabilities. Then, we apply hyperparameter tuning using exhaustive search to optimize the DL network. A Long-Short-Term-Memory (LSTM) DL architecture is used. The results show superior Symbol Error Rate vs Signal-to-Noise Ratio performance compared to the state-of-the-art methods such as Maximum Likelihood, Minimum Mean Square Error, and Successive Interface Cancellation, even though the network is only half as complex as previously proposed DL networks in the literature.
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References
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Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Hasan, M. K., Shahjalal, M., Islam, M. M., Alam, M. M., Ahmed, M. F., & Jang, Y. M. (2020). The role of deep learning in NOMA for 5G and beyond communications. Paper presented at the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).
Islam, S., Zeng, M., & Dobre, O. A. (2017). NOMA in 5G systems: Exciting possibilities for enhancing spectral efficiency. arXiv preprint arXiv:1706.08215. https://doi.org/10.48550/arXiv.1706.08215
Kang, J.-M., Kim, I.-M., & Chun, C.-J. (2019). Deep learning-based MIMO-NOMA with imperfect SIC decoding. IEEE Systems Journal, 14(3), 3414–3417. https://doi.org/10.1109/JSYST.2019.2937463
Khan, W. U., Liu, J., Jameel, F., Sharma, V., Jäntti, R., & Han, Z. (2020). Spectral efficiency optimization for next generation NOMA-enabled IoT networks. IEEE Transactions on Vehicular Technology, 69(12), 15284–15297. https://doi.org/10.1109/TVT.2020.3038387
Lin, C., Chang, Q., & Li, X. (2019). A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection. Sensors, 19(11), 2526. Retrieved from https://www.mdpi.com/1424-8220/19/11/2526
Liu, Y., Qin, Z., Elkashlan, M., Ding, Z., Nallanathan, A., & Hanzo, L. (2017). Non-orthogonal multiple access for 5G and beyond. Proceedings of the IEEE, 105(12), 2347–2381. https://doi.org/10.1109/JPROC.2017.2768666
Luo, J., Tang, J., So, D. K., Chen, G., Cumanan, K., & Chambers, J. A. (2019). A deep learning-based approach to power minimization in multi-carrier NOMA with SWIPT. IEEE Access, 7, 17450–17460. https://doi.org/10.1109/ACCESS.2019.2895201
Park, S., Truong, A. Q., & Nguyen, T. H. (2019). Power control for sum spectral efficiency optimization in MIMO-NOMA systems with linear beamforming. IEEE Access, 7, 10593–10605. https://doi.org/10.1109/ACCESS.2018.2890441
Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098. https://doi.org/10.48550/arXiv.1706.05098
Shin, W., Vaezi, M., Lee, J., & Poor, H. V. (2016). On the number of users served in MIMO-NOMA cellular networks. Paper presented at the 2016 International Symposium on Wireless Communication Systems (ISWCS).
Sun, Y., Wang, Y., Jiao, J., Wu, S., & Zhang, Q. (2019). Deep learning-based long-term power allocation scheme for NOMA downlink system in S-IoT. IEEE Access, 7, 86288–86296. https://doi.org/10.1109/ACCESS.2019.2926426
Thompson, J. (2019). Deep learning for signal detection in non-orthogonal multiple access wireless systems. Paper presented at the 2019 UK/China Emerging Technologies (UCET). https://doi.org/10.1109/UCET.2019.8881888
Ye, N., Li, X., Yu, H., Zhao, L., Liu, W., & Hou, X. (2020). DeepNOMA: A unified framework for NOMA using deep multi-task learning. IEEE Transactions on Wireless Communications, 19(4), 2208–2225. https://doi.org/10.1109/TWC.2019.2963185
Ye, Y., Hu, R. Q., Lu, G., & Shi, L. (2020). Enhance latency-constrained computation in MEC networks using uplink NOMA. IEEE Transactions on Communications, 68(4), 2409–2425. https://doi.org/10.1109/TCOMM.2020.2969666
Chen, X., Jia, R., & Ng, D. W. K. (2018). On the design of massive non-orthogonal multiple access with imperfect successive interference cancellation. IEEE Transactions on Communications, 67(3), 2539–2551. https://doi.org/10.1109/TCOMM.2018.2884476
Chin, W. H., Fan, Z., & Haines, R. (2014). Emerging technologies and research challenges for 5G wireless networks. IEEE Wireless Communications, 21(2), 106–112. https://doi.org/10.1109/MWC.2014.6812298
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Hasan, M. K., Shahjalal, M., Islam, M. M., Alam, M. M., Ahmed, M. F., & Jang, Y. M. (2020). The role of deep learning in NOMA for 5G and beyond communications. Paper presented at the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).
Islam, S., Zeng, M., & Dobre, O. A. (2017). NOMA in 5G systems: Exciting possibilities for enhancing spectral efficiency. arXiv preprint arXiv:1706.08215. https://doi.org/10.48550/arXiv.1706.08215
Kang, J.-M., Kim, I.-M., & Chun, C.-J. (2019). Deep learning-based MIMO-NOMA with imperfect SIC decoding. IEEE Systems Journal, 14(3), 3414–3417. https://doi.org/10.1109/JSYST.2019.2937463
Khan, W. U., Liu, J., Jameel, F., Sharma, V., Jäntti, R., & Han, Z. (2020). Spectral efficiency optimization for next generation NOMA-enabled IoT networks. IEEE Transactions on Vehicular Technology, 69(12), 15284–15297. https://doi.org/10.1109/TVT.2020.3038387
Lin, C., Chang, Q., & Li, X. (2019). A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection. Sensors, 19(11), 2526. Retrieved from https://www.mdpi.com/1424-8220/19/11/2526
Liu, Y., Qin, Z., Elkashlan, M., Ding, Z., Nallanathan, A., & Hanzo, L. (2017). Non-orthogonal multiple access for 5G and beyond. Proceedings of the IEEE, 105(12), 2347–2381. https://doi.org/10.1109/JPROC.2017.2768666
Luo, J., Tang, J., So, D. K., Chen, G., Cumanan, K., & Chambers, J. A. (2019). A deep learning-based approach to power minimization in multi-carrier NOMA with SWIPT. IEEE Access, 7, 17450–17460. https://doi.org/10.1109/ACCESS.2019.2895201
Park, S., Truong, A. Q., & Nguyen, T. H. (2019). Power control for sum spectral efficiency optimization in MIMO-NOMA systems with linear beamforming. IEEE Access, 7, 10593–10605. https://doi.org/10.1109/ACCESS.2018.2890441
Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098. https://doi.org/10.48550/arXiv.1706.05098
Shin, W., Vaezi, M., Lee, J., & Poor, H. V. (2016). On the number of users served in MIMO-NOMA cellular networks. Paper presented at the 2016 International Symposium on Wireless Communication Systems (ISWCS).
Sun, Y., Wang, Y., Jiao, J., Wu, S., & Zhang, Q. (2019). Deep learning-based long-term power allocation scheme for NOMA downlink system in S-IoT. IEEE Access, 7, 86288–86296. https://doi.org/10.1109/ACCESS.2019.2926426
Thompson, J. (2019). Deep learning for signal detection in non-orthogonal multiple access wireless systems. Paper presented at the 2019 UK/China Emerging Technologies (UCET). https://doi.org/10.1109/UCET.2019.8881888
Ye, N., Li, X., Yu, H., Zhao, L., Liu, W., & Hou, X. (2020). DeepNOMA: A unified framework for NOMA using deep multi-task learning. IEEE Transactions on Wireless Communications, 19(4), 2208–2225. https://doi.org/10.1109/TWC.2019.2963185
Ye, Y., Hu, R. Q., Lu, G., & Shi, L. (2020). Enhance latency-constrained computation in MEC networks using uplink NOMA. IEEE Transactions on Communications, 68(4), 2409–2425. https://doi.org/10.1109/TCOMM.2020.2969666
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Published
Mar 30, 2022
Article Details
How to Cite
Ali, A. H., Al-Musawi , R. S. H., & Al-Majdi, K. (2022). Enhancements to the Deep Learning Signal Detection Model in Non-Orthogonal Multiple Access Receivers and Noisy Channels. Journal of Telecommunications and the Digital Economy, 10(1), 92–100. https://doi.org/10.18080/jtde.v10n1.471
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Telecommunications
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