MIMO-NOMA Systems Channel Performance Using SCMA-Deep Learning Method
Keywords:
NOMA, Multiple Input Multiple Output (MIMO), Deep Neural Network (DNN), SCMA.Abstract
The present research initiatives are based on improved multiple access methodologies with a focus on future wireless communication technologies. A skilled competitor, Non-Orthogonal Multiple Access (NOMA), can be utilized to construct the next generation of wireless communications. When compared to other orthogonal resources, NOMA's main strength is its ability to handle many users. The major NOMA detection method used at receivers for downlink NOMA transmissions is Successive Interference Cancellation (SIC). The receiver complexity and concerns about error propagation are the key limitations of SIC. Deep Learning (DL) is used for downlink NOMA transmission, which is decoded using a Sparse Code Multiple Access (SCMA) decoder. SCMA is used in conjunction with DL to forecast the channel and decode it at the receiver. Two users are provided equal access to resources, notably power, based on their proximity to the base station (BS). With SCMA decoding at the receiver, simulations for AWGN, Rayleigh, and Rician channels were carried out while various constraints were taken into account. SCMA-DL surpasses the MMSE and SIC detection methods in terms of Bit Error Rate (BER) during the decoding phase.
Downloads
References
Mathur, H., & Deepa, T. (2021). "A survey on advanced multiple access techniques for 5G and beyond wireless communications, Wireless Personal Communications," 118, 1775–1792.
Lin, C., Chang, Q., & Li, X. (2019). "A deep learning approach for MIMO-NOMA downlink signal detection. Sensors," 19(11), 2526.
Z. Ding, F. Adachi, and H. V. Poor, "Performance of MIMO-NOMA Downlink Transmissions", 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA, 2015, pp. 1–6.
Wei, C. P., Yang, H., Li, C. P., & Chen, Y. M. (2020). "SCMA decoding via deep learning," IEEE Wireless Communications Letters, 10(4), 878–881.
Yang, M., Li, B., Bai, Z., Zuo, X., Yan, Z., & Liang, Y. (2018). "Semi-granted sparse code multiple access (SCMA) for 5G networks". In IoT as a Service: Third International Conference, IoTaaS 2017, Taichung, Taiwan, September 20–22, 2017, Proceedings 3 (pp. 381-388), Springer International Publishing
Sanjana, T., and Suma, M.N. (2021). "Deep Learning Approaches Used in Downlink MIMO-NOMA System": A Survey", Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol. 1325
Nallanathan, A., & Hanzo, L. (2017). "Non-orthogonal multiple access for 5G and beyond". Proceedings of the IEEE, 105 (12), 2347–2381.
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.
Muhammad Hussain and Haroon Rasheed, "Nonorthogonal Multiple Access for Next- Generation Mobile Networks: A Technical Aspect for Research Direction," Wireless Communication and Mobile Computing, vol. 2020,
Su, X., Yu, H., Kim, W., Choi, C., & Choi, D. (2016). "Interference cancellation for non-orthogonal multiple access used in future wireless mobile networks". EURASIP Journal on Wireless Communications and Networking, 2016, 1–12.
T. Manglayev, R. C. Kizilirmak, Y. H. Kho, N. Bazhayev, and I. Lebedev, "NOMA with imperfect SIC implementation," IEEE EUROCON 2017, 17th International Conference on Smart Technologies, Ohrid, Macedonia, 2017, pp. 22–25.
Emir, A., Kara, F., Kaya, H., & Li, X. (2021). "Deep learning-based flexible joint channel estimation and signal detection of multi-user OFDM-NOMA": Physical Communication, 48, 101443.
C. Ezhilazhagan and M. Ramakrishnan, "An efficient relay selection method based on ANN channel estimation technique for Amplify and Forward relay in Cooperative Networks", Dynamic Systems and Applications, vol. 30, no. 10, pp. 1622–1639, Oct. 2021. https://doi.org/10.46719/dsa202130.10.06
Thompson, J. (2019, August). "Deep learning for signal detection in non- orthogonal multiple access wireless systems": In 2019 UK/China Emerging Technologies (UCET), pp. 1–4.
Kanzariya, D., Trada, P., Kaswala, H., & Shah, H. B. (2022). "Deep Learning Based Signal Detection and Channel Estimation for MIMO-NOMA System": Science, 1(01), 23–35.
M. Rebhi, K. Hassan, K. Raoof, and P. Chargé, "Sparse Code Multiple Access: Potentials and Challenges," in IEEE Open Journal of the Communications Society, vol. 2, pp. 1205–1238, 2021.
C. Ezhilazhagan, K. L. Prabha, and K. Sakthisudhan, "Multiple Timing Offset and Channel Parameters Estimation for Decode and Forward Relay Cooperative Network," 2022, 6th International Conference on Devices, Circuits, and Systems (ICDCS), 2022, pp. 43–47, Doi: 10.1109/ICDCS54290.2022.9780699.
Pandya, S., Wakchaure, M. A., Shankar, R., & Annam, J. R. (2022). "Analysis of the NOMA-OFDM 5G wireless system using a deep neural network": The Journal of Defence Modelling and Simulation, 19(4), 799–806.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
