Efficient Precoding Strategies to Mitigate PAPR in Massive MIMO with Reduced Computational Load
Main Article Content
Abstract
Massive Multiple-Input Multiple-Output (MIMO) technology is a key enabler for next-generation wireless communication systems, offering significant gains in capacity and spectral efficiency. However, one of the major challenges in massive MIMO is the high Peak-to-Average Power Ratio (PAPR) of transmitted signals, which reduces the efficiency of power amplifiers and causes nonlinear distortion. This paper investigates efficient precoding strategies aimed at mitigating PAPR while maintaining low computational complexity, making them suitable for practical massive MIMO deployments. The proposed methods leverage advanced signal processing and optimization techniques to achieve a balance between reducing PAPR and preserving system performance metrics such as throughput and bit error rate. We analyze various low-complexity precoding algorithms, including optimization-based, iterative, and heuristic approaches, comparing their effectiveness in reducing PAPR and their computational demands. Simulation results demonstrate that these efficient precoding strategies substantially lower PAPR levels without compromising communication quality, enabling power amplifiers to operate closer to their optimal efficiency region. The reduced computational load of the proposed methods also facilitates real-time implementation in large-scale antenna systems. This study provides valuable insights into designing energy-efficient and robust massive MIMO systems by integrating PAPR-aware precoding techniques with practical complexity constraints, paving the way for enhanced performance in 5G and beyond wireless networks.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Cha, H. S., Chae, H., Kim, K., Jang, J., Yang, J., & Kim, D. K. (2014). Generalized inverse aided PAPR-aware linear precoder design for MIMO-OFDM system. IEEE communications letters, 18(8), 1363-1366.
Chen, J., Lu, S., Wang, T. Y., Wu, J. Y., Li, C. P., Ng, S. X., ... & Hanzo, L. (2024). A three-stage-concatenated non-liner MMSE interference rejection combining aided MIMO-OFDM receiver and its EXIT-chart analysis. IEEE Open Journal of Vehicular Technology.
Cheng, X., Zayani, R., Ferecatu, M., & Audebert, N. (2022, April). Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities. In 2022 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1039-1044). IEEE.
Cherif, M., Arfaoui, A., Zayani, R., & Bouallegue, R. (2023). End-to-end deep learning for multipair two-way massive MIMO with PA impairments. IEEE Systems Journal, 17(2), 3150-3159.
da Silva, B. S. D. C., Souto, V. D., Souza, R. D., & Mendes, L. L. (2024). A survey of PAPR techniques based on machine learning. Sensors, 24(6), 1918.
Dokhanchi, S. H., Shankar, M. B., Mishra, K. V., & Ottersten, B. (2020, May). Multi-constraint spectral co-design for colocated MIMO radar and MIMO communications. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4567-4571). IEEE.
Du, C., Zhang, H., Na, S., Wu, R., & Liu, Y. (2024, July). Root Sparse Bayesian Learning-Based 2-D Off-Grid DOA Estimation Algorithm for Massive MIMO Systems. In International Symposium on Neural Networks (pp. 235-247). Singapore: Springer Nature Singapore.
Durant, A., Zayani, R., & Demmer, D. (2024, April). Sequential Hardware-aware Precoding for CF-mMIMO-OFDM with serial fronthaul. In 2024 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.
Durant, A., Zayani, R., Mabrouk, A., & Demmer, D. (2024, April). Inband and out-of-band performance evaluation of downlink CF-mMIMO-OFDM under low-resolution DACs. In 2024 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.
Furqan, H. M., Solaija, M. S. J., Hamamreh, J. M., & Arslan, H. (2019). Intelligent physical layer security approach for V2X communication. arXiv preprint arXiv:1905.05075.
Galaviz-Aguilar, J. A., Vargas-Rosales, C., & Tlelo-Cuautle, E. (2020). RF-PA modeling of PAPR: A precomputed approach to reinforce spectral efficiency. IEEE Access, 8, 138217-138235.
Goyal, S., Saida, A., Chandna, M., Kesana, S., Amrutha, C., & Petricia, D. (2024, October). Antenna Design for MIMO SDR Systems Using Deep Learning. In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.