Enhanced chicken swarm optimization for channel estimation and low complexity hybrid beamforming over massive MIMO
Abstract
Massive Multiple-Input Multiple-Output (MIMO) systems with several antennas at the base station (BS) achieve overspatial degrees of freedom. It significantly increases energy and spectrum efficiencies, making it crucial technology for future wireless communications. Even with these alluring benefits, using a lot of antennas results in significant power and hardware costs. Additionally, the efficacy of hybrid beamforming is lacking in the current system, which has an impact on both system and energy efficiency. Enhanced Chicken Swarm Optimisation (ECSO) and energy-efficient Hybrid Analog-Digital (HAD) beamforming using Analog-To-Digital Converter (ADC) are used in this study to address the aforementioned issues. This research work includes main steps are system model, channel estimation and energy efficient ADC on MIMO systems. Initially, the system model is constructed using antennas, Radio Frequency (RF) chains and antenna users. Then, channel estimation accuracy is ensured by using ECSO algorithm. It generates best fitness values in terms of best channel and sum rate using local and global optimal values. K users concurrent in antennas are acquired to guarantee that channel coefficient estimates between antennas and, low complexity heuristic based on channel estimations can be carried out. Lastly, beamforming is accomplished by HAD both digitally and analogously where pilot sequence optimizations, HAD, and ADC quantization bits distributions reduce Mean Square Error (MSE) by working in paraleel for estimating channels. The ECSO-HAD outperformed other approaches in terms of spectrum efficiencies, greater sum rates, lower energy consumptions, and better Normalised Mean Square Error (NMSE) rates in simulation results.
Keywords
Full Text:
PDFReferences
1. Marzetta TL. Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas. IEEE Transactions on Wireless Communications. 2010; 9(11): 3590-3600. doi: 10.1109/twc.2010.092810.091092
2. Rusek F, Persson D, Lau BK, et al. Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays. IEEE Signal Processing Magazine. 2013; 30(1): 40-60. doi: 10.1109/msp.2011.2178495
3. Lu L, Li GY, Swindlehurst AL, et al. An Overview of Massive MIMO: Benefits and Challenges. IEEE Journal of Selected Topics in Signal Processing. 2014; 8(5): 742-758. doi: 10.1109/jstsp.2014.2317671
4. Shin C, Heath RW, Powers EJ. Blind Channel Estimation for MIMO-OFDM Systems. IEEE Transactions on Vehicular Technology. 2007; 56(2): 670-685. doi: 10.1109/tvt.2007.891429
5. Shlezinger N, van Sloun RJG, Huijben IAM, et al. Learning Task-Based Analog-to-Digital Conversion for MIMO Receivers. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); May 2020. doi: 10.1109/icassp40776.2020.9053855
6. Wu X, Liu D, Yin F. Hybrid Beamforming for Multi-User Massive MIMO Systems. IEEE Transactions on Communications. 2018; 66(9): 3879-3891. doi: 10.1109/tcomm.2018.2829511
7. Ali B, Asaad S, Mueller RR. Stepwise transmit antenna selection in downlink massive multiuser MIMO. 22nd International ITG Workshop on Smart Antennas. VDE; 2018.
8. Asaad S, Rabiei AM, Muller RR. Massive MIMO With Antenna Selection: Fundamental Limits and Applications. IEEE Transactions on Wireless Communications. 2018; 17(12): 8502-8516. doi: 10.1109/twc.2018.2877992
9. Asaad S, Bereyhi A, Rabiei AM, et al. Optimal Transmit Antenna Selection for Massive MIMO Wiretap Channels. IEEE Journal on Selected Areas in Communications. 2018; 36(4): 817-828. doi: 10.1109/jsac.2018.2825159
10. Wang J, Cheng Z, Ersoy OK, et al. Improvement and Application of Chicken Swarm Optimization for Constrained Optimization. IEEE Access. 2019; 7: 58053-58072. doi: 10.1109/access.2019.2913180
11. Liang S, Fang Z, Sun G, et al. Sidelobe Reductions of Antenna Arrays via an Improved Chicken Swarm Optimization Approach. IEEE Access. 2020; 8: 37664-37683. doi: 10.1109/access.2020.2976127
12. He H, Wen CK, Jin S, et al. Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems. IEEE Wireless Communications Letters. 2018; 7(5): 852-855. doi: 10.1109/lwc.2018.2832128
13. Lin T, Yu X, Zhu Y, et al. Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave MIMO Systems. GLOBECOM 2020 - 2020 IEEE Global Communications Conference; December 2020. doi: 10.1109/globecom42002.2020.9322519
14. Ding Q, Jing Y. Receiver Energy Efficiency and Resolution Profile Design for Massive MIMO Uplink With Mixed ADC. IEEE Transactions on Vehicular Technology. 2018; 67(2): 1840-1844. doi: 10.1109/tvt.2017.2763825
15. Liu T, Tong J, Guo Q, et al. Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Successive Interference Cancellation. IEEE Transactions on Wireless Communications. 2019; 18(8): 3987-4002. doi: 10.1109/twc.2019.2920129
16. Wang G, Yang Z, Gong T. Hybrid Beamforming Design for Self-Interference Cancellation in Full-Duplex Millimeter-Wave MIMO Systems with Dynamic Subarrays. Entropy. 2022; 24(11): 1687. doi: 10.3390/e24111687
17. Ardah K, Fodor G, Silva YCB, et al. Hybrid Analog-Digital Beamforming Design for SE and EE Maximization in Massive MIMO Networks. IEEE Transactions on Vehicular Technology. 2020; 69(1): 377-389. doi: 10.1109/tvt.2019.2933305
18. Tan W, Li S, Zhou M. Spectral and energy efficiency for uplink massive MIMO systems with mixed-ADC architecture. Physical Communication. 2022; 50: 101516. doi: 10.1016/j.phycom.2021.101516
19. Ahmed K, Hassanien AE, Bhattacharyya S. A novel chaotic chicken swarm optimization algorithm for feature selection. 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN); November 2017. doi: 10.1109/icrcicn.2017.8234517
20. Wu D, Xu S, Kong F. Convergence Analysis and Improvement of the Chicken Swarm Optimization Algorithm. IEEE Access. 2016; 4: 9400-9412. doi: 10.1109/access.2016.2604738
21. Sohrabi F, Yu W. Hybrid analog and digital beamforming for OFDM-based large-scale MIMO systems. 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC); July 2016. doi: 10.1109/spawc.2016.7536763
22. Morsali A, Haghighat A, Champagne B. Generalized Framework for Hybrid Analog/Digital Signal Processing in Massive and Ultra-Massive-MIMO Systems. IEEE Access. 2020; 8: 100262-100279. doi: 10.1109/access.2020.2998064
23. Xue X, Bogale TE, Wang X, et al. Hybrid analog-digital beamforming for multiuser MIMO millimeter wave relay systems. 2015 IEEE/CIC International Conference on Communications in China (ICCC); November 2015. doi: 10.1109/iccchina.2015.7448664
24. Bogale TE, Le LB. Beamforming for multiuser massive MIMO systems: Digital versus hybrid analog-digital. 2014 IEEE Global Communications Conference; December 2014. doi: 10.1109/glocom.2014.7037444
DOI: https://doi.org/10.32629/jai.v7i5.1596
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 M. Kasiselvanathan, S. Nagarani, P. Ramya, Manikandan Rajagopal
License URL: https://creativecommons.org/licenses/by-nc/4.0/