Citation :

Ashutosh Pande, Pankaj Shankar Shrivastav, "A PSO-Optimized Non-Linear Channel Capacity Enhancement Framework for CSI-based and MIMO Wireless Communications," International Journal of Electronics and Communication Engineering, vol. 13, no. 10, pp. 224-238, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I10P113

Abstract

Optimization of channel capacity in Channel State Information (CSI)-assisted Multiple-Input Multiple-Output (MIMO) systems is a critical need for beyond 5G and future 6G wireless communication systems, in which non-linear propagation and time-varying fading channels significantly affect the channel capacity. Traditional approaches based on linear channel models and complex optimization algorithms often face challenges in terms of robustness, Scalability, and global optimality. To overcome these limitations, this study proposes a Particle Swarm Optimization (PSO)-optimized non-linear channel capacity enhancement scheme by incorporating Monte Carlo-based stochastic channel modeling, polynomial analysis of Signal-to-Noise Ratio (SNR)-capacity trends using K-fold cross-validation, and evolutionary optimization. Simulations performed in a realistic fading channel environment strongly validate the existence of a strong non-linear SNR-capacity trend and statistical channel stability. The baseline Monte Carlo simulation model establishes a channel capacity prediction of approximately 1.3×108 bps when operating at an SNR value of 20 dB. The PSO-optimized approach results in a channel capacity increase to 3.79 × 10⁸ bps, representing an approximately 2.91-fold increase over the baseline system. The proposed scheme demonstrates superior performance through simulation tests against Single Input Single Output (SISO), Single Input Multiple Output (SIMO), Multiple Input Single Output (MISO), and CSI-assisted MIMO systems, which prove its effectiveness for future high-capacity wireless communication systems to achieve maximum channel capacity.

Keywords

MIMO wireless communications, Channel capacity optimization, Particle swarm optimization, Monte Carlo simulation, Non-linear channel modeling, 6G networks.

References

1. Muhammad Zeeshan Asghar, Shafique Ahmed Memon, and Jyri Hämäläinen, “Evolution of Wireless Communication to 6G: Potential Applications and Research Directions,” Sustainability, vol. 14, no. 10, pp. 1-26, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Mohammed H. Alsharif et al., “Milestones of Wireless Communication Networks and Technology Prospect of Next Generation (6G),” Computers, Materials & Continua, vol. 71, no. 3, pp. 4803-4818, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Simon Gathu, “High-Performance Computing and Big Data: Emerging Trends in Advanced Computing Systems for Data-Intensive Applications,” Journal of Advanced Computing Systems, vol. 4, no. 8, pp. 22-35, 2024.
   [Google Scholar] [Publisher Link]
4. Olusekemi Afolabi, and Toby Oni, Future Directions in 5G Network Architecture for Enhanced Data Communication Reliability, pp. 1-13.
   [Google Scholar]
5. Agrippina Mwangi et al., “Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management,” Energies, vol. 17, no. 12, pp. 1-31, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Marwa Chafii et al., “Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory,” IEEE Communications Surveys & Tutorials, vol. 25, no. 2, pp. 868-904, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Ali Sufyan et al., “From 5G to beyond 5G: A Comprehensive Survey of Wireless Network Evolution, Challenges, and Promising Technologies,” Electronics, vol. 12, no. 10, pp. 1-25, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Haider A. H. Alobaidy et al., “Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges,” IEEE Access, vol. 10, pp. 24095-24131, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Muntadher Alsabah et al., “6G Wireless Communications Networks: A Comprehensive Survey,” IEEE Access, vol. 9, pp. 148191-148243, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Joel Poncha Lemayian, and Jehad M. Hamamreh, “Hybrid MIMO: A New Transmission Method For Simultaneously Achieving Spatial Multiplexing and Diversity Gains in MIMO Systems,” RS Innovative Communication Technologies, vol. 2, no. 4, pp. 1-11, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Muddasar Naeem, Giuseppe De Pietro, and Antonio Coronato, “Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems,” Sensors, vol. 22, no. 1, pp. 1-41, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Syed Agha Hassnain Mohsan et al., “A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends,” Sensors, vol. 23, no. 6, pp. 1-35, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Nasir Faruk et al., “Large-Scale Radio Propagation Path Loss Measurements and Predictions in the VHF and UHF Bands,” Heliyon, vol. 7, no. 6, pp. 1-15, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Yu Zhang, Xinzhe Pi, and Meiyun Xie, “Channel Modeling and Interference Analysis in IEEE 802.11a Wireless Communication Systems,” Journal of Networking and Network Applications, vol. 4, no. 4, pp. 178-189, 2025.
    [Google Scholar] [Publisher Link]
15. Yao Shi, 5g and Beyond Wireless Networks Optimization Through Uplink and Downlink Decoupled Access, The University of Manchester, 2021.
    [Google Scholar] [Publisher Link]
16. Laith Ibrahim et al., “Joint Beamforming Optimization Design and Performance Evaluation of RIS-Aided Wireless Networks: A Comprehensive State-of-the-Art Review,” IEEE Access, vol. 11, pp. 141801-141859, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Agbotiname Lucky Imoize et al., “Standard Propagation Channel Models for MIMO Communication Systems,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1-36, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Asad Saleem et al., “A Critical Review on Channel Modeling: Implementations, Challenges and Applications,” Electronics, vol. 12, no. 9, pp. 1-42, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Nir Shlezinger et al., “Artificial Intelligence-Empowered Hybrid Multiple-Input/Multiple-Output Beamforming: Learning to Optimize for High-Throughput Scalable MIMO,” IEEE Vehicular Technology Magazine, vol. 19, no. 3, pp. 58-67, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Yandong Shi et al., “Machine Learning for Large-Scale Optimization in 6G Wireless Networks,” IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2088-2132, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Yu Qiao et al., “Deepseek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey,” ACM Computing Surveys, vol. 58, no. 7, pp. 1-37, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Fenghao Zhu et al., “Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond,” arXiv preprint, pp. 1-80, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Maria Trigka, and Elias Dritsas, “Wireless Sensor Networks: From Fundamentals and Applications to Innovations and Future Trends,” IEEE Access, vol. 13, pp. 96365-96399, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
24. David Sarrutet al., “The OpenGATE Ecosystem for Monte Carlo Simulation in Medical Physics,” Physics in Medicine & Biology, vol. 67, no. 18, pp. 1-35, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
25. Yuan Zhong et al., “2-D Pinching-Antenna Systems: Modeling and Beamforming Design,” IEEE Transactions on Communications, vol. 74, pp. 8254-8266, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Tianheng Xu et al., “Advancing Fluid Antenna-Assisted Non-Terrestrial Networks in 6G and Beyond: Fundamentals, State of the Art, and Future Directions,” IEEE Communications Surveys & Tutorials, vol. 28, pp. 6797-6835, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Ogenyi Fabian Chukwudi et al., “Adaptive Energy-Efficient Power Allocation in Downlink NOMA Systems with Imperfect CSI: A PSO-Based Approach for Improving SIC Accuracy and System Performance,” IEEE Access, vol. 13, pp. 164313-164313, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
28. Zhiyu Han et al., “Adaptive Channel Estimation for Semi-Passive IRS with Optimized Sensor Deployment,” Sensors, vol. 25, no. 21, pp. 1-12, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Mohammed A. El-Shorbagy et al., “Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm with Its Variants and Applications,” IEEE Access, vol. 12, pp. 26062-26095, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
30. A. Jameer Basha et al., “PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network,” Computer Systems Science & Engineering, vol. 45, no. 2, pp. 1483-1493, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
31. Kanchan Sharma, and Surender K. Grewal, “Optimization of throughput for Free Space Optical Communication System in the Presence of Atmospheric Turbulence and Pointing Error,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 4, pp. 3423-3439, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
32. Yashuai Cao, Tiejun Lv, and Wei Ni, “Two-Timescale Optimization for Intelligent Reflecting Surface-Assisted MIMO Transmission in Fast-Changing Channels,” IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 10424-10437, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
33. Bugra Turan, and Sinem Coleri, “Machine Learning Based Channel Modeling for Vehicular Visible Light Communication,” IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 9659-9672, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
34. Muhammad Asadullah et al., “Blind Channel and Data Estimation using Fuzzy Logic Empowered Cognitive and Social Information-Based Particle Swarm Optimization (PSO),” International Journal of Computational Intelligence Systems, vol. 13, no. 1, pp. 400-408, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
35. Salih Hajem Glood, Mohammad Kaisb Layous, and Shaymaa Kaseb Layus, “Enhancing OFDMA for Next-Generation Wireless Communications,” International Journal of Computing and Digital Systems, vol. 18, no. 1, pp. 1-12, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
36. M. Mokhtar Zayed, and Mona Shokair, “Performance Analysis and Optimization of Modulation Techniques for Underwater Optical Wireless Communication in Varied Aquatic Environments,” Scientific Reports, vol. 15, no. 1, pp. 1-27, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
37. Said Mikki, “The Shannon Information Capacity of an Arbitrary Radiating Surface: An Electromagnetic Approach,” IEEE Transactions on Antennas and Propagation, vol. 71, no. 3, pp. 2556-2570, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
38. Dequn Liang, and Xinyu Dou, “Research on the Technology of Breaking the Shannon Channel Capacity and Shannon Limit,” Research Square, pp. 1-23, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
39. Sahil Shah, “Monte Carlo Simulation in Renewable Energy Planning: A Comprehensive Review and Novel Framework for Uncertainty Quantification,” The American Journal of Engineering and Technology, vol. 7, no. 6, pp. 24-45, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
40. Tiago P. Abud et al., “State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation,” Energies, vol. 16, no. 1, pp. 1-24, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
41. Renzhi Yuan et al., “Monte-Carlo Integration Models for Multiple Scattering Based Optical Wireless Communication,” IEEE Transactions on Communications, vol. 68, no. 1, pp. 334-348, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
42. Intesar Ramley et al., “An Overview of Underwater Optical Wireless Communication Channel Simulations with a Focus on the Monte Carlo Method,” Mathematics, vol. 12, no. 24, pp. 1-63, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
43. Yuanwei Liu et al., “CAPA: Continuous-Aperture Arrays for Revolutionizing 6G Wireless Communications,” IEEE Wireless Communications, vol. 32, no. 4, pp. 38-45, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
44. Poonam Yadav, Sandeep Kumar, and Rajesh Kumar, “A Review of Transmission Rate over Wireless Fading Channels: Classifications, Applications, and Challenges,” Wireless Personal Communications, vol. 122, no. 2, pp. 1709-1765, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
45. Ruoyu Zhang et al., “Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation,” IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 8571-8587, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
46. Yang Liu et al., “Channel Estimation for Intelligent Reflecting Surface Empowered Coal Mine Wireless Communication Systems,” Entropy, vol. 27, no. 9, pp. 1-20, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
47. Vishnu S. Pendyala, and Milind Patil, “Multi-Link Prediction for mmWave Wireless Communication Systems Using Liquid Time-Constant Networks, Long Short- Term Memory, and Interpretation Using Symbolic Regression,” Electronics, vol. 13, no. 14, pp. 1-13, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
48. Murilo Alencar Alves Junior et al., “Multiple-Antenna Weibull-Fading Wireless Communications Enhanced by Reconfigurable Intelligent Surfaces,” IEEE Access, vol. 11, pp. 107218-107236, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
49. T.R. Mahesh et al., “AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, pp. 1-11, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
50. Tareq M. Shami et al., “Particle Swarm Optimization: A Comprehensive Survey,” IEEE Access, vol. 10, pp. 10031-10061, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
51. Mohammed A.T. Alrubei et al., “Capacity Analysis & Performance Comparison of SISO, SIMO, MISO & MIMO Systems,” Journal of Physics: Conference Series, vol. 1530, no. 1, pp. 1-9, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
52. Neeraj Kumar, “Enhance the Capacity of MIMO Wireless Communication Channel using SVD and Optimal Power Allocation Algorithm,” International Journal of Electronics and Telecommunications, vol. 65, no. 1, pp. 71-78, 2019.
    [Google Scholar] [Publisher Link]
53. Felix Ma Yun, Jordan Nabi, and Mitra Hassani, “Resource Allocation in MIMO Setup,” arXiv preprint, pp. 1-19, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]