Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks
Abstract MIMO–OFDM systems are essential for high-capacity wireless networks, offering improved data throughput and spectral efficiency necessary for dense user environments. Effective power and interference management are pivotal for maintaining signal quality and enhancing resource utilization. Ex...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97944-x |
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| author | R. Kanmani S. Mary Praveena |
| author_facet | R. Kanmani S. Mary Praveena |
| author_sort | R. Kanmani |
| collection | DOAJ |
| description | Abstract MIMO–OFDM systems are essential for high-capacity wireless networks, offering improved data throughput and spectral efficiency necessary for dense user environments. Effective power and interference management are pivotal for maintaining signal quality and enhancing resource utilization. Existing techniques for resource allocation and interference control in massive MIMO–OFDM networks face challenges related to scalability, adaptability, and energy efficiency. To address these limitations, this work proposes a novel bio-inspired Termite Colony Optimization-based Multi-Agent System (TCO-MAS) integrated with an LSTM model for predictive adaptability. The deep learning LSTM model aids agents in forecasting future network conditions, enabling dynamic adjustment of pheromone levels for optimized power allocation and interference management. By simulating termite behavior, agents utilize pheromone-based feedback to achieve localized optimization decisions with minimal communication overhead. Experimental analyses evaluated the proposed TCO-MAS across key metrics such as Sum Rate, Energy Efficiency, Spectral Efficiency, Latency, and Fairness Index. Results demonstrate that TCO-MAS outperformed conventional algorithms, achieving a 20% higher sum rate and 15% better energy efficiency under high-load conditions. Limitations include dependency on specific pheromone adjustment parameters, which may require fine-tuning for diverse scenarios. Practical implications highlight its potential for scalable and adaptive deployment in ultra-dense wireless networks, though additional field testing is recommended to ensure robustness in varied real-world environments. |
| format | Article |
| id | doaj-art-13f0334295a04a109548bd188e4ff016 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-13f0334295a04a109548bd188e4ff0162025-08-20T02:19:58ZengNature PortfolioScientific Reports2045-23222025-04-0115112510.1038/s41598-025-97944-xBio inspired multi agent system for distributed power and interference management in MIMO OFDM networksR. Kanmani0S. Mary Praveena1Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of TechnologyDepartment of Electronics and Communication Engineering, Sri Ramakrishna Institute of TechnologyAbstract MIMO–OFDM systems are essential for high-capacity wireless networks, offering improved data throughput and spectral efficiency necessary for dense user environments. Effective power and interference management are pivotal for maintaining signal quality and enhancing resource utilization. Existing techniques for resource allocation and interference control in massive MIMO–OFDM networks face challenges related to scalability, adaptability, and energy efficiency. To address these limitations, this work proposes a novel bio-inspired Termite Colony Optimization-based Multi-Agent System (TCO-MAS) integrated with an LSTM model for predictive adaptability. The deep learning LSTM model aids agents in forecasting future network conditions, enabling dynamic adjustment of pheromone levels for optimized power allocation and interference management. By simulating termite behavior, agents utilize pheromone-based feedback to achieve localized optimization decisions with minimal communication overhead. Experimental analyses evaluated the proposed TCO-MAS across key metrics such as Sum Rate, Energy Efficiency, Spectral Efficiency, Latency, and Fairness Index. Results demonstrate that TCO-MAS outperformed conventional algorithms, achieving a 20% higher sum rate and 15% better energy efficiency under high-load conditions. Limitations include dependency on specific pheromone adjustment parameters, which may require fine-tuning for diverse scenarios. Practical implications highlight its potential for scalable and adaptive deployment in ultra-dense wireless networks, though additional field testing is recommended to ensure robustness in varied real-world environments.https://doi.org/10.1038/s41598-025-97944-xMIMOOFDMPower allocationInterferenceOptimization algorithmsDeep learning |
| spellingShingle | R. Kanmani S. Mary Praveena Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks Scientific Reports MIMO OFDM Power allocation Interference Optimization algorithms Deep learning |
| title | Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks |
| title_full | Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks |
| title_fullStr | Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks |
| title_full_unstemmed | Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks |
| title_short | Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks |
| title_sort | bio inspired multi agent system for distributed power and interference management in mimo ofdm networks |
| topic | MIMO OFDM Power allocation Interference Optimization algorithms Deep learning |
| url | https://doi.org/10.1038/s41598-025-97944-x |
| work_keys_str_mv | AT rkanmani bioinspiredmultiagentsystemfordistributedpowerandinterferencemanagementinmimoofdmnetworks AT smarypraveena bioinspiredmultiagentsystemfordistributedpowerandinterferencemanagementinmimoofdmnetworks |