Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks.
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| Název: | Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks. |
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| Autoři: | Kanmani R; Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, 641010, India. kanmanivan@gmail.com., Praveena SM; Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, 641010, India. |
| Zdroj: | Scientific reports [Sci Rep] 2025 Apr 21; Vol. 15 (1), pp. 13740. Date of Electronic Publication: 2025 Apr 21. |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Abstrakt: | 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. (© 2025. The Author(s).) |
| Competing Interests: | Declarations. Competing intertests: The author has no relevant financial or non-financial interests to disclose. Consent to publication: All authors listed above have consented to get their data and image published. |
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| Contributed Indexing: | Keywords: Deep learning; Interference; Long short-term memory network; MIMO; OFDM; Optimization algorithms; Power allocation; Termite colony optimization |
| Entry Date(s): | Date Created: 20250421 Latest Revision: 20250424 |
| Update Code: | 20250424 |
| PubMed Central ID: | PMC12012048 |
| DOI: | 10.1038/s41598-025-97944-x |
| PMID: | 40258916 |
| Databáze: | MEDLINE |
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