在MIMO OFDM网络中用于分布式电源和干扰管理的生物启发多代理系统
1Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, 641010, India. kanmanivan@gmail.com.
这项研究介绍了一种基于长殖民地优化的多代理系统 (TCO-MAS) 与LSTM用于无线网络的新型系统. 在大规模的MIMO-OFDM系统中,TCO-MAS提高了总和率和能源效率.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 网络优化 网络优化
背景情况:
- 大规模的MIMO-OFDM系统对于高容量无线网络至关重要,要求高效的功率和干扰管理.
- 现有的资源分配和干扰控制方法在密集环境中难以实现可扩展性,适应性和能源效率.
研究的目的:
- 提出一种基于生物启发的新型甲虫群优化多种代理系统 (TCO-MAS),与LSTM模型集成.
- 通过预测性适应性,增强大规模MIMO-OFDM网络的功率分配和干扰管理.
主要方法:
- 一个多代理系统 (MAS) 灵感来源于白殖民地优化开发.
- 集成了一个深度学习的长短期记忆 (LSTM) 模型,用于预测网络条件.
- 代理商使用基于激素的反来进行局部优化,减少通信开销.
主要成果:
- 与传统算法相比,TCO-MAS实现了20%更高的总和率和15%更好的能源效率.
- 评估包括总比率,能源效率,光谱效率,延迟和公平指数.
- 该系统在高负载条件下表现出卓越的性能.
结论:
- 拟议的TCO-MAS提供了一个可扩展和适应的解决方案,用于超密集无线网络中的电源和干扰管理.
- 虽然有效,但对于各种场景来说,对激素调整的参数微调是必要的.
- 建议进行进一步的实地测试,以验证现实世界的稳定性.
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