This research introduces a revolutionary paradigm for HetNet management, presenting an innovative algorithmic framework that transcends traditional notions of network capacity enhancement. Our exploration delves into the intricate interplay among distinct components, weaving together metaheuristic algorithms, Neural Networks optimization, and Federated Learning approaches. The primary focus is on optimizing capacity in IoT-based heterogeneous networks while ensuring impeccable coverage and data reliability. Employing multi-layer optimization methods, we propose a dynamic model for optimal transmission strategy, strategically allocating replicas within cloud computing environments to curtail data access costs. Our algorithm not only discerns optimal data replication locations but also navigates the delicate balance between spectral efficiency and ergodic capacity in cellular IoT networks with small cells using on/off control. The orchestrated interplay between metaheuristic algorithms, Neural Networks optimization, and Federated Learning orchestrates resource reallocation, attaining an optimal balance between spectral efficiency, power utility, and ergodic capacity based on Quality of Service (QoS) requirements. Simulation results corroborate the efficacy of our approach, showcasing enhanced tradeoffs between spectral efficiency and total ergodic capacity with diminished outage probability compared to prevailing algorithms across diverse scenarios.

翻译：本研究提出了一种革命性的异构网络管理范式，构建了一个超越传统网络容量增强理念的创新算法框架。我们深入探讨了不同组件之间的复杂相互作用，将元启发式算法、神经网络优化与联邦学习方法有机结合。主要目标是在确保完美覆盖与数据可靠性的前提下，优化基于物联网的异构网络容量。通过采用多层优化方法，我们提出了一种面向最优传输策略的动态模型，在云计算环境中战略性地分配副本以降低数据访问成本。我们的算法不仅能识别最优数据副本放置位置，还能通过小蜂窝的开/关控制，在蜂窝物联网网络中实现频谱效率与遍历容量之间的精妙平衡。元启发式算法、神经网络优化与联邦学习的协同作用，实现了资源再分配，基于服务质量要求达成了频谱效率、功率效用与遍历容量之间的最优平衡。仿真结果验证了该方法的效果，表明与现有算法相比，该方法能在多种场景下以更低的断线概率实现频谱效率与总遍历容量之间更优的权衡。