Serve Yourself! Federated Power Control for AI-Native 5G and Beyond

Saad Abouzahir;Essaid Sabir;Halima Elbiaze;Mohamed Sadik
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Abstract

The adoption of the Industrial Internet of Things (IIoT) in industries necessitates advancements in energy efficiency and latency reduction, especially for resource-constrained devices. Services require specific Quality of Service (QoS) levels to function properly, and meeting a threshold QoS can be sufficient for smooth connectivity, reducing the need to maximize perceived QoS due to energy concerns. This is modeled as a satisfactory game, aiming to find minimal power allocation to meet target demands. Due to environmental uncertainties, achieving a Robust Satisfactory Equilibrium (RSE) can be challenging, leading to less satisfaction. We propose a fully distributed, environment-aware power control scheme to enhance satisfaction in dynamic environments. The proposed Robust Banach-Picard (RBP) learning scheme combines deep learning and federated learning to overcome channel and interference impacts and accelerate convergence. Extensive simulations evaluate the scheme under varying channel states and QoS demands, with discussions on convergence speed, energy efficiency, scalability, complexity, and violation rate.
为你自己!AI-Native 5G及以后的联合功率控制
在工业中采用工业物联网(IIoT)需要提高能源效率和减少延迟,特别是对于资源受限的设备。服务需要特定的服务质量(QoS)级别才能正常工作,满足阈值QoS就足以实现平滑连接,从而减少了由于能源问题而最大化感知QoS的需求。这是一个令人满意的博弈模型,其目标是找到满足目标需求的最小功率分配。由于环境的不确定性,实现鲁棒满意均衡(RSE)可能具有挑战性,导致满意度降低。我们提出了一种全分布式、环境感知的电力控制方案,以提高动态环境下的满意度。鲁棒Banach-Picard (RBP)学习方案结合了深度学习和联邦学习,克服了信道和干扰的影响,加快了收敛速度。广泛的仿真评估了不同信道状态和QoS需求下的方案,并讨论了收敛速度、能源效率、可扩展性、复杂性和违反率。
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