Integrating Long Short-Term Memory and Reinforcement Learning in Federated Learning Frameworks for Energy-Efficient Signal Processing in UAV-Assisted Wireless Communication Networks
Keywords:
UAV, Long Short-Term Memory, Reinforcement Learning, signal processing, wireless communication networks, Federated Learning , energy efficiencyAbstract
This paper presents a comprehensive study of signal processing algorithms designed for enhancing the energy efficiency of UAV-aided wireless communication networks. We explore a sequence of advanced machine learning techniques, each tailored to address specific challenges within the network. We begin by detailing the application of Long Short-Term Memory (LSTM) networks, which are adept at uncovering patterns in data with unknown objectives or constraints. Echo-State Networks (ESNs) are then introduced for their proficiency in sequence and pattern detection, essential for classification and regression prediction problems in signal processing. We further examine the role of Reinforcement Learning (RL) in actively engaging with prediction problems and NP-hard problems, leveraging a reward-based system to facilitate active learning. In addressing the critical concerns of data privacy and excessiveness, Federated Learning (FL) is proposed as a decentralized solution that promotes local training on UAVs, significantly reducing the need for data centralization. Through the methods outlined, we achieve a novel optimization framework that integrates the aforementioned techniques, commencing with the identification and mitigation of unwanted vehicles in the network, which is processed into a Data Traffic Matrix. This feeds into an LTE DIC algorithm based on correlation and culminates in an optimization process that considers specific network parameters 'P' and 'B'. The results, derived from the comparative analysis using the established techniques, indicate a significant improvement in network efficiency. The proposed framework demonstrates a marked enhancement in energy efficiency, with an observed improvement percentage over existing methods. This substantiates the efficacy of the integrated approach, suggesting that the application of machine learning algorithms can lead to superior performance in UAV-assisted networks, providing a significant step forward in the development of autonomous and efficient wireless communication systems.
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