Distributed Deep Learning for Modulation Classification in 6G Cell-Free Wireless Networks
arxiv(2024)
摘要
In the evolution of 6th Generation (6G) technology, the emergence of
cell-free networking presents a paradigm shift, revolutionizing user
experiences within densely deployed networks where distributed access points
collaborate. However, the integration of intelligent mechanisms is crucial for
optimizing the efficiency, scalability, and adaptability of these 6G cell-free
networks. One application aiming to optimize spectrum usage is Automatic
Modulation Classification (AMC), a vital component for classifying and
dynamically adjusting modulation schemes. This paper explores different
distributed solutions for AMC in cell-free networks, addressing the training,
computational complexity, and accuracy of two practical approaches. The first
approach addresses scenarios where signal sharing is not feasible due to
privacy concerns or fronthaul limitations. Our findings reveal that maintaining
comparable accuracy is remarkably achievable, yet it comes with an increase in
computational demand. The second approach considers a central model and
multiple distributed models collaboratively classifying the modulation. This
hybrid model leverages diversity gain through signal combining and requires
synchronization and signal sharing. The hybrid model demonstrates superior
performance, achieving a 2.5
computational load. Notably, the hybrid model distributes the computational
load across multiple devices, resulting in a lower individual computational
load.
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