IEEE Transactions on Vehicular Technology, February 2025
Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna
position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these
issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate
that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation
FA-enabled communication systems.
Type:
Journal
Date:
2025-02-04
Department:
Communication systems
Eurecom Ref:
8053
Copyright:
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