Paper Title: Federated Learning mmWave Beamforming for V2X Communications with Imperfect CSI and Doppler Shift
Conference Name: The 15th International Conference on Ubiquitous and Future Networks (ICUFN), July 2-5, 2024, Budapes
Abstract: The proposed FL-mm-V2X approach addresses
challenges in vehicle-to-everything (V2X) communications, leveraging
mmWave beamforming to enhance spectrum efficiency and
reduce interference in the presence of severe Doppler shift (DS)
and imperfect channel state information (I-CSI). FL-mm-V2X
combines federated learning (FL) with non-orthogonal multiple
access (NOMA) for mmWave beamforming. Vehicle users conduct
client training, and road side users (RSUs) collect gradients,
optimizing power allocation through iterative updates. The optimization
involves solving a non-convex problem with Lagrangian
variables, adhering to Karush–Kuhn–Tucker conditions, utilizing
a sub-gradient approach. The approach employs a convolutional
neural network for DS estimation, evaluated against metrics such
as bit error rate, DS estimation error, and signal-to-interference plus-
noise ratio (SINR). Comparative analysis includes SINR,
the number of vehicle users, transmitted power, and complexity.
Simulation results highlight the proposed approach’s effectiveness
in mitigating I-CSI and DS, demonstrating lower complexity
compared to existing methods and confirming its suitability for
dynamic performance in high-mobility mmWave channels.