Fast Port Selection using Temporal and Spatial Correlation for Fluid Antenna Systems
Shunhang Zhang, Jinghan Mao, Yanzhao Hou, Yu Chen, Kai‐Kit Wong, Qimei Cui, Xiaofeng Tao
Abstract
Fluid antenna system (FAS) is a flexible antenna structure that obtains tremendous space diversity by allowing the antenna to change its position (or port) in a given space. The extraordinary performance requires FAS to always switch to the port with the largest signal-to-noise ratio (SNR) from the large number of ports. In practice, however, this means that a large number of channel observations are required and the overhead could outweigh the benefits. In this paper, we exploit the spatial and temporal correlation of the port channels using a machine learning approach. The proposed algorithm first estimates all the port channels in space from a small number of observations, then predicts the port channels in the subsequent time slots. Re-observations are used to reduce error propagation in long short-term memory (LSTM) rolling window regression. Simulation results demonstrate that the proposed algorithm can achieve promising performance with few re-observations in high-mobility scenarios.