Channel Estimation and Reconstruction in Fluid Antenna System: Oversampling is Essential

Abstract

Fluid antenna system (FAS) has recently surfaced as a promising technology for the upcoming sixth generation (6G) wireless networks. Unlike traditional antenna system (TAS) with fixed antenna location, FAS introduces a flexible component in which the radiating element can switch its position within a predefined space. This capability allows FAS to achieve additional diversity and multiplexing gains. Nevertheless, to fully reap the benefits of FAS, obtaining channel state information (CSI) over the predefined space is crucial. In this paper, we study the system with a transmitter equipped with a traditional fixed antenna and a receiver with a fluid antenna by considering an electromagnetic-compliant channel model. We address the challenges of channel estimation and reconstruction using Nyquist sampling and maximum likelihood estimation (MLE) methods. Our analysis reveals a fundamental tradeoff between the accuracy of the reconstructed channel and the number of estimated channels, indicating that half-wavelength sampling is insufficient for perfect reconstruction and that oversampling is essential to enhance accuracy. Despite its advantages, oversampling can introduce practical challenges. Consequently, we propose a suboptimal sampling distance that facilitates efficient channel reconstruction. In addition, we employ the MLE method to bound the channel estimation error by $\epsilon $ , with a specific confidence interval (CI). Our findings enable us to determine the minimum number of estimated channels and the total number of pilot symbols required for efficient channel reconstruction in a given space. Lastly, we investigate the rate performance of FAS and TAS and demonstrate that FAS with imperfect CSI can outperform TAS with perfect CSI. In contrast to existing works, we also show that there is an optimal fluid antenna size that maximizes the achievable rate when considering the energy and bandwidth overheads for full CSI acquisition.