End-to-End Communication With Task-Driven CSI Acquisition in Multiple-Antenna Systems

Abstract

The utilization of a Deep Autoencoder (DAE) in End-to-End (E2E) communication systems has been recognized for its potential to achieve global optimality, especially in scenarios involving multiple antennas. Although previous studies have leveraged channel state information (CSI) at the transmitter or receiver to augment DAE performance, a comprehensive examination of the CSI acquisition process remains limited. Addressing this gap, this letter introduces a task-driven approach to CSI acquisition for E2E communications with multiple antennas. The cornerstone of this approach is the selective acquisition of channel information pertinent to the design of the transceiver by integrating the CSI acquisition process with DAE, aiming to curtail CSI acquisition overhead. Implemented through AI-based pilot symbols generation and received pilot signals feedback, this method avoids explicit channel estimation and CSI reconstruction at the transceiver, facilitating a joint design paradigm. Our simulation results underline that the proposed framework substantially outperforms traditional maximum ratio transmission (MRT) precoding systems and other advanced DAE models in terms of both reducing channel acquisition overhead and improving overall system performance.