Sensing aided Channel Estimation in Wideband Millimeter-Wave MIMO Systems

TL;DR

This paper introduces a sensing-aided channel estimation method for wideband mmWave MIMO systems that reduces pilot overhead by leveraging environmental scatterer information, improving estimation accuracy.

Contribution

It proposes a novel SWOMP-SBL algorithm that integrates sensing data into channel estimation, handling uncertainties and inaccuracies in scatterer information.

Findings

Effective reduction in pilot overhead for channel estimation.

Improved accuracy in wideband mmWave MIMO systems.

Theoretical bounds on angle and gain estimation accuracy.

Abstract

In this work, the uplink channel estimation problem is considered for a millimeter wave (mmWave) multi-input multi-output (MIMO) system. It is well known that pilot overhead and computation complexity in estimating the channel increases with the number of antennas and the bandwidth. To overcome this, the proposed approach allows the channel estimation at the base station to be aided by the sensing information. The sensing information contains an estimate of scatterers locations in an environment. A simultaneous weighting orthogonal matching pursuit (SWOMP) - sparse Bayesian learning (SBL) algorithm is proposed that efficiently incorporates this sensing information in the communication channel estimation procedure. The proposed framework can cope with scenarios where a) scatterers present in the sensing information are not associated with the communication channel and b) imperfections in the scatterers' location. Simulation results show that the proposed sensing aided channel estimation algorithm can obtain good wideband performance only at the cost of fractional pilot overhead. Finally, the Cramer-Rao Bound (CRB) for the angle estimation and multipath channel gains in the SBL is derived, providing valuable insights into the local identifiability of the proposed algorithms.