Sensing With Random Signals

TL;DR

This paper introduces a new sensing performance metric for ISAC systems using random signals, proposes optimized and low-complexity precoding schemes, and demonstrates their effectiveness through simulations.

Contribution

It defines the ELMMSE metric for sensing with random signals and develops both data-dependent and data-independent precoding schemes with a stochastic gradient algorithm.

Findings

Data-dependent precoding achieves optimal sensing performance.

The stochastic gradient projection algorithm effectively minimizes ELMMSE.

Proposed methods outperform traditional approaches in simulations.

Abstract

Radar systems typically employ well-designed deterministic signals for target sensing. In contrast to that, integrated sensing and communications (ISAC) systems have to use random signals to convey useful information, potentially causing sensing performance degradation. In this paper, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), accounting for the randomness of ISAC signals. Then, we investigate a data-dependent precoding scheme to minimize the ELMMSE, which attains the optimized sensing performance at the price of high computational complexity. To reduce the complexity, we present an alternative data-independent precoding scheme and propose a stochastic gradient projection (SGP) algorithm for ELMMSE minimization, which can be trained offline by locally generated signal samples. Finally, we demonstrate the superiority of the proposed methods by simulations.