Random ISAC Signals Deserve Dedicated Precoding
Shihang Lu, Fan Liu, Fuwang Dong, Yifeng Xiong, Jie Xu, Ya-Feng Liu,, and Shi Jin

TL;DR
This paper introduces new precoding schemes for random ISAC signals, optimizing sensing performance through a novel metric and demonstrating significant gains over traditional methods.
Contribution
It proposes both data-dependent and data-independent precoding schemes for random ISAC signals, with optimized structures and an extension to ISAC scenarios, improving sensing performance.
Findings
Proposed DDP and DIP schemes outperform conventional ISAC signaling.
ELMMSE metric effectively characterizes sensing performance with random signals.
Dedicated precoding designs significantly enhance ISAC system performance.
Abstract
Radar systems typically employ well-designed deterministic signals for target sensing, while integrated sensing and communications (ISAC) systems have to adopt random signals to convey useful information. This paper analyzes the sensing and ISAC performance relying on random signaling in a multi-antenna system. Towards this end, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), which characterizes the estimation error averaged over random ISAC signals. Then, we investigate a data-dependent precoding (DDP) scheme to minimize the ELMMSE in sensing-only scenarios, which attains the optimized performance at the cost of high implementation overhead. To reduce the cost, we present an alternative data-independent precoding (DIP) scheme by stochastic gradient projection (SGP). Moreover, we shed light on the optimal structures of both…
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Taxonomy
TopicsRadar Systems and Signal Processing · Distributed Sensor Networks and Detection Algorithms · Advanced SAR Imaging Techniques
