# Task-Based Quantizer Design for Sensing With Random Signals

arXiv: https://arxiv.org/abs/2403.11187 · HTML: https://mlsanity.com/paper/2403.11187/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing](https://mlsanity.com/paper/2102.12780/) (2021)
- [Asymptotic Task-Based Quantization with Application to Massive MIMO](https://mlsanity.com/paper/1811.10077/) (2018)
- [Integrated Sensing and Communication with mmWave Massive MIMO: A Compressed Sampling Perspective](https://mlsanity.com/paper/2201.05766/) (2022)
- [Random ISAC Signals Deserve Dedicated Precoding](https://mlsanity.com/paper/2311.01822/) (2023)

## Abstract (arXiv)

In integrated sensing and communication (ISAC) systems, random signaling is used to convey useful information as well as sense the environment. Such randomness poses challenges in various components in sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems. Unlike quantizers for channel estimation in massive multiple-input-multiple-out (MIMO) communication systems, sensing in ISAC systems needs to deal with random nonorthogonal transmitted signals rather than a fixed orthogonal pilot. Considering sensing performance and hardware implementation, we focus on task-based hardware-limited quantization with spatial analog combining. We propose two strategies of quantizer optimization, i.e., data-dependent (DD) and data-independent (DI). The former achieves optimized sensing performance with high implementation overhead. To reduce hardware complexity, the latter optimizes the quantizer with respect to the random signal from a stochastic perspective. We derive the optimal quantizers for both strategies and formulate an algorithm based on sample average approximation (SAA) to solve the optimization in the DI strategy. Numerical results show that the optimized quantizers outperform digital-only quantizers in terms of sensing performance. Additionally, the DI strategy, despite its lower computational complexity compared to the DD strategy, achieves near-optimal sensing performance.