# Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery

arXiv: https://arxiv.org/abs/2301.11640 · HTML: https://mlsanity.com/paper/2301.11640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [White Paper on Broadband Connectivity in 6G](https://mlsanity.com/paper/2004.14247/) (2020)
- [Asymptotic Task-Based Quantization with Application to Massive MIMO](https://mlsanity.com/paper/1811.10077/) (2018)
- [One-Bit Radar Processing with Time-Varying Sampling Thresholds](https://mlsanity.com/paper/1911.10170/) (2019)
- [BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization](https://mlsanity.com/paper/2010.00195/) (2020)
- [Deep Task-Based Analog-to-Digital Conversion](https://mlsanity.com/paper/2201.12634/) (2022)
- [Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits](https://mlsanity.com/paper/1801.06718/) (2018)
- [Compressed Ultrasound Imaging:from Sub-Nyquist Rates to Super-Resolution](https://mlsanity.com/paper/2107.11263/) (2021)
- [Analog to Digital Cognitive Radio: Sampling, Detection and Hardware](https://mlsanity.com/paper/1708.05201/) (2017)
- [Sub-Nyquist Radar Systems: Temporal, Spectral and Spatial Compression](https://mlsanity.com/paper/1807.11542/) (2018)
- [Analyzing Uplink Grant-free Sparse Code Multiple Access System in Massive IoT Networks](https://mlsanity.com/paper/2103.10241/) (2021)

## Built on by (most cited first, 1 of 1)

- [Pragmatic Communication in Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2401.12694/) (2024)

## Abstract (arXiv)

Quantization plays a critical role in digital signal processing systems, allowing the representation of continuous amplitude signals with a finite number of bits. However, accurately representing signals requires a large number of quantization bits, which causes severe cost, power consumption, and memory burden. A promising way to address this issue is task-based quantization. By exploiting the task information for the overall system design, task-based quantization can achieve satisfying performance with low quantization costs. In this work, we apply task-based quantization to multi-user signal recovery and present a hardware prototype implementation. The prototype consists of a tailored configurable combining board, and a software-based processing and demonstration system. Through experiments, we verify that with proper design, the task-based quantization achieves a reduction of 25 fold in memory by reducing from 16 receivers with 16 bits each to 2 receivers with 5 bits each, without compromising signal recovery performance.