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arXiv · 2301.11640

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

Xing Zhang·Haiyang Zhang·Nimrod Glazer·Oded Cohen·Eliya Reznitskiy·Shlomi Savariego·Moshe Namer·Yonina C. Eldar
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Abstract

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.

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Xing Zhang, Haiyang Zhang, Nimrod Glazer, Oded Cohen, Eliya Reznitskiy, Shlomi Savariego, Moshe Namer, Yonina C. Eldar. 2023-01-27. Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery. https://arxiv.org/abs/2301.11640

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