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IEEE ICASSP 2022 || Singapore || 7-13 May 2022 Virtual; 22-27 May 2022 In-Person

IEEE ICASSP 2022

2022 IEEE International Conference on Acoustics, Speech and Signal Processing

7-13 May 2022
  • Virtual (all paper presentations)
22-27 May 2022
  • Main Venue: Marina Bay Sands Expo & Convention Center, Singapore
27-28 October 2022
  • Satellite Venue: Crowne Plaza Shenzhen Longgang City Centre, Shenzhen, China

ICASSP 2022
ST-6: TASKED-BASED QUANTIZATION For MULTI-USER SIGNAL RECOVERY
Mon, 9 May, 23:00 - 23:45 China Time (UTC +8)
Mon, 9 May, 15:00 - 15:45 UTC
Location: Gather Area P
Virtual
Gather.Town
Show & Tell
Presented by: Xing Zhang, The Weizmann Institute of Science Haiyang Zhang, The Weizmann Institute of Science Oded Cohen, The Weizmann Institute of Science Eliya Reznitskiy, The Weizmann Institute of Science Shlomi Savariego, The Weizmann Institute of Science Nimrod Glazer, The Weizmann Institute of Science Moshe Namer, The Weizmann Institute of Science Yonina C. Eldar, The Weizmann Institute of Science

In this demo, we present task-based quantization hardware built for providing an accurate signal estimate in a multi-user wireless communication setting. Specifically, the transmitted signals from multi-users are recovered at the receiver by applying the developed task-based low-bit quantization board.

Quantization plays a critical role in digital signal processing systems, allowing the representation of a continuous-amplitude signal using a finite number of bits. However, for high-dimensional input signals such as those in multi-user MIMO systems, accurately representing these signals requires a large number of quantization bits, causing severe cost, power consumption, and memory burden. To address this challenge, we recently proposed a task-based quantization approach that guarantees the recovery of high-dimensional signals from a low-bit representation by accounting for the underlying task in the design of the quantizer [1]. A tailored analog precoder is designed to properly pre-process the signal prior to quantization allowing to dramatically reduce the number of bits while still allowing for signal recovery.

In this demo, we design a configurable quantization hardware, consisting of an analog combiner to reduce the input dimensionality and scalar quantizers with dynamically adjustable quantization bits. The developed hardware platform is then applied to multi-user signal recovery. Our demonstration platform consists of a 16x2 analog combiner and a configurable quantizer, including 2, 3, 4 & 12 bits quantization. Using a dedicated GUI, our demo will show that the nearly optimal performance of multi-user signal recovery can be achieved with a low-bit quantizer by accounting for the task.

[1] N. Shlezinger, Y. C. Eldar, and M. R. Rodrigues, “Hardware-limited task-based quantization,” IEEE Trans. Signal Process., vol. 67, no. 20, pp. 5223–5238, 2019