# Asymptotic Task-Based Quantization with Application to Massive MIMO

arXiv: https://arxiv.org/abs/1811.10077 · HTML: https://mlsanity.com/paper/1811.10077/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Deep Task-Based Analog-to-Digital Conversion](https://mlsanity.com/paper/2201.12634/) (2022)
- [Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers](https://mlsanity.com/paper/2208.07525/) (2022)
- [Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systems](https://mlsanity.com/paper/2403.19132/) (2024)
- [Task-Based Graph Signal Compression](https://mlsanity.com/paper/2110.12387/) (2021)
- [Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery](https://mlsanity.com/paper/2301.11640/) (2023)
- [Interference Cancellation GAN Framework for Dynamic Channels](https://mlsanity.com/paper/2208.08019/) (2022)
- [Task-Based Quantizer Design for Sensing With Random Signals](https://mlsanity.com/paper/2403.11187/) (2024)
- [Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers](https://mlsanity.com/paper/2409.02430/) (2024)

## Abstract (arXiv)

Quantizers take part in nearly every digital signal processing system which operates on physical signals. They are commonly designed to accurately represent the underlying signal, regardless of the specific task to be performed on the quantized data. In systems working with high-dimensional signals, such as massive multiple-input multiple-output (MIMO) systems, it is beneficial to utilize low-resolution quantizers, due to cost, power, and memory constraints. In this work we study quantization of high-dimensional inputs, aiming at improving performance under resolution constraints by accounting for the system task in the quantizers design. We focus on the task of recovering a desired signal statistically related to the high-dimensional input, and analyze two quantization approaches: We first consider vector quantization, which is typically computationally infeasible, and characterize the optimal performance achievable with this approach. Next, we focus on practical systems which utilize hardware-limited scalar uniform analog-to-digital converters (ADCs), and design a task-based quantizer under this model. The resulting system accounts for the task by linearly combining the observed signal into a lower dimension prior to quantization. We then apply our proposed technique to channel estimation in massive MIMO networks. Our results demonstrate that a system utilizing low-resolution scalar ADCs can approach the optimal channel estimation performance by properly accounting for the task in the system design.