# Task Oriented Channel State Information Quantization

## TL;DR

This paper introduces a task-oriented approach to quantizing channel state information (CSI) that optimizes feedback for decision-making, using analytical solutions for specific cases and neural networks for general utility functions, improving compression efficiency.

## Contribution

It presents a novel task-oriented CSI quantization framework, including an analytical solution for energy-efficient power control and a neural network-based method for broader utility functions.

## Key findings

- Optimal task-oriented CSI quantizer can be derived analytically for specific cases.
- Neural networks effectively learn quantization for general utility functions.
- Adaptive feedback rate improves compression efficiency significantly.

## Abstract

In this paper, we propose a new perspective for quantizing a signal and more specifically the channel state information (CSI). The proposed point of view is fully relevant for a receiver which has to send a quantized version of the channel state to the transmitter. Roughly, the key idea is that the receiver sends the right amount of information to the transmitter so that the latter be able to take its (resource allocation) decision. More formally, the decision task of the transmitter is to maximize an utility function u(x;g) with respect to x (e.g., a power allocation vector) given the knowledge of a quantized version of the function parameters g. We exhibit a special case of an energy-efficient power control (PC) problem for which the optimal task oriented CSI quantizer (TOCQ) can be found analytically. For more general utility functions, we propose to use neural networks (NN) based learning. Simulations show that the compression rate obtained by adapting the feedback information rate to the function to be optimized may be significantly increased.