Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation
Hang Zou, Chao Zhang, Samson Lasaulce, Lucas Saludjian, Vincent, Poor

TL;DR
This paper develops a goal-oriented quantization framework for resource allocation and other applications, optimizing quantizer design to minimize performance loss due to parameter quantization.
Contribution
It introduces a high-resolution analysis-based method for designing quantizers tailored to specific goal functions, with practical algorithms and insights into quantization difficulty.
Findings
Quantizer design reduces performance gap in resource allocation tasks.
High-resolution analysis guides effective quantizer construction.
Significant improvements shown in numerical experiments.
Abstract
In this paper, the situation in which a receiver has to execute a task from a quantized version of the information source of interest is considered. The task is modeled by the minimization problem of a general goal function for which the decision has to be taken from a quantized version of the parameters . This problem is relevant in many applications e.g., for radio resource allocation (RA), high spectral efficiency communications, controlled systems, or data clustering in the smart grid. By resorting to high resolution (HR) analysis, it is shown how to design a quantizer that minimizes the gap between the minimum of (which would be reached by knowing perfectly) and what is effectively reached with a quantized . The conducted formal analysis both provides quantization strategies in the HR regime and insights for the general regime and allows a practical…
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Taxonomy
TopicsAdvanced Wireless Network Optimization · Energy Harvesting in Wireless Networks · Distributed Sensor Networks and Detection Algorithms
