English

Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation

Machine Learning 2019-05-20 v1 Networking and Internet Architecture Machine Learning

Abstract

In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task. More specifically, we restrict our attention to how the source should quantize information so that the destination can maximize a utility function which represents the task to be executed only knowing the quantized information. For example, for utility functions under the form u(x; g)u\left(\boldsymbol{x};\ \boldsymbol{g}\right), x\boldsymbol{x} might represent a decision in terms of using some radio resources and g\boldsymbol{g} the system state which is only observed through its quantized version Q(g)Q(\boldsymbol{g}). Both in the case where the utility function is known and the case where it is only observed through its realizations, we provide solutions to determine such a quantizer. We show how this approach applies to energy-efficient power allocation. In particular, it is seen that quantizing the state very roughly is perfectly suited to sum-rate-type function maximization, whereas energy-efficiency metrics are more sensitive to imperfections.

Keywords

Cite

@article{arxiv.1905.07339,
  title  = {Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation},
  author = {Hang Zou and Chao Zhang and Samson Lasaulce and Lucas Saludjian and Patrick Panciatici},
  journal= {arXiv preprint arXiv:1905.07339},
  year   = {2019}
}
R2 v1 2026-06-23T09:10:56.804Z