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

arXiv: https://arxiv.org/abs/1905.07339 · HTML: https://mlsanity.com/paper/1905.07339/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 2 of 2)

- [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022)
- [Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling](https://mlsanity.com/paper/2106.01021/) (2021)

## Abstract (arXiv)

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 uleft(boldsymbolx; boldsymbolgright), boldsymbolx might represent a decision in terms of using some radio resources and boldsymbolg the system state which is only observed through its quantized version Q(boldsymbolg). 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.