Proactive Resource Allocation with Predictable Channel Statistics

TL;DR

This paper introduces a predictive resource allocation method leveraging predictable user demand and channel statistics, outperforming reactive strategies and approaching fundamental cost bounds as service windows extend.

Contribution

It develops a novel predictive approach that jointly considers demand and channel patterns, achieving near-optimal cost bounds in resource allocation.

Findings

Predictive method outperforms reactive approaches in cost efficiency.

The approach attains a fundamental cost bound as the service window increases.

Numerical results show how uncertainty sources influence decisions and costs.

Abstract

The behavior of users in relatively predictable, both in terms of the data they request and the wireless channels they observe. In this paper, we consider the statistics of such predictable patterns of the demand and channel jointly across multiple users, and develop a novel predictive resource allocation method. This method is shown to provide performance benefits over a reactive approach, which ignores these patterns and instead aims to satisfy the instantaneous demands, irrespective of cost to the system. In particular, we show that our proposed method is able to attain a novel fundamental bound on the achievable cost, as the service window grows. Through numerical evaluation, we gain insights into how different uncertainty sources affect the decisions and the cost.