Constrained Resource Allocation Problems in Communications: An Information-assisted Approach

TL;DR

This paper introduces two dynamic programming algorithms enhanced with information theory to efficiently solve constrained resource allocation problems in communications, achieving optimal solutions with reduced computational effort.

Contribution

The paper presents novel algorithms that combine dynamic programming with information-theoretic measures to efficiently solve NP-hard resource allocation problems in wireless communications.

Findings

Algorithms guarantee optimal solutions under specific conditions.

Significant reduction in computational resources required.

Successful application to 5G MIMO bit allocation problem.

Abstract

We consider a class of resource allocation problems given a set of unconditional constraints whose objective function satisfies Bellman's optimality principle. Such problems are ubiquitous in wireless communication, signal processing, and networking. These constrained combinatorial optimization problems are, in general, NP-Hard. This paper proposes two algorithms to solve this class of problems using a dynamic programming framework assisted by an information-theoretic measure. We demonstrate that the proposed algorithms ensure optimal solutions under carefully chosen conditions and use significantly reduced computational resources. We substantiate our claims by solving the power-constrained bit allocation problem in 5G massive Multiple-Input Multiple-Output receivers using the proposed approach.