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We consider a class of resource allocation problems given a set ofunconditional constraints whose objective function satisfies Bellman'soptimality principle. Such problems are ubiquitous in wireless communication,signal processing, and networking. These constrained combinatorial optimizationproblems are, in general, NP-Hard. This paper proposes two algorithms to solvethis class of problems using a dynamic programming framework assisted by aninformation-theoretic measure. We demonstrate that the proposed algorithmsensure optimal solutions under carefully chosen conditions and usesignificantly reduced computational resources. We substantiate our claims bysolving the power-constrained bit allocation problem in 5G massiveMultiple-Input Multiple-Output receivers using the proposed approach.

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