Power minimization and resource allocation in HetNets with uncertain channel-gains

TL;DR

This paper introduces a robust optimization framework for power minimization and resource allocation in HetNets, accounting for uncertain channel gains modeled as log-normal distributions, validated through real-world city data.

Contribution

It formulates a novel chance-constrained mixed-integer optimization problem and transforms it into a robust geometric program for efficient power and resource management.

Findings

Effective power reduction in HetNets under channel uncertainty

Robust optimization maintains user throughput demands

Validated approach in a real-world urban scenario

Abstract

We propose an optimization problem to minimize the base stations transmission powers in OFDMA heterogeneous networks, while respecting users' individual throughput demands. The decision variables are the users' working bandwidths, their association, and the base stations transmission powers. To deal with wireless channel uncertainty, the channel gains are treated as random variables respecting a log-normal distribution, leading to a non-convex chance constrained mixed-integer optimization problem, which is then formulated as a mixed-integer Robust Geometric Program. The efficacy of the proposed method is shown in a real-world scenario of a large European city.