Decision Set Optimization and Energy-Efficient MIMO Communications

TL;DR

This paper introduces a novel framework for optimizing finite decision sets in energy-efficient MIMO communications, reducing feedback rates and improving energy performance through a combined evolutionary algorithm approach.

Contribution

It presents a new method for designing decision sets that minimize performance loss and enhance energy efficiency in MIMO systems with finite feedback.

Findings

Feedback rate reduced by 50% with optimized decision sets

Energy efficiency significantly improved using the proposed algorithm

Decision set design leads to notable performance gains

Abstract

Assuming that the number of possible decisions for a transmitter (e.g., the number of possible beamforming vectors) has to be finite and is given, this paper investigates for the first time the problem of determining the best decision set when energy-efficiency maximization is pursued. We propose a framework to find a good (finite) decision set which induces a minimal performance loss w.r.t. to the continuous case. We exploit this framework for a scenario of energy-efficient MIMO communications in which transmit power and beamforming vectors have to be adapted jointly to the channel given under finite-rate feedback. To determine a good decision set we propose an algorithm which combines the approach of Invasive Weed Optimization (IWO) and an Evolutionary Algorithm (EA). We provide a numerical analysis which illustrates the benefits of our point of view. In particular, given a performance loss level, the feedback rate can by reduced by 2 when the transmit decision set has been designed properly by using our algorithm. The impact on energy-efficiency is also seen to be significant.