Hierarchical Resource Allocation: Balancing Throughput and Energy Efficiency in Wireless Systems

TL;DR

This paper introduces a hierarchical resource allocation method for wireless systems that prioritizes energy efficiency while maintaining near-optimal throughput, significantly reducing power consumption with minimal throughput loss.

Contribution

It proposes a novel hierarchical approach to resource allocation that effectively balances energy efficiency and throughput in wireless networks.

Findings

Reduces transmit power by 65% in multi-cell scenarios

Achieves only 5% decrease in throughput

Saves energy even when global efficiency maximization fails

Abstract

A main challenge of 5G and beyond wireless systems is to efficiently utilize the available spectrum and simultaneously reduce the energy consumption. From the radio resource allocation perspective, the solution to this problem is to maximize the energy efficiency instead of the throughput. This results in the optimal benefit-cost ratio between data rate and energy consumption. It also often leads to a considerable reduction in throughput and, hence, an underutilization of the available spectrum. Contemporary approaches to balance these metrics based on multi-objective programming theory often lack operational meaning and finding the correct operating point requires careful experimentation and calibration. Instead, we propose the novel concept of hierarchical resource allocation where conflicting objectives are ordered by their importance. This results in a resource allocation algorithm that strives to minimize the transmit power while keeping the data rate close the maximum achievable throughput. In a typical multi-cell scenario, this strategy is shown to reduces the transmit power consumption by 65% at the cost of a 5% decrease in throughput. Moreover, this strategy also saves energy in scenarios where global energy efficiency maximization fails to achieve any gain over throughput maximization.