Learning Centric Power Allocation for Edge Intelligence

TL;DR

This paper introduces a learning-centric power allocation method for edge intelligence that optimizes resource distribution based on learning performance rather than traditional channel quality metrics.

Contribution

It proposes a novel power allocation approach that considers empirical classification error models, improving learning performance in edge intelligence systems.

Findings

LCPA outperforms traditional algorithms in experiments.

Transmit powers are inversely proportional to channel gain.

Power scales exponentially with learning parameters.

Abstract

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.