# On Provisioning Cellular Networks for Distributed Inference

## TL;DR

This paper develops an analytical framework to understand how cellular network design impacts distributed ML inference accuracy at the network edge, highlighting the importance of access point density and bandwidth.

## Contribution

It introduces a novel analytical model linking cellular network parameters to distributed inference accuracy, guiding network provisioning for edge ML applications.

## Key findings

- Minimum AP density is needed for target inference accuracy.
- Asymptotic accuracy is limited by air-interface bandwidth.
- Edge inference accuracy depends inversely on AP density and bandwidth.

## Abstract

Wireless traffic attributable to machine learning (ML) inference workloads is increasing with the proliferation of applications and smart wireless devices leveraging ML inference. Owing to limited compute capabilities at these "edge" devices, achieving high inference accuracy often requires coordination with a remote compute node or "cloud" over the wireless cellular network. The accuracy of this distributed inference is, thus, impacted by the communication rate and reliability offered by the cellular network. In this paper, an analytical framework is proposed to characterize inference accuracy as a function of cellular network design. Using the developed framework, it is shown that cellular network should be provisioned with a minimum density of access points (APs) to guarantee a target inference accuracy, and the inference accuracy achievable at asymptotically high AP density is limited by the air-interface bandwidth. Furthermore, the minimum accuracy required of edge inference to deliver a target inference accuracy is shown to be inversely proportional to the density of APs and the bandwidth.