Finite Blocklength Analysis of Energy Harvesting Channels

TL;DR

This paper develops a unified finite blocklength analysis framework for energy harvesting channels, including AWGN and DMC, providing bounds on capacity and moderate deviation asymptotics for systems like IoT and sensor networks.

Contribution

It introduces a comprehensive framework for finite blocklength analysis of energy harvesting channels applicable to various channel types.

Findings

Achievability and converse bounds on channel capacity in finite blocklength regime.

Moderate deviation asymptotic bounds for energy harvesting channels.

Unified analysis applicable to AWGN and DMC models.

Abstract

We consider Additive White Gaussian Noise channels and Discrete Memoryless channels when the transmitter harvests energy from the environment. These can model wireless sensor networks as well as Internet of Things. By providing a unifying framework that works for any energy harvesting channel, we study these channels assuming an infinite energy buffer and provide the corresponding achievability and converse bounds on the channel capacity in the finite blocklength regime. We additionally provide moderate deviation asymptotic bounds as well.