On the Transmission-Computation-Energy Tradeoff in Wireless and Fixed Networks

TL;DR

This paper introduces a unified framework analyzing the tradeoff between transmission, computation, and energy consumption in wireless and fixed networks, highlighting how coding complexity and data rate influence network design choices.

Contribution

It presents a novel analytical framework that accounts for exponential decoding complexity and demonstrates how data rate and computation energy impact optimal network configurations.

Findings

Stronger codes are preferable at low data rates.

Uncoded transmission is optimal at high data rates.

Multi-hopping benefits increase with higher data rates.

Abstract

In this paper, a framework for the analysis of the transmission-computation-energy tradeoff in wireless and fixed networks is introduced. The analysis of this tradeoff considers both the transmission energy as well as the energy consumed at the receiver to process the received signal. While previous work considers linear decoder complexity, which is only achieved by uncoded transmission, this paper claims that the average processing (or computation) energy per symbol depends exponentially on the information rate of the source message. The introduced framework is parametrized in a way that it reflects properties of fixed and wireless networks alike. The analysis of this paper shows that exponential complexity and therefore stronger codes are preferable at low data rates while linear complexity and therefore uncoded transmission becomes preferable at high data rates. The more the computation energy is emphasized (such as in fixed networks), the less hops are optimal and the lower is the benefit of multi-hopping. On the other hand, the higher the information rate of the single-hop network, the higher the benefits of multi-hopping. Both conclusions are underlined by analytical results.