Low-Latency Communication with Computational Complexity Constraints

TL;DR

This paper emphasizes the importance of including decoding time, modeled via per-bit complexity, in low-latency communication analysis, revealing its impact on optimal system parameter choices.

Contribution

It introduces an empirical model for decoding complexity-performance trade-offs and demonstrates its significance in latency optimization for wireless networks.

Findings

Decoding time significantly affects total latency calculations.

Including decoding complexity alters optimal parameter selection.

The empirical model accurately predicts decoding time based on code performance.

Abstract

Low-latency communication is one of the most important application scenarios in next-generation wireless networks. Often in communication-theoretic studies latency is defined as the time required for the transmission of a packet over a channel. However, with very stringent latency requirements and complexity constrained receivers, the time required for the decoding of the packet cannot be ignored and must be included in the total latency analysis through accurate modeling. In this paper, we first present a way to calculate decoding time using \textit{per bit} complexity metric and introduce an empirical model that accurately describes the trade-off between the decoding complexity versus the performance of state-of-the-art codes. By considering various communication parameters, we show that including the decoding time in latency analyses has a significant effect on the optimum selection of parameters.