Joint Sum Rate And Error Probability Optimization: Finite Blocklength Analysis

TL;DR

This paper investigates the tradeoff between sum rate and error probability in wireless networks with finite blocklength codes, proposing an optimization algorithm to enhance throughput under delay constraints.

Contribution

It introduces a joint optimization framework for sum rate and error probability in finite blocklength regimes, with an efficient divide-and-conquer algorithm.

Findings

Optimizing per-user error probability improves throughput in delay-constrained scenarios.

The proposed algorithm effectively balances sum rate and error probability.

Finite blocklength code analysis impacts system performance evaluation.

Abstract

We study the tradeoff between the sum rate and the error probability in downlink of wireless networks. Using the recent results on the achievable rates of finite-length codewords, the problem is cast as a joint optimization of the network sum rate and the per-user error probability. Moreover, we develop an efficient algorithm based on the divide-and-conquer technique to simultaneously maximize the network sum rate and minimize the maximum users' error probability and to evaluate the effect of the codewords length on the system performance. The results show that, in delay-constrained scenarios, optimizing the per-user error probability plays a key role in achieving high throughput.