BICM Performance Improvement via Online LLR Optimization

TL;DR

This paper proposes an online adaptive LLR scaling algorithm to enhance BICM receiver performance by maximizing GMI, effectively adapting to time-varying channels and improving achievable rates in mobile scenarios.

Contribution

It introduces a practical online LLR scaling method that dynamically optimizes BICM performance without requiring complex look-up tables for each scenario.

Findings

Significant increase in achievable rates with the proposed method

Effective adaptation to time-varying channel conditions

Improved link-level performance in mobile transmission scenarios

Abstract

We consider bit interleaved coded modulation (BICM) receiver performance improvement based on the concept of generalized mutual information (GMI). Increasing achievable rates of BICM receiver with GMI maximization by proper scaling of the log likelihood ratio (LLR) is investigated. While it has been shown in the literature that look-up table based LLR scaling functions matched to each specific transmission scenario may provide close to optimal solutions, this method is difficult to adapt to time-varying channel conditions. To solve this problem, an online adaptive scaling factor searching algorithm is developed. Uniform scaling factors are applied to LLRs from different bit channels of each data frame by maximizing an approximate GMI that characterizes the transmission conditions of current data frame. Numerical analysis on effective achievable rates as well as link level simulation of realistic mobile transmission scenarios indicate that the proposed method is simple yet effective.