Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective

2025-06-14

AI Summary

Interference is ubiquitous in communication systems, yet receivers often employ suboptimal decoding due to limited channel knowledge or high computational complexity—rendering conventional capacity and mutual information metrics inadequate for characterizing practical performance. To address this, we establish a precise performance evaluation framework for interference-limited scenarios based on mismatched decoding theory. Specifically, we are the first to incorporate the generalized mutual information (GMI) into the bit-interleaved coded modulation (BICM) demodulation process, demonstrating its high accuracy in throughput prediction. We further propose an interference-resilient decoding metric and develop a GMI-driven joint precoding design for multi-user multiple-input single-output (MU-MISO) systems. The derived matched/mismatched capacity bounds, validated via simulations, show that our framework significantly improves spectral efficiency and assessment reliability under interference. This work establishes a new design paradigm for BICM and multi-antenna systems operating under non-ideal decoding conditions.

Technology Category

• Machine Learning: Information Theory
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Interference widely exists in communication systems and is often not optimally treated at the receivers due to limited knowledge and/or computational burden. Evolutions of receivers have been proposed to balance complexity and spectral efficiency, for example, for 6G, while commonly used performance metrics, such as capacity and mutual information, fail to capture the suboptimal treatment of interference, leading to potentially inaccurate performance evaluations. Mismatched decoding is an information-theoretic tool for analyzing communications with suboptimal decoders. In this work, we use mismatched decoding to analyze communications with decoders that treat interference suboptimally, aiming at more accurate performance metrics. Specifically, we consider a finite-alphabet input Gaussian channel under interference, representative of modern systems, where the decoder can be matched (optimal) or mismatched (suboptimal) to the channel. The matched capacity is derived using Mutual Information (MI), while a lower bound on the mismatched capacity under various decoding metrics is derived using the Generalized Mutual Information (GMI). We show that the decoding metric in the proposed channel model is closely related to the behavior of the demodulator in Bit-Interleaved Coded Modulation (BICM) systems. Simulations illustrate that GMI/MI accurately predicts the throughput performance of BICM-type systems. Finally, we extend the channel model and the GMI to multiple antenna cases, with an example of multi-user multiple-input-single-output (MU-MISO) precoder optimization problem considering GMI under different decoding strategies. In short, this work discovers new insights about the impact of interference, proposes novel receivers, and introduces a new design and performance evaluation framework that more accurately captures the effect of interference.

Problem

• Analyzing suboptimal interference treatment in communication decoders
• Deriving accurate performance metrics using mismatched decoding theory
• Extending channel models to multi-antenna systems with GMI

Innovation

• Uses mismatched decoding for suboptimal interference analysis
• Derives capacity bounds with Generalized Mutual Information
• Extends model to multi-antenna and MU-MISO optimization