Belief-Adaptive MAP Detection for Molecular ISI Channels with Heteroscedastic Noise

2026-03-06

AI Summary

This work addresses the challenge of intersymbol interference (ISI) in molecular diffusion communication, which induces state-dependent heteroscedastic noise—a statistical characteristic inadequately modeled by existing detectors. To overcome this limitation, the paper proposes two decoding schemes, BA-MAP and Soft BA-MAP, which explicitly incorporate the ISI-state-dependent noise mean and variance into the detection process for the first time. By leveraging Bayesian posterior inference, these methods construct adaptive MAP decision thresholds and integrate soft information through hybrid log-likelihood ratio weighting. Evaluated under realistic molecular channel conditions, the proposed approaches achieve up to a 100% throughput gain compared to conventional fixed-threshold or equalization-based methods, while significantly reducing bit error rates and enhancing overall transmission efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: State Estimation
• Machine Learning: Information Theory

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Content-based information diffusion
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Inter-symbol interference (ISI) with heteroscedastic, or state-dependent, noise is a defining feature of molecular communication via diffusion (MCvD). However, such noise variance dependency across ISI states has not been systematically considered in prior detector designs. This letter introduces two decoding mechanisms, Belief-Adaptive Maximum A Posteriori (BA-MAP) and Soft BA-MAP, that explicitly incorporate state-dependent means and variances of the molecular count channel. The BA-MAP method derives per-symbol adaptive MAP thresholds based on the receiver's current state beliefs, whereas the Soft BA-MAP approach computes mixture log-likelihood ratios by weighting all possible ISI states. Simulation and information-theoretic analyses confirm that the proposed detectors outperform conventional equalization and fixed-threshold methods, achieving up to 100% throughput improvement under realistic MCvD settings.

Problem

• molecular communication
• inter-symbol interference
• heteroscedastic noise
• state-dependent noise
• diffusion-based communication

Innovation

• belief-adaptive detection
• heteroscedastic noise
• molecular communication
• MAP decoding
• inter-symbol interference