Belief-Adaptive MAP Detection for Molecular ISI Channels with Heteroscedastic Noise
This work provides a significant improvement in data throughput for molecular communication via diffusion systems by better handling the inherent heteroscedastic noise.
This paper addresses inter-symbol interference (ISI) with heteroscedastic noise in molecular communication via diffusion (MCvD) channels. The proposed Belief-Adaptive MAP (BA-MAP) and Soft BA-MAP detectors explicitly account for state-dependent noise, leading to up to 100% throughput improvement compared to conventional methods.
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.