Closed-Loop Bayesian Bandit Encoder with GRAND Receiver for a Bursty Interference Channel

2026-07-16

AI Summary

This work addresses the adaptive selection between interleaved and non-interleaved coding in bursty interference channels by proposing a channel state–aware learning-based transmission strategy. The receiver employs a GRAND decoder to estimate interference parameters and feeds back statistical information, while the transmitter dynamically switches coding modes using Bayesian estimation combined with discounted Thompson sampling. Innovatively integrating Bayesian multi-armed bandit learning with GRAND decoding under a replaceable noise model, the approach models bit-flip probabilities via a hidden Markov model to optimize query ordering and introduces confidence-weighted pseudo-observations to accelerate convergence. Experiments demonstrate that the proposed method reduces block error rates by nearly an order of magnitude compared to ORBGRAND, achieves up to a 4.5× reduction in pre-convergence error rates with partial channel knowledge, cuts suboptimal selections by approximately 65% through pseudo-observations, and attains learning transients within mere milliseconds of over-the-air time.

Abstract

Interleaving mitigates burst errors but introduces decoding delay and removes temporal error structure that a channel-aware decoder could exploit. We consider packet-level selection between a random linear code and the same code used with cross-codeword interleaving, over a channel with an unknown number of on/off interferers. The receiver uses Guessing Random Additive Noise Decoding (GRAND) with a replaceable noise model and feeds aggregate channel statistics back to a Bayesian estimator at the transmitter. Once the interference amplitudes and timing parameters are estimated, the receiver's noise model is replaced: it computes hidden-Markov-model posterior bit-flip probabilities and uses them to order GRAND queries. A discounted Thompson sampler selects between the two transmission modes using a goodput-minus-latency reward whose distribution is endogenously nonstationary: receiver adaptation, rather than channel change, alters the value of each mode. Across five simulation seeds, the interleaved mode is preferred before channel estimation converges. After the learned decoder is activated, the non-interleaved mode becomes preferable because it achieves lower block error rate without interleaving delay. In the reference configuration, the learned noise model reduces block error rate by approximately one order of magnitude relative to ORBGRAND. Using partial channel estimates before full convergence reduces pre-convergence block error rate by up to $4.5\times$. Adding model-predicted utilities as confidence-weighted pseudo-observations reduces post-transition selection of the inferior arm by approximately $65\%$. Under an idealized airtime conversion at a 100~MHz 5G~NR-like symbol rate, the learning transient corresponds to a few milliseconds of occupied symbol time.

Problem

• bursty interference
• interleaving delay
• channel estimation
• block error rate
• nonstationary reward

Innovation

• Bayesian Bandit
• GRAND decoding
• bursty interference
• adaptive interleaving
• non-stationary reward