Semantic-Aided Iterative Decoding for Uplink Non-Orthogonal Transmission

2026-08-07

AI Summary

This work addresses the challenges of interference cancellation and reliable decoding in uplink non-orthogonal multiple access (NOMA) with multi-user joint transmission by proposing an iterative decoding framework that integrates semantic information with physical-layer processing. The proposed approach uniquely embeds a fine-tuned ByT5 byte-level language model into the NOMA iterative loop, leveraging semantic prefixes from successfully decoded users to predict byte-wise posteriors for users yet to converge. These predictions are incorporated via convex combination into the iterative process involving LDPC decoding and the elementary signal estimator (ESE). Experimental results demonstrate that, at a signal-to-noise ratio of 8 dB, the proposed scheme reduces block error rate by an order of magnitude compared to conventional NOMA, significantly outperforming both TDMA and NOMA baselines without semantic feedback.

Abstract

This paper proposes semantic-aided iterative decoding (Sem-IR) for uplink non-orthogonal transmission of a shared natural-language source. K users each hold one segment of a common sentence and superimpose low-density parity-check (LDPC) coded transmissions over an additive white Gaussian noise (AWGN) channel. At the base station, an iterative elementary signal estimator (ESE) and K parallel LDPC decoders progressively cancel inter-user interference. As high-power users pass both parity and language-plausibility checks earlier, their decoded bytes form a reliable linguistic prefix for the remaining users; a fine-tuned ByT5 byte-level language model exploits this prefix to predict byte posteriors for the unconverged user. The byte posteriors are marginalized to bit-level log-likelihood ratios and convex-combined with the LDPC posteriors inside the iterative loop. The resulting feedback closes the loop between the language model and the physical-layer iteration. Simulations show that Sem-IR outperforms orthogonal time-division access (TDMA) and the same NOMA receiver without semantic feedback in block error rate (BLER), yielding an order-of-magnitude reduction over NOMA at 8 dB.

Problem

• non-orthogonal transmission
• semantic decoding
• inter-user interference
• natural-language source
• block error rate

Innovation

• semantic-aided decoding
• non-orthogonal multiple access
• language model feedback
• iterative interference cancellation
• byte-level semantics