A BART-based semantic error correction and list decoding framework for short block codes achieves 0.4-0.8 dB BLER gains and up to 90% lower latency than conventional short or long codes for sentence transmission.
Semantic Pilot Design for Data-Aided Channel Estimation Using a Large Language Model
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abstract
This paper proposes a semantic pilot design for data-aided channel estimation in text-inclusive data transmission, using a large language model (LLM). In this scenario, channel impairments often appear as typographical errors in the decoded text, which can be corrected using an LLM. The proposed method compares the initially decoded text with the LLM-corrected version to identify reliable decoded symbols. A set of selected symbols, referred to as a semantic pilot, is used as an additional pilot for data-aided channel estimation. To the best of our knowledge, this work is the first to leverage semantic information for reliable symbol selection. Simulation results demonstrate that the proposed scheme outperforms conventional pilot-only estimation, achieving lower normalized mean squared error and phase error of the estimated channel, as well as reduced bit error rate.
years
2026 2representative citing papers
A semantic pilot design uses LLM-based error correction to select reliable decoded symbols for data-aided channel estimation, showing lower NMSE, phase error, and BER than pilot-only methods in simulations.
citing papers explorer
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Semantic Error Correction and Decoding for Short Block Codes
A BART-based semantic error correction and list decoding framework for short block codes achieves 0.4-0.8 dB BLER gains and up to 90% lower latency than conventional short or long codes for sentence transmission.
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Semantic Pilot Design for Data-Aided Channel Estimation Using a Large Language Model
A semantic pilot design uses LLM-based error correction to select reliable decoded symbols for data-aided channel estimation, showing lower NMSE, phase error, and BER than pilot-only methods in simulations.