Blind Recognition of Intercepted Limited BCH and RS Codes Bitstream Using Single-Error Correction Under Harsh Channel Environments
This paper proposes a full-blind recognition method that aims to recognize coding parameters from a noisy bitstream of limited BCH and RS codes intercepted in a non-cooperative communication system under harsh channel environments. This paper optimizes the codeword reliability calculation by fully utilizing channel soft information, based on which the single-bit error codewords can be effectively selected from the codeword stream with a high probability. Subsequently, the channel soft information-based single-error correction (CSI-SEC) algorithm is proposed for correcting single-bit error codewords. The blind recognition algorithm, based on the Galois domain Fourier transform (GFFT) with a dual decision-making mechanism, is applied to these corrected codewords and reliable codewords to extract the encoding parameters. Finally, the method’s superiority is demonstrated through comprehensive simulation experiments. Compared to other blind recognition methods based on error correction, the proposed method exhibits enhanced recognition performance, with a cumulative computational complexity of only 39.8% of that of the other methods. Compared to other algorithms for blind recognition of limited intercepted bitstream, the proposed method achieves at least a 3 dB improvement in recognition performance.
Authors
- Zhuolun Wu (ORCID: https://orcid.org/0000-0002-5205-783X)
- Yanyan Liu (ORCID: https://orcid.org/0000-0001-8488-5480)
- Wei Zhang (ORCID: https://orcid.org/0000-0002-2601-3198)
- Hao Wang (ORCID: https://orcid.org/0000-0002-8157-2996)
- Yihan Wang (ORCID: https://orcid.org/0009-0009-7323-0994)
Institutions
- Tianjin University (CN)
- Nankai University (CN)
Publication Details
- Journal
- IEEE Transactions on Communications
- Published
- 2025-08-20
- DOI
- https://doi.org/10.1109/tcomm.2025.3600549
- Citations
- 1
- Primary Topic
- Error Correcting Code Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.49