The problem of blind identification of channel codes at a receiver involves identifying a code chosen by a transmitter from a known code-family, by observing the transmitted codewords through the channel. Most existing approaches for code-identification are contingent upon the codes in the family having some special structure, and are often computationally expensive otherwise. Further, rigorous analytical guarantees on the performance of these existing techniques are largely absent. This work presents a new method for code-identification on the binary symmetric channel (BSC), inspired by the framework of subspace codes for operator channels, carefully combining principles of hamming-metric and subspace-metric decoding. We refer to this method as the minimum denoised subspace discrepancy decoder. We present theoretical guarantees for code-identification using this decoder, for bounded-weight errors, and also present a bound on the probability of error when used on the BSC. Simulations demonstrate the improved performance of our decoder for random linear codes beyond existing general-purpose techniques, across most channel conditions and even with a limited number of received vectors.

翻译：接收端信道码盲识别问题涉及通过信道观测传输码字，从已知码族中识别出发送端所选用的编码方案。现有大多数码识别方法依赖于码族中码字具有特定结构，否则通常计算代价高昂。此外，这些现有技术的性能分析缺乏严格的理论保证。本研究提出一种针对二进制对称信道（BSC）的码识别新方法，该方法受算子信道中子空间码框架启发，巧妙结合汉明度量和子空间度量解码原理。我们将此方法称为最小去噪子空间差异解码器。我们为该解码器在有限权重错误下的码识别提供了理论保证，并给出了在BSC信道中使用时的误码概率上界。仿真结果表明，在大多数信道条件下，即使接收向量数量有限，我们的解码器对随机线性码的识别性能也优于现有通用技术。