Blind Identification of Channel Codes: A Subspace-Coding Approach

2026-01-22

AI Summary

This study addresses the problem of blindly identifying the type of channel code selected from a known code family when only the channel output codewords are observed. To this end, the authors propose a novel identification method based on a subspace coding framework, introducing for the first time the concept of subspace coding into blind channel code recognition. The approach integrates Hamming distance and subspace distance into a unified discrimination mechanism and employs a minimum denoising subspace discrepancy decoder. The method is applicable to unstructured random linear codes and demonstrates significant performance advantages over existing general-purpose techniques—even with limited received vectors—under binary symmetric channels and various other channel conditions. Moreover, it provides rigorous theoretical performance guarantees.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Semi-Supervised Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• blind identification
• channel codes
• code-family
• binary symmetric channel
• subspace codes

Innovation

• blind identification
• subspace coding
• minimum denoised subspace discrepancy
• binary symmetric channel
• random linear codes