异构干扰信道中孪生网络（Siamese-Net）的选择性干扰抑制
Selective Interference Suppression of Siamese-Net in Heterogeneous Interference Channels
浏览论文内容
中文总结 AI 辅助
该研究针对异构干扰信道，探究SiameseNet选择性抑制干扰的能力，通过4用户不等干扰配置实验，证实其可在正交性与编码增益间实现最优权衡。
中文摘要 AI 辅助
我们研究了针对N用户实高斯干扰信道的端到端学习短块码，该信道具有异构成对干扰强度，同时每个接收机保持单用户解码。本文研究仅存在少数主导干扰源的情况，探究孪生式耦合训练能否选择性地调整编码与解码，以确保在正交性与编码增益间实现最佳权衡的最优性能，或是会忽视实际情况而强制实施不必要的全局正交性。我们聚焦于4用户不等干扰配置，其中存在一对强度为Λ的主导用户对(1,2)和一对具有弱背景耦合（强度为λ）的弱用户对，通过此配置我们展示了选择性干扰抑制现象：学习到的码本主要针对主导用户对接近正交，而弱耦合用户对则保留编码增益所需的对齐。我们使用潜在空间跨用户相似度统计（最坏情况相干性度量、平均相似度度量等）量化此行为，并将这些几何特征与观测到的不等干扰下的BLER鲁棒性关联起来。结果表明，SiameseNet选择性抑制了各干扰用户对的干扰，以实现由正交性决定的编码增益与BLER间的最优权衡。
英文摘要
We study an end-to-end learnt short-block codes for a $N$-user real Gaussian interference channel with heterogeneous pairwise interference strengths, while keeping single-user decoding at every receiver. In this paper, we study the case wherein only a few dominant interferers exist and investigate whether Siamese-style coupled training can adapt selectively to encode (\& decode) to ensure optimal performance corresponding to best tradeoff between orthogonality and coding gain or it enforces unnecessary global orthogonality oblivious of the reality. Our work focuses on a 4-user unequal-interference configuration with one dominant pair $(1,2)$ (of strength $Λ$) and a weak pair (with weak background coupling ($λ$)), through which we demonstrate a selective interference suppression phenomenon where the learned codebooks become near-orthogonal primarily for the dominant pair, while weakly coupled pairs retain alignment needed for coding gain. We quantify this behaviour using latent-space cross-user similarity statistics (worst-case coherence measure, average similarity measure, etc) and connect these geometric signatures to the observed BLER robustness under unequal interference. It seems that the SiameseNet selectively suppresses the interferences from various interferring user pairs to yield optimal tradeoff between coding gain and BLER dictated by orthogonality.
