用自编码器设计抗干扰通信模型，性能优于传统正交方法。
Learning Robust Representations for Communications over Interference-limited Channels
- 设计TwinNet和SiameseNet双模型，利用干扰结构优化编码解码。
- 在干扰受限场景下，性能超越依赖完全正交的传统方法。
- 适合研究无线通信鲁棒表征与数据驱动信道设计的学者。
在蜂窝网络中，小区边缘用户易受邻区强烈干扰，可建模为两用户干扰信道。本文提出两种基于自编码器的有效方法——TwinNet和SiameseNet，专用于块传输与检测中的编码器和解码器设计。结果明确表明，所提模型能有效利用干扰结构，性能优于依赖完全正交的传统方法。尽管协作传输与独立检测系统具备更高容量，但数据驱动模型的具体增益尚未被充分量化与阐释。本文通过分析揭示了此类模型在特定场景下的可量化优势。此外，对模型生成的码字特征进行了全面考察，以更直观理解其性能提升机制。
原文摘要 · Abstract (English)
In the context of cellular networks, users located at the periphery of cells are particularly vulnerable to substantial interference from neighbouring cells, which can be represented as a two-user interference channel. This study introduces two highly effective methodologies, namely TwinNet and SiameseNet, using autoencoders, tailored for the design of encoders and decoders for block transmission and detection in interference-limited environments. The findings unambiguously illustrate that the developed models are capable of leveraging the interference structure to outperform traditional methods reliant on complete orthogonality. While it is recognized that systems employing coordinated transmissions and independent detection can offer greater capacity, the specific gains of data-driven models have not been thoroughly quantified or elucidated. This paper conducts an analysis to demonstrate the quantifiable advantages of such models in particular scenarios. Additionally, a comprehensive examination of the characteristics of codewords generated by these models is provided to offer a more intuitive comprehension of how these models achieve superior performance.
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