Learning Robust Representations for Communications over Interference-limited Channels
This work addresses interference issues in cellular networks, offering incremental improvements for users in high-interference areas.
The paper tackles the problem of interference in cellular networks, particularly for users at cell edges, by introducing TwinNet and SiameseNet autoencoder-based methods for encoder and decoder design, showing they outperform traditional orthogonal methods by leveraging interference structure.
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.