Unified Space-Time-Message Interference Alignment: An End-to-End Learning Approach.

Area of Science:

• Wireless communication systems
• Information theory
• Machine learning for signal processing

Background:

• Multi-user MISO broadcast channels face performance degradation due to imperfect channel state information at the transmitter (CSIT).
• Conventional interference alignment (IA) techniques struggle with successive interference cancellation (SIC) error propagation under CSI uncertainty.
• Existing methods like rate-splitting multiple access (RSMA) have limitations in practical, non-ideal CSIT scenarios.

Purpose of the Study:

• To develop a novel deep learning framework, Deep-STMIA, for robust interference management in MU-MISO broadcast channels.
• To address the limitations of conventional IA and SIC methods under imperfect, delayed, and quantized CSIT.
• To adaptively mitigate error propagation and optimize performance across various CSIT conditions.

Main Methods:

• Proposing Deep-STMIA, an end-to-end deep learning framework utilizing a neural network-based autoencoder.
• Implementing structural message-domain regularization for joint optimization of space, time, and message domains.
• Evaluating performance against benchmarks and state-of-the-art methods under diverse CSIT imperfections.

Main Results:

• Deep-STMIA demonstrates performance matching degrees-of-freedom (DoF) optimal benchmarks in extreme CSIT regimes.
• The framework significantly outperforms rate-splitting multiple access (RSMA) in practical imperfect CSIT scenarios.
• Deep-STMIA effectively mitigates error propagation caused by CSI uncertainty and quantization.

Conclusions:

• Deep-STMIA offers a powerful, adaptive solution for interference management in MU-MISO systems with practical CSIT constraints.
• The deep learning approach provides superior performance and robustness compared to traditional methods.
• This framework advances the practical deployment of efficient wireless communication systems.