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DeepIC plus : Learning Codes for Interference Channels
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PRE
AI
DOI：10.1109/TWC.2023.3302617.png)
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Abstract
En 中文
A two-user interference channel is a canonical model for multiple one-to-one communications, where two transmitters wish to communicate with their receivers via a shared medium, examples of which include pairs of base stations and handsets near the cell boundary that suffer from interference. Practical codes and the fundamental limit of communications are unknown for interference channels as mathematical analysis becomes intractable. Hence, simple heuristic coding schemes are used in practice to mitigate interference, e.g., time division, treating interference as noise, and successive interference cancellation. These schemes are nearly optimal for extreme cases: when interference is strong or weak. However, there is no optimality guarantee for channels with moderate interference. Here we combine deep learning and network information theory to overcome the limitation on the tractability of analysis and construct finite-blocklength coding schemes for channels with various interference levels. We show that carefully designed and trained neural codes using network information theoretic insight can achieve several orders of reliability improvement for channels with moderate interference. Furthermore, we present the interpretation of the learned codes based on the codeword distance and the Centered Kernel Alignment (CKA) analysis.
Keywords:
Deep learning for channel coding
interference channels
turbo autoencoder
interpretable ML
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W
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