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An Unsupervised Deep Unfolding Framework for Robust Symbol-Level Precoding
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DOI：10.1109/OJCOMS.2023.3270455.png)
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Symbol Level Precoding (SLP) has attracted significant research interest due to its ability to exploit interference for energy-efficient transmission. This paper proposes an unsupervised deep-neural network (DNN) based SLP framework. Instead of naively training a DNN architecture for SLP without considering the specifics of the optimization objective of the SLP domain, our proposal unfolds a power minimization SLP formulation based on the interior point method (IPM) proximal 'log' barrier function. Furthermore, we extend our proposal to a robust precoding design under channel state information (CSI) uncertainty. The results show that our proposed learning framework provides near-optimal performance while reducing the computational cost from O(n(7.5)) to O(n(3)) for the symmetrical system case where n = number of transmit antennas = number of users. This significant complexity reduction is also reflected in a proportional decrease in the proposed approach's execution time compared to the SLP optimization-based solution.
Keyword:
Precoding
Interference
Optimization
Symbols
Minimization
Downlink
Array signal processing
Symbol level precoding
constructive interference
downlink beamforming
power minimization
deep neural networks
期刊
I
IF:
4.3
论文数:
1.7K
被引数:
991
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