An Unsupervised Deep Unfolding Framework for Robust Symbol-Level Precoding
An Unsupervised Deep Unfolding Framework for Robust Symbol-Level Precoding
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DOI：
10.1109/ojcoms.2023.3270455
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发表时间：
2021-07
影响因子：
7.9
通讯作者：
A. Mohammad;C. Masouros;Y. Andreopoulos
中科院分区：
文献类型：
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作者：
A. Mohammad;C. Masouros;Y. Andreopoulos
符号级预编码(SLP)由于其能够利用干扰进行能量高效传输而引起了人们极大的研究兴趣。提出了一种基于无监督深度神经网络(DNN)的SLP框架。我们的建议不是天真地训练用于SLP的DNN体系结构而不考虑SLP域优化目标的细节，而是基于内点法(IPM)近端的LOG势垒函数展开功率最小化SLP公式。此外，我们将我们的建议扩展到在信道状态信息(CSI)不确定性下的稳健预编码设计。结果表明，对于$n=\Text{发送天线数}=\Text{用户数}的对称系统，我们提出的学习框架提供了接近最优的性能，同时将计算代价从$\mathcal{O}(n^{7.5})$降低到$\mathcal{O}(n^{3})$。这种显著的复杂性降低还反映在与基于SLP优化的解决方案相比，所提出的方法的执行时间成比例地减少。
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 $\mathcal{O}(n^{7.5})$ to $\mathcal{O}(n^{3})$ for the symmetrical system case where $n=\text {number of transmit antennas}=\text {number of users}$ . 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.