An Unsupervised Deep Unfolding Framework for Robust Symbol Level Precoding

• Main Authors: Andreopoulos, Y. (Author), Masouros, C. (Author), Mohammad, A. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: Array signal processing; Constructive Interference; Deep Neural Networks; Downlink; downlink beamforming; Interference; Minimization; Optimization; power minimization; Precoding; Symbol level precoding; Symbols
• Online Access: View Fulltext in Publisher; View in Scopus

 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(n7.5) to O(n3) for the symmetrical system case where n=numberoftransmitantennas=numberofusers. 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. Author