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Model-Driven Distributed WMMSE for Downlink Massive MIMO Systems
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DOI：10.1109/LWC.2025.3600415.png)
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Abstract
En 中文
Distributed signal processing based on the decentralized architecture plays an important role in the next generation of wireless communications. In this letter, we propose a model-driven distributed WMMSE algorithm (MDD-WMMSE) for downlink massive MIMO systems. The proposed MDD-WMMSE is model-driven, which is designed based on a distributed version of the traditional WMMSE algorithm. Specifically, each distributed unit (DU) in it has a local private network and calculates in parallel, while the related distributed training is achieved by simply exchanging the local information. Simulation results show that the proposed MDD-WMMSE achieves competitive performance compared to the centralized WMMSE algorithm but with much lower complexity.
Keywords:
Distributed precoding
massive MIMO
deep unfolding
distributed learning
Journal
I
IF:
5.5
Papers:
673
Citations:
0