Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems
This work addresses power control challenges in wireless communication systems, offering a low-complexity solution that is incremental in applying deep learning to a specific domain.
The authors tackled power control optimization in uplink cell-free massive MIMO systems by proposing a deep learning framework using unsupervised learning, which achieved higher spectral efficiency than previous methods for max-min optimization and performed well for max-sum-rate and max-product optimizations.
In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed. Instead of using supervised learning, the proposed method relies on unsupervised learning, in which optimal power allocations are not required to be known, and thus has low training complexity. More specifically, a deep neural network (DNN) is trained to learn the map between fading coefficients and power coefficients within short time and with low computational complexity. It is interesting to note that the spectral efficiency of CF mMIMO systems with the proposed method outperforms previous optimization methods for max-min optimization and fits well for both max-sum-rate and max-product optimizations.