Deep Learning Power Allocation in Massive MIMO
This addresses power allocation efficiency for wireless communication systems, but it is incremental as it applies deep learning to an existing optimization problem.
The paper tackles power allocation in Massive MIMO networks by using deep learning to predict optimal policies based on user positions, achieving near-optimal performance with reduced complexity compared to traditional methods.
This work advocates the use of deep learning to perform max-min and max-prod power allocation in the downlink of Massive MIMO networks. More precisely, a deep neural network is trained to learn the map between the positions of user equipments (UEs) and the optimal power allocation policies, and then used to predict the power allocation profiles for a new set of UEs' positions. The use of deep learning significantly improves the complexity-performance trade-off of power allocation, compared to traditional optimization-oriented methods. Particularly, the proposed approach does not require the computation of any statistical average, which would be instead necessary by using standard methods, and is able to guarantee near-optimal performance.