Deep unfolding for energy-efficient resource allocation in mmWave networks with multi-connectivity

In millimeter-wave (mmWave) communications, multi-connectivity can enhance the communication capacity while at the cost of increased power consumption. In this paper, we investigate a deep-unfolding-based approach for joint user association and power allocation to maximize the energy efficiency of mmWave networks with multi-connectivity. The problem is formulated as a mixed integer nonlinear fractional optimization problem. First, we develop a three-stage iterative algorithm to achieve an upper bound of the original problem. Then, we unfold the iterative algorithm with a convolutional neural network (CNN)-based accelerator and trainable parameters. Moreover, we propose a CNN-aided greedy algorithm to obtain a feasible solution. The simulation results show that the proposed algorithm can achieve good performance and strong robustness but with much reduced computational complexity.