Learning-Based Resource Management in Device-to-Device Communications With Energy Harvesting Requirements

In this paper, we propose a resource management method based on deep learning, which controls both the transmit power and the power splitting ratio to maximize the sum rate with low computational complexity in D2D networks with energy harvesting requirements. The introduction of the energy harvesting requirements to D2D networks makes it hard to design an effective resource management solution since the treatment of interference signals should be completely different from the conventional resource management focusing only on the rate maximization. To deal with drawbacks of the conventional deep learning-based approach, we propose a new training algorithm suitable for our resource management problem. Numerical simulations show that the proposed learning-based method outperforms the benchmark methods, which are derived from some relevant works, in most situations and achieves performances comparable to an exhaustive search in terms of the sum rate and energy outage probability. Although the conventional optimization-based method is derived to achieve the asymptotic optimal performance for a large network, the proposed deep learning method is shown to achieve almost the same performance with much lower computational complexity. Furthermore, simulation results offer new insights to the impact of the energy harvesting requirements on the behaviour of the optimal resource management.