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.

中文翻译：

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具有能量收集要求的设备到设备通信中基于学习的资源管理

在本文中，我们提出了一种基于深度学习的资源管理方法，该方法控制发射功率和功率分配比，以在具有能量收集要求的 D2D 网络中以低计算复杂度最大化总和率。将能量收集要求引入 D2D 网络使得很难设计有效的资源管理解决方案，因为干扰信号的处理应该与仅关注速率最大化的传统资源管理完全不同。为了解决传统的基于深度学习的方法的缺点，我们提出了一种适用于我们的资源管理问题的新训练算法。数值模拟表明，所提出的基于学习的方法优于来自一些相关工作的基准方法，在大多数情况下，并在总速率和能源中断概率方面实现了与穷举搜索相当的性能。虽然传统的基于优化的方法被推导出来实现大型网络的渐近最优性能，但所提出的深度学习方法显示出几乎相同的性能，但计算复杂度要低得多。此外，模拟结果为能量收集要求对最佳资源管理行为的影响提供了新的见解。所提出的深度学习方法显示出几乎相同的性能，但计算复杂度要低得多。此外，模拟结果为能量收集要求对最佳资源管理行为的影响提供了新的见解。所提出的深度学习方法显示出几乎相同的性能，但计算复杂度要低得多。此外，模拟结果为能量收集要求对最佳资源管理行为的影响提供了新的见解。