Device-to-device (D2D) communication plays a significant role in cellular networks as it can increase the capacity, spectrum efficiency and energy efficiency of the system. However, the large computational complexity of D2D resource management optimisation algorithms creates a serious gap between theoretical design and real-time processing, which leads to the limited use of D2D communication technology. In this study, a novel deep learning-based optimisation method is proposed to overcome the high computational complexity of joint beamforming design and power allocation optimisation algorithms in D2D communication. Unlike existing approaches, the authors design a convolutional neural network based end-to-end network structure to solve complex computing problems for channel state information under a limited feedback scenario. The Max-SE loss function which indicates quality-of-service (QoS) constraint and interference constraint, together with the mean squared error (MSE) function, are designed to maximise the spectral efficiency of the system while minimising the total transmit power. The simulation results show that the proposed approach can achieve performance comparable to the weighted minimum MSE scheme with low computation time.

中文翻译：

---

使用深度学习的联合波束成形和功率分配，用于异构网络中的D2D通信

设备到设备（D2D）通信在蜂窝网络中起着重要作用，因为它可以增加系统的容量，频谱效率和能效。但是，D2D资源管理优化算法的巨大计算复杂性在理论设计和实时处理之间造成了严重的差距，这导致D2D通信技术的使用受到限制。在这项研究中，提出了一种新颖的基于深度学习的优化方法，以克服D2D通信中联合波束成形设计和功率分配优化算法的高计算复杂性。与现有方法不同，作者设计了一种基于卷积神经网络的端到端网络结构，以解决有限反馈情况下信道状态信息的复杂计算问题。指示服务质量（QoS）约束和干扰约束的Max-SE损失函数，以及均方误差（MSE）函数，旨在最大程度地降低系统的频谱效率，同时将总发射功率降至最低。仿真结果表明，所提出的方法能够以较低的计算时间实现与加权最小MSE方案相当的性能。