There has been growing interest in device to device (D2D) millimeterwave (mmwave) communication, due to the promising high speeds and immense amounts of unused bandwidth available. However, mmwaves suffer from unusually high attenuation, through free space, and especially through obstacles. The accepted way to avoid such attenuation is to break up the transmission path into multiple short hops, such that there are no obstacles between nodes. We extend the possibility of using a global positioning system (GPS) based, location aware, centralized approach to the problem of relay selection. We propose a simple learning based approach to detect the presence of static as well as dynamic obstacles, without having access to any data regarding their location and sizes. We then use this knowledge to efficiently select an appropriate relay for a UE, lowering the chance of allocating an obstacle prone link. Our proposed algorithm works even for UEs inside vehicles. We also propose a smart way of checking whether a pair of UEs is likely to be blocked, in real time. Finally we compare our relay selection algorithm with an existing algorithm and show that there is a significant improvement in the quality of link allocation.

由于有前途的高速和大量可用的未使用带宽，对设备到设备（D2D）毫米波（mmwave）通信的兴趣日益增长。但是，毫米波会通过自由空间，特别是通过障碍物而受到异常高的衰减。避免这种衰减的公认方法是将传输路径分成多个短跳，以使节点之间没有障碍。我们扩展了使用基于全球定位系统（GPS）的，位置感知的集中式方法来解决中继器选择问题的可能性。我们提出了一种基于学习的简单方法，可以检测静态和动态障碍物的存在，而无需访问有关其位置和大小的任何数据。然后，我们使用此知识来为UE有效地选择合适的中继，降低分配容易产生障碍的链接的机会。我们提出的算法甚至适用于车辆内部的UE。我们还提出了一种智能方式，可以实时检查一对UE是否可能被阻塞。最后，我们将中继选择算法与现有算法进行比较，结果表明链路分配的质量有了显着提高。