Relay selection in millimeter wave D2D communications through obstacle learning

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.