Evidential Obstacle learning in Millimeter wave D2D communication using Spatial Correlation

Abstract

The 5G technology has created a lot of interest recently to support the increasing demand for higher data rates by the user equipments (UEs). The most interesting part of 5G is the use of device to device (D2D) communication, using millimeter waves. Millimetre waves can immensely increase the data rates compared to 4G. But millimetre waves su er from a host of problems ranging from high free space loss restricting range, and also extremely high penetration loss, making it almost line of sight (LOS) communication. To handle these problems, communication links can be broken into multiple hops by using intermediate devices as relays, avoiding obstacles and thereby extending the range. Knowing the location and size of obstacles is key to choosing good relays. Satellite imagery can be used to do the same. But small obsta- cles like trees cannot be captured properly using satellite imagery. Moreover presence of an obstacle in the image, does not guarantee obstruction. Our work concentrates on mapping static obstacles in a given area to help in the process of relay selection. We propose a learning based strategy, which considers spatial correlation of obstacles, to build the static obstacle map e ciently without relying on satellite imagery. We propose a evidential framework to model the con dence in the knowledge gained for each cell, as this can model uncertainty better than a typical probabilistic model. We also propose an operation similar to Gaussian smoothing, to learn information about a cell, using the information available from the nearby cells. We have proposed a visibility graph algorithm which will reduce the overhead due to repeated updation of the location information by the UEs, and thereby improve the overall coverage dra- matically. We have also proposed a exploration mode during relay selection, which can be used in the place of the normal mode of relay selection, which can reduce the time taken to learn the map, by selecting relays which encourage exploration rather than prioritizing maximizing throughput only. Through simulations, we show that our evidential framework based approach can learn the map faster and more accurately than the typical probabilistic model based work [18].