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Evidential Obstacle learning in Millimeter wave D2D communication using Spatial Correlation

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dc.contributor.author Ganesan, Harish
dc.date.accessioned 2021-07-29T05:14:09Z
dc.date.available 2021-07-29T05:14:09Z
dc.date.issued 2020-07
dc.identifier.citation 55p. en_US
dc.identifier.uri http://hdl.handle.net/10263/7167
dc.description Dissertation under the supervision of Dr. Sasthi Charan Ghosh, ACMU en_US
dc.description.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]. en_US
dc.language.iso en en_US
dc.publisher Indian Statistical Institute, Kolkata en_US
dc.relation.ispartofseries Dissertation;;2020-13
dc.subject Learning en_US
dc.subject Obstacle avoidance en_US
dc.subject Evidential Framework en_US
dc.subject 5G D2D Com- munication en_US
dc.subject relay selection en_US
dc.title Evidential Obstacle learning in Millimeter wave D2D communication using Spatial Correlation en_US
dc.type Other en_US


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