In wireless communication systems, there is a need to design efficient schemes in order to overcome the problem of spectrum scarcity. One technology to address the problem of spectrum scarcity is cognitive radio (CR), in which a network entity is able to adapt intelligently to the environment through observation, exploration and learning.
When multiple autonomous cognitive radios are searching for spectrum opportunities, they face competition from each other in order to access the available free channel. This will result in reduced throughput which occurs due to collision between cognitive radios, when they try to transmit in the same channel.
The purpose of this thesis is to study a smart adaptation scheme for efficient channel access which enable autonomous cognitive radios to improve their overall bandwidth efficiency in a distributed cognitive radio network with the help of spatial reuse.
An adaptive persistent strategy with efficient collision detection has been studied in this work for autonomous channel sensing order selection which enable distributed CRs to avoid collision and allow them to improve their overall system efficiency by increasing the average number of successful transmissions, especially, when number of available channels are less than the number of CRs competing to access these free channels.
The performance of the studied strategy is compared with random selection of sensing orders. Simulation results are presented, which indicate that the studied strategy with spatial reuse achieves the highest number of successful transmissions in a given time slot as compared to other strategies. Simulation results are also compared for the case with no spatial reuse and the results indicate that it degrades the system efficiency by reducing the average number of successful transmissions in a given time slot.
Identifer | oai:union.ndltd.org:oulo.fi/oai:oulu.fi:nbnfioulu-201612103244 |
Date | 12 December 2016 |
Creators | Sultan, A. (Akmal) |
Publisher | University of Oulu |
Source Sets | University of Oulu |
Language | English |
Detected Language | English |
Type | info:eu-repo/semantics/masterThesis, info:eu-repo/semantics/publishedVersion |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess, © Akmal Sultan, 2016 |
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