Interference modeling and performance analysis of 5G MmWave networks

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dc.contributor.author Niknam, Solmaz
dc.date.accessioned 2018-12-05T19:43:42Z
dc.date.available 2018-12-05T19:43:42Z
dc.date.issued 2019-05-01
dc.identifier.uri http://hdl.handle.net/2097/39369
dc.description.abstract Triggered by the popularity of smart devices, wireless traffic volume and device connectivity have been growing exponentially during recent years. The next generation of wireless networks, i.e., 5G, is a promising solution to satisfy the increasing data demand through combination of key enabling technologies such as deployment of a high density of access points (APs), referred to as ultra-densification, and utilization of a large amount of bandwidth in millimeter wave (mmWave) bands. However, due to unfavorable propagation characteristics, this portion of spectrum has been under-utilized. As a solution, large antenna arrays that coherently direct the beams will help overcome the hostile characteristics of mmWave signals. Building networks of directional antennas has given rise to many challenges in wireless communication design. One of the main challenges is how to incorporate 5G technology into current networks and design uniform structures that bring about higher network performance and quality of service. In addition, the other factor that can be severely impacted is interference behavior. This is basically due to the fact that, narrow beams are highly vulnerable to obstacles in the environment. Motivated by these factors, the present dissertation addresses some key challenges associated with the utilization of mmWave signals. As a first step towards this objective, we first propose a framework of how 5G mmWave access points can be integrated into the current wireless structures and offer higher data rates. The related resource sharing problem has been also proposed and solved, within such a framework. Secondly, to better understand and quantify the interference behavior, we propose interference models for mmWave networks with directional beams for both large scale and finite-sized network dimension. The interference model is based on our proposed blockage model which captures the average number of obstacles that cause a complete link blockage, given a specific signal beamwidth. The main insight from our analysis shows that considering the effect of blockages leads to a different interference profile. Furthermore, we investigate how to model interference considering not only physical layer specifications but also upper layers constraints. In fact, upper network layers, such as medium access control (MAC) protocol controls the number of terminals transmitting simultaneously and how resources are shared among them, which in turn impacts the interference power level. An interesting result from this analysis is that, from the receiving terminal standpoint, even in mmWave networks with directional signals and high attenuation effects, we still need to maintain some sort of sensing where all terminals are not allowed to transmit their packets, simultaneously. The level of such sensing depends on the terminal density. Lastly, we provide a framework to detect the network regime and its relation to various key deployment parameters, leveraging the proposed interference and blockage models. Such regime detection is important from a network management and design perspective. Based on our finding, mmWave networks can exhibit either an interference-limited regime or a noise-limited regime, depending on various factors such as access point density, blockage density, signal beamwidth, etc. en_US
dc.language.iso en_US en_US
dc.subject Interference modeling en_US
dc.subject Millimeter wave communication en_US
dc.subject 5G en_US
dc.subject Stochastic geometry en_US
dc.subject Multi-band heterogeneous networks en_US
dc.subject Blockage effect en_US
dc.title Interference modeling and performance analysis of 5G MmWave networks en_US
dc.type Dissertation en_US
dc.description.degree Doctor of Philosophy en_US
dc.description.level Doctoral en_US
dc.description.department Department of Electrical and Computer Engineering en_US
dc.description.advisor Balasubramaniam Natarajan en_US
dc.date.published 2019 en_US
dc.date.graduationmonth May en_US


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