Title

Network Density Estimators and Density-aware Wireless Networks

Abstract

Abstract

New network architectures and communication technologies continue to emerge to meet rapidly increasing and changing user demands requiring continuous connectivity and high data rate transmissions. These ubiquitous infrastructures result in a paradigm shift in mobile communications with the advent of mobile robots equipped with sensors, unmanned aerial vehicles, and mobile small-cells, which makes the future networks highly dynamic. This dynamism poses unpredictable variations in the network density causing many run-time problems such as disrupted coverage, undesirable quality of service, and inefficient resource usage. Pre-configurations are no longer suitable because of the network topology variations, which prompts us to develop density-adaptive protocols and self-configured system designs. Therefore, this thesis's most crucial objective is to make future wireless networks density-aware and -adaptive. We propose novel network density estimators using received signal strength and density-aware networking applications. We introduce a distance-matrix- based density estimator, multi-access edge cloud-based density estimator, and interference-based density estimator for wireless networks. We also develop density-aware network outage, transmit power adaptation, and channel utilization approaches by considering the effective network density as an optimization parameter for clustered ad hoc networks, mobile cellular networks, and flying ad hoc networks. We validate the expected results by implementing Monte-Carlo simulations on MATLAB. This thesis's outputs help network operators enhance service quality, create the best deployment strategies, reduce operational expenditures, and meet increasing user expectations without wasting network resources. Density-aware and -adaptive applications make wireless networks self-organized and run-time adaptable.

Biography:

Supervisor(s)

Supervisor(s)

ALPEREN EROGLU

Date and Location

Date and Location

2020-10-08;14:00:00-Webinar

Category

Category

PhD_Thesis