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Title: A localization system for optimizing the deployment of small cells in 2-tier heterogeneous wireless networks
Author: Abonyi, Dorathy O.
ISNI:       0000 0004 7651 6492
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
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Due to the ever growing population of mobile device users and expansion on the number of devices and applications requiring data usage, there is an increasing demand for improved capacity in wireless cellular networks. Cell densification and 2-tier heterogeneous networks (HetNets) are two solutions which will assist 5G systems in meeting these growing capacity demands. Small-cell deployment over existing heterogeneous networks have been considered by researchers. Different strategies for deploying these small-cells within the existing network among which are random, cell-edge and high user concentration (HUC) have also been explored. Small cells deployed on locations of HUC offloads traffic from existing network infrastructure, ensure good Quality of Service (QoS) and balanced load in the network but there is a challenge of identifying HUC locations. There has been considerable research performed into techniques for determining user location and cell deployment. Currently localization can be achieved using time dependent methods such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), or Global Positioning Systems (GPS). GPS based solutions provide high accuracy user positioning but suffer from concerns over user privacy, and other time dependent approaches require regular synchronization which can be difficult to achieve in practice. Alternatively, Received Signal Strength (RSS) based solutions can provide simple anonymous user data, requiring no extra hardware within the mobile handset but often rely on triangulation from adjacent Base Stations (BS). In mobile cellular networks such solutions are therefore often only applicable near the cell edge, as installing additional BS would increase the complexity and cost of a network deployment. The work presented in this thesis overcomes these limitations by providing an observer system for wireless networks that can be used to periodically monitor the cell coverage area and identify regions of high concentrations of users for possible small cell deployment in 2-tier heterogeneous networks. The observer system comprises of two collinear antennas separated by λ/2. The relative phase of each antenna was varied using a phase shifter so that the combined output of the two antennas were used to create sum and difference radiation patterns, and to steer the antenna radiation pattern creating different azimuth positions for AoA estimation. Statistical regression analysis was used to develop range estimation models based on four different environment empirical pathloss models for user range estimation. Users were located into clusters by classifying them into azimuth-range classes and counting the number of users in each class. Locations for small cell deployment were identified based on class population. BPEM, ADEM, BUEM, EARM and NLOS models were developed for more accurate range estimation. A prototype system was implemented and tested both outdoor and indoor using a network of WiFi nodes. Experimental results show close relationship with simulation and an average PER in range estimation error of 80% by applying developed error models. Based on both simulation and experiment, system showed good performance. By deploying micro-, pico-, or femto-cells in areas of higher user concentration, high data rates and good quality of service in the network can be maintained. The observer system provides the network manager with relative angle of arrival (AoA), distance estimation and relative location of user clusters within the cell. The observer system divides the cell into a series of azimuthal and range sectors, and determines which sector the users are located in. Simulation and a prototype design of the system is presented and results have shown system robustness and high accuracy for its purpose.
Supervisor: Rigelsford, Jonathan M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available