TOWARD ENHANCED WIRELESS COEXISTENCE IN THE 2.4GHZ ISM BAND VIA TEMPORAL CHARACTERIZATION AND EMPIRICAL MODELING OF 802.11B/G/N NETWORKS A DISSERTATION

This dissertation presents an extensive experimental characterization and empirical modelling of 802.11 temporal behavior. A detailed characterization of 802.11b/g/n homogeneous and heterogeneous network traffic patterns is featured, including idle time distribution and channel utilization. Duty cycle serves as a measure for spectrum busyness. Higher duty cycle levels directly impact transceivers using the spectrum, which either refrain from transmission or suffer from increased errors. Duty cycle results are provided for 802.11b, g and n Wi-Fi technologies at various throughput levels. Lower values are observed for 802.11b and g networks. Spectrum occupancy measurements are essential for wireless networks planning and deployment. Detailed characterization of 802.11g/n homogeneous and heterogeneous network traffic patterns, including activity and idle time distribution are presented. Distributions were obtained from time domain measurements and represent time fragment distributions for active and inactive periods during a specific test. This information can assist other wireless technologies in using the crowded ISM band more efficiently and achieve enhanced wireless coexistence. Empirical models of 802.11 networks in the 2.4 GHz Industrial, Scientific, and Medical (ISM) band are also presented. This information can assist other wireless technologies aiming to utilize the crowded ISM band more efficiently and achieve enhanced wireless coexistence. In this work models are derived for both homogeneous and heterogeneous 802.11 network idle time distribution. Additionally, two applications of 802.11 networks temporal characterization are presented. The first application investigates a novel method for identifying wireless technologies through the use of simple energy detection techniques to measure the channel temporal characteristics including activity and idle time probability distributions. In this work, a wireless technology identification algorithm was assessed experimentally. Temporal traffic pattern for 802.11b/g/n homogeneous and heterogeneous networks were measured and used as algorithm input. Identification accuracies of up to 96.83% and 85.9% are achieved for homogeneous and heterogeneous networks, respectively. The second application provides a case study using 802.15.4 ZigBee transmitter packet size on-line adjustments is also presented. Packet size is adaptively modified based on channel idle time distribution obtained using simple channel power measurements. Results demonstrate improved ZigBee performance and significant enhancement in throughput as a result of using adaptive packet size transmissions

An Adaptive Packet Aggregation Algorithm (AAM) for Wireless Networks

Packet aggregation algorithms are used to improve the throughput performance by combining a number of packets into a single transmission unit in order to reduce the overhead associated with each transmission within a packet-based communications network. However, the throughput improvement is also accompanied by a delay increase. The biggest drawback of a significant number of the proposed packet aggregation algorithms is that they tend to only optimize a single metric, i.e. either to maximize throughput or to minimize delay. They do not permit an optimal trade-off between maximizing throughput and minimizing delay. Therefore, these algorithms cannot achieve the optimal network performance for mixed traffic loads containing a number of different types of applications which may have very different network performance requirements. In this thesis an adaptive packet aggregation algorithm called the Adaptive Aggregation Mechanism (AAM) is proposed which achieves an aggregation trade-off in terms of realizing the largest average throughput with the smallest average delay compared to a number of other popular aggregation algorithms under saturation conditions in wireless networks. The AAM algorithm is the first packet aggregation algorithm that employs an adaptive selection window mechanism where the selection window size is adaptively adjusted in order to respond to the varying nature of both the packet size and packet rate. This algorithm is essentially a feedback control system incorporating a hybrid selection strategy for selecting the packets. Simulation results demonstrate that the proposed algorithm can (a) achieve a large number of sub-packets per aggregate packet for a given delay and (b) significantly improve the performance in terms of the aggregation trade-off for different traffic loads. Furthermore, the AAM algorithm is a robust algorithm as it can significantly improve the performance in terms of the average throughput in error-prone wireless networks

Cross-Layer-Optimierungen für WLAN-Mesh-Netzwerke

Gegenstand dieser Arbeit ist es, das Verhalten von IEEE-802.11s-Mesh-Netzwerken in der Praxis zu untersuchen und Strategien und Lösungen zu entwickeln, durch die einerseits die Administrierbarkeit und Skalierbarkeit komplexer Mesh-Backbones erhöht werden und andererseits verteilte Anwendungen die darunter liegende Netzwerkstruktur gezielt berücksichtigen können, um das vorhandene Datendurchsatzpotential effizient zu nutzen.The aim of this thesis is to investigate the practical behavior of IEEE 802.11s mesh networks and to develop strategies and solutions that, on the one hand, increase the scalability and manageability of complex mesh backbones and, on the other hand, enable distributed applications to explicitly consider the underlying network structure, allowing them to utilize the available network capacity efficiently