Stochastic Performance Analysis of a Tree Top ology Network.pdf

1、Wireless networks are stochastic by nature, i.e., the wireless channel capacity is variant over time due to channel impairment and contention, and efficient approaches are needed so as to conduct performance analysis of

2、such networks. Stochastic network calculus is a newly developed theory for queueing systems that provides stochastic service guarantees. Particularly, this thesis focuses on developing wireless channel models for QoS ana

3、lysis taking into account the underlying statistical properties and providing stochastic service guarantees of tree topology networks with stochastic network calculus.Future wireless communication calls for exploration o

4、f more efficient use of wireless channel capacity to meet the increasing demand on higher data rate and less latency. In order to explore the ultimate capacity that the wireless channel can provide and to guarantee plura

5、listic quality of service, cumulative capacity is proposed with focus on the finite time regime. Specifically, based on the cumulative distribution function of cumulative capacity, moment generating function, stochastic

6、service curve, and Mellin transform are investigated as further properties of wireless channel capacity. These further properties of wireless channel capacity are essential building blocks for QoS analysis with stochasti

7、c network calculus.A reference analysis loop is proposed in the process of performance analysis, i.e., the performance metrics are derived based on a generic net-work specification, then the network parameters are calcul

8、ated satisfying a given performance requirement in a reverse style. The analysis loop is then used to analyze the topology control boundary conditions under performance constraints in a wireless mesh network and to inves

9、tigate the quantitative impact of duty cycle on the end-to-end network perfor-mance. Specifically, properties of logarithm functions and Taylor series are utilized to derive closed-form expressions of network diameter su

10、bject to end-to-end performance metrics and/or duty cycle.The basic properties of stochastic network calculus are further in-vestigated under the combination of leaky-bucket-type stochastic arrival curve with negative ex

11、ponential bounding function and latency-rate-type stochastic service curve with negative exponential bounding function. It is shown that all the further results are bounded with a negative exponential bounding function.

12、These results are applied to a tree topology wireless sensor network that provides stochastic service guarantees. It is worth noting that the tree topology is a generalization of the line topology, in other words, line t

13、opology network is an end-to-end path of a tree topology network. When analyzing end-to-end performance of a tree topology network, it is transformed to a line topology network, taking advantage of the properties of stoc

14、hastic network calculus, a network ser-vice curve is derived for an end-to-end path of the tree topology network, together with the stochastic arrival curve for the input, per-hop backlog and end-to-end delay are easily