Analysis Of Wireless Communication Systems With Stochastic Network Calculus

Traditional mathematical tools like queuing theory and probability theory and stochastic process have been applied to conduct the performance analysis of wireless communication systems, where some meaningful results are obtained. However, there are also some limitations of these analysis tools with the increasing complexity and diversity of the networks. Stochastic network calculus, a newly developed theory, makes a breakthrough and brings new insights into analysis of wireless systems.To begin with, stochastic network calculus theory is emphatically introduced, which includes mathematical basis of min-plus algebra, two basic concepts stochastic arrival curve and stochastic service curve, three significant results-Back Bound theorem, Delay Bound theorem and Leftover Service theorem. Besides, typical traffic models and server models that are employed in this work are analyzed respectively.For considered cognitive radio network here, delay distribution bounds of the primary users and the secondary users are derived by independent case analysis and min-plus convolution on the basic of corresponding stochastic arrival curve and stochastic service curve. The numerical results are obtained, where the good match validates the theoretical analysis. The network capacity is also discussed in this paper. And the simulation results show that independent case analysis can perform more efficiently than the min-plus convolution.Finally, we take GSM network and TD-SCDMA network as examples to illustrate how stochastic network calculus can apply to analysis of actual network congestion. For voice and data services, respectively, stochastic arrival curve and stochastic service curve are derived based on proper traffic model and service model. Then, congestion distribution bounds are figured out by substituting into proper network parameters. The computed results of huge network data show that the models in stochastic network calculus can match the real system better than traditional analysis tools. In other words, stochastic network calculus will play a significant positive role in performance analysis of wireless communication systems.