A mobile hotspot is a moving vehicle that hosts an Access Point (AP) such as train, bus and subway where users in these vehicles connect to external cellular network through AP to access their internet services. To meet Quality of Service (QoS) requirements, typically throughput and/or delay, a Call Admission Control (CAC) is needed to restrict the number of users accepted by the AP. In this paper, we analyze a modified guard channel scheme as CAC for mobile hotspot as follows: During a mobile hotspot is in the stop-state, we adopt a guard channel scheme where the optimal number of resource units is reserved for vertical handoff users from cellular network to WLAN. During a mobile hotspot is in the move-state, there are no handoff calls and so no resources for handoff calls are reserved in order to maximize the utility of the WLAN capacity. We model call's arrival and departure processes by Markov Modulated Poisson Process (MMPP) and then we model our CAC by 2-dimensional continuous time Markov chain (CTMC) for single traffic and 3-dimensional CTMC for two types of traffic. We solve steady-state probabilities by the Quasi-Birth and Death (QBD) method and we get various performance measures such as the new call blocking probabilities, the handoff call dropping probabilities and the channel utilizations. We compare our CAC with the conventional guard channel scheme which the number of guard resources is fixed all the time regardless of states of the mobile hotspot. Finally, we find the optimal threshold value on the amount of resources to be reserved for the handoff call subject to a strict constraint on the handoff call dropping probability.

Measuring and modeling network traffic is of key importance for the traffic engineering of IP networks, due to the growing diversity of multimedia applications and the need to efficiently support QoS differentiation in the network. Several recent measurements have shown that Internet traffic may incorporate long-range dependence and self-similar characteristics, which can have significant impact on network performance. Self-similar traffic shows variability over many time scales, and this behavior must be taken into account for accurate prediction of network performance. In this paper, we propose a new parameter fitting procedure for a superposition of Markov Modulated Poisson Processes (MMPPs), which is able to capture self-similarity over a range of time scales. The fitting procedure matches the complete distribution of the arrival process at each time scale of interest. We evaluate the procedure by comparing the Hurst parameter, the probability mass function at each time scale, and the queuing behavior (as assessed by the loss probability and average waiting time), corresponding to measured traffic traces and to traces synthesized according to the proposed model. We consider three measured traffic traces, all exhibiting self-similar behavior: the well-known pOct Bellcore trace, a trace of aggregated IP WAN traffic, and a trace corresponding to the popular file sharing application Kazaa. Our results show that the proposed fitting procedure is able to match closely the distribution over the time scales present in data, leading to an accurate prediction of the queuing behavior.