تجزیه و تحلیل عملکرد از خدمات عمومی رادیویی بسته اطلاعاتی

This paper presents an efficient and accurate analytical model for the radio interface of the general packet radio service (GPRS) in a GSM network. The model is utilized for investigating how many packet data channels should be allocated for GPRS under a given amount of traffic in order to guarantee appropriate quality of service. The presented model constitutes a continuous-time Markov chain. The Markov model represents the sharing of radio channels by circuit switched GSM connections and packet switched GPRS sessions under a dynamic channel allocation scheme. In contrast to previous work, the Markov model explicitly represents the mobility of users by taking into account arrivals of new GSM and GPRS users as well as handovers from neighboring cells. Furthermore, we take into account TCP flow control for the GPRS data packets. To validate the simplifications necessary for making the Markov model amenable to numerical solution, we provide a comparison of the results of the Markov model with a detailed simulator on the network level.

The general packet radio service (GPRS) is a standard from the European Telecommunications Standards Institute (ETSI) on packet data in GSM systems [10]. By adding GPRS functionality to the existing GSM network, operators can give their subscribers resource-efficient wireless access to external Internet protocol-based networks, such as the Internet and corporate Intranets. The basic idea of GPRS is to provide a packet-switched bearer service in a GSM network. As impressively demonstrated by the Internet, packet-switched networks make more efficient use of the resources for bursty data applications and provide more flexibility in general. To evaluate the performance of GPRS, several simulation studies were conducted. Early simulation studies for GPRS have been reported in [6] and [7]. Meyer evaluated the performance of TCP over GPRS under several carrier to interference conditions and data coding schemes [13] and [17]. Malomsoky et al. developed a simulator for dimensioning GSM networks with GPRS [15]. Stuckmann and Müller developed a system simulator for GPRS and studied the correlation of GSM and GPRS users for fixed and on-demand channel allocation techniques [18]. In previous work, several analytical models based on continuous-time Markov chains have been introduced for studying performance issues in GSM networks. Marsan et al. evaluated the impact of reserving channels for data and multimedia services on the performance in a circuit switched GSM network [1]. Marsan et al. developed an approximate analytical model for evaluating the performance of dual-band GSM networks [3]. Boucherie and Litjens developed a Markov model for analyzing the performance of GPRS under a given GSM call characteristic [5]. Markoulidakis et al. developed a Markov model for third generation mobile telecommunication systems [16]. They employed the Markov model for estimating the cell border crossing rate and the time it takes a busy mobile user to leave a cell area. Recently, Ermel et al. developed a Markov model for deriving blocking probabilities and average data rates for GPRS in GSM networks [9]. In none of these previous work, the question how many packet data channels (PDCH) should be allocated for GPRS for a given amount of traffic in order to guarantee appropriate quality of service (QoS) has been investigated. This paper presents an efficient and accurate analytical performance model for the radio interface of the GPRS in a GSM network. The presented model constitutes a continuous-time Markov chain. The Markov model introduced in this paper represents the sharing of radio channels by circuit switched GSM connections and packet switched GPRS sessions under a dynamic channel allocation scheme. We assume a fixed number of physical channels permanently reserved for GPRS sessions and the remaining channels to be shared by GSM and GPRS connections. The model is utilized for investigating how many PDCH should be allocated for GPRS for a given amount of traffic in order to guarantee appropriate QoS. We present performance curves for average carried data traffic, packet loss probability, throughput per user, and queueing delay for different network configurations and traffic parameters. In contrast to previous work, the Markov model explicitly represents the mobility of users by taking into account arrivals of new GSM and GPRS users as well as handovers from neighboring cells. Furthermore, we employ the traffic model defined by the 3rd Generation Partnership Project (3GPP) in [11] that can be effectively represented by an interrupted Poisson process (IPP), i.e., an on–off source. We consider a cluster comprising of seven hexagonal cells in an integrated GSM/GPRS network, serving circuit-switched voice and packet-switched data sessions. To allow the effective employment of numerical solution methods, the Markov model represents just one cell (i.e., the mid-cell) and employs the procedure for balancing incoming and outgoing handover rates introduced in [2]. To validate this simplification, we provide a comparison of the results of the Markov model with a detailed simulator implemented using the simulation library CSIM [8]. The simulator represents the entire cell cluster on the network level. Furthermore, an accurate implementation of the TCP flow control mechanism is included in the simulator. This validation shows that almost all performance curves derived from the Markov model lie in the confidence intervals of the corresponding curve of the simulator. Because of the employment of a numerical method for steady-state analysis, we can efficiently and accurately compute sensitive performance measures such as loss probabilities. In fact, using the presented Markov model sensitive performance measures can be computed on a modern PC within few minutes of CPU solution time. Note, that even with simulation runs in the order of hours proper estimates for such measures cannot be derived using discrete-event simulation because the large width of confidence intervals makes the results meaningless. The remainder of the paper is organized as follows. Section 2 describes the basic GPRS network architecture and the radio interface which provide the technical background of the simulator and the analytical model. In Section 3, we describe the model and introduce its parameters. Section 4 derives the state space and driving processes for the analysis of the Markov model. Comprehensive performance studies for GPRS are presented in Section 5. A detailed comparison of the performance between different network configurations and percentages of GPRS users is provided. Finally, concluding remarks are given.

This paper presented a comprehensive performance study of the radio resource sharing by circuit switched GSM connections and packet switched GPRS sessions under a dynamic channel allocation scheme. We assumed a fixed number of physical channels permanently allocated to GPRS and the remaining channels to be on-demand channels that can be used by GSM voice service and GPRS packets. Performance results are derived from the steady-state analysis of a Markov model. A validation of the Markov model with a detailed simulator on the network level shows that almost all performance curves derived from the Markov model lie in the confidence intervals of the corresponding curve of the simulator. We investigated the impact of the number of PDCH reserved for GPRS users on the performance of the cellular network. That is for example, for GPRS users with a QoS profile allowing a throughput degradation of at most 50%, we concluded that for 2% GPRS users among all incoming calls, the reservation of four PDCHs is sufficient up to an GSM/GPRS call arrival rate of 1 call per second. However, for the case of 5% and 10% GPRS users, the QoS profile can only be guaranteed up to a call arrival rate of 0.5 and 0.3 calls per second, respectively. Such results give valuable hints for network designers on how many PDCHs should be allocated for GPRS for a given amount of traffic in order to guarantee appropriate QoS. Note, that determining the number of PDCHs for GPRS is a tradeoff between GSM and GPRS performance. Therefore, an optimal value of PDCHs can be only determined with respect to the desired performance requirements for GSM and GPRS that must be selected by the network operator. Applying adaptive performance management [14], future work considers the dynamic adjustment of the number of PDCHs with respect to the current GSM and GPRS traffic load and the desired performance requirements.