Evaluating the GPRS Radio Interface for Different Quality of Service
Profiles
Abstract. This paper presents a discrete-event simulator for the General
Packet Radio Service (GPRS) on the IP level. GPRS is a standard on
packet data in GSM systems that will become commercially available by
the end of this year. The simulator focuses on the communication over
the radio interface, because it is one of the central aspects of GPRS.
We study the correlation of GSM andGPRS users by a static and dynamic
channel allocation scheme. In contrast to previous work, our approach
represents the mobility of users through arrival rates of new GSM and
GPRS users as well as handover rates of GSM and GPRS users from
neighboring cells. Furthermore, we consider users with different QoS
profiles modeled by a weighted fair queueing scheme. The simulator
considers a cell cluster comprising seven hexagonal cells. We provide
curves for average carried traffic and packet loss probabilities for
differentchannel allocation schemes and packet priorities as well as
curves for average throughput per GPRS user. A detailed comparison
between static and dynamic channel allocation schemes is provided.
1 Introduction
The General Packet Radio Service (GPRS) is a standard from the European
Telecommunications Standards Institute (ETSI) on packet data in GSM
systems [6], [14]. By adding GPRS functionality to the existing GSM
network, operators can givetheir subscribers resource-efficient wireless
access to external Internet protocol-bases 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. In previous work, several analytical models
have been developed to study data services in a GSM network. Ajmone
Marsan et al. studied multimedia services in a GSM network by providing
more than one channel for data services [1]. Boucherie and Litjens
developed an analytical model based on Markov chain analysis to study
the performance of GPRS under a given GSM call characteristic [4]. For
analytical tractability, they assumed exponentially distributed arrival
times for packets and exponential packet transfer times, respectively.
On the other hand, discrete-event simulation based studies of GPRS were
conducted. Meyer et al. focused on the performance of TCP over GPRS
under several carrier to interference conditions and coding schemes of
data [10]. Furthermore, they provided a detailed implementation of the
GPRS protocol stack [11]. Malomsoky et al. developed a simulation based
GPRS network dimensioning tool [9]. Stuckmann et al. studied the
correlation of GSM and GPRS users with the simulator GPRSim [13]. This
paper describes a discrete-event simulator for GPRS on the IP level. The
simulator is developed using the simulation package CSIM [12] and
considers a cellcluster comprising of seven hexagonal cells. The
presented performance studies were conducted for the innermost cell of
the seven cell cluster. The simulator focuses on the communication over
the radio interface, because this is one of the central aspects of GPRS.
In fact, the air interface mainly determines the performance of GPRS. We
studied the correlation of GSM and GPRS users by a static and dynamic
channel allocation scheme. A first approach of modeling dynamic channel
allocation was introduced by Bianchi et al. and is known as Dynamic
Channel Stealing (DCS) [3].
The basic DCS concept is to temporarily assign the traffic channels
dedicated to circuit-switched connections but unused because
statistical traffic fluctuations. This can be done at no expense in
terms of radio resource, and with no impact on circuitswitched services
performance if the channel allocation to packet-switched services is
permitted only for idle traffic channels, and the stolen channels are
immediately released when requested by the circuit-switched service. In
contrast to the models developed in [4], [9], [10], and [11], our
approach additionally represents the mobility of users through arrival
rates of new GSM and GPRS users as well as handover rates of GSM and
GPRS users from neighboring cells. Furthermore, we consider users with
different QoS profiles modeled by a weighted fair queueing scheme
according to [5]. The remainder of the paper is organized as follows.
Section 2 describes the basic GPRS network architecture, the radio
interface, and different QoS profiles, which will be considered in the
simulator. In Section 3 we describe the software architecture of the
GPRS simulator, details about the mobility of GSM and GPRS users, the
way we modeled quality of service profiles, and the workload model we
used. Results of the simulation studies are presented in Section 4. We
provide curves for average carried traffic and packet loss probabilities
for different channel allocation schemes and packet priorities as well
as curves for average throughput per GPRS user.
3 The Simulation Model
?GSM and ?GPRS, respectively. GSM calls are handled circuit-switched, so
that one physical channel is exclusively dedicated to the corresponding
mobile station. After the arrival of a GPRS call, a GPRS session begins.
During this time a GPRS user allocates no physical channel exclusively.
Instead the radio interface is scheduled among different GPRS users by
the Base Station Controller (BSC). Every GPRS user receives packets
according to a specified workload model. The amount of time that a
mobile station with an ongoing call remains within the area covered by
the same BSC is called dwell time. If the call is still active after the
dwell time, a handover toward an adjacent cell takes place. The call
duration is defined as the amount of time that the call will be active,
assuming it completes without being forced to terminate due to handover
failure. We assume the dwell time to be an exponentially distributed
random variable with mean 1/?h,GSM for GSM calls and 1/?h,GPRS for GPRS
calls. The call durations are
also exponentially distributed with mean values 1/?GSM and 1/?GPRS for
GSM and
GPRS calls, respectively. To exactly model the user behavior in the
seven cell cluster, we have to consider the handover flow of GSM and
GPRS users from adjacent cells. At the boundary cells of the seven cell
cluster, the intensity of the incoming handover flow cannot be
specified in advance. This is due to the handover rate out of a cell
depends on the
?h GSM
in i ,
( ) ( ) ?1 computed at step i-1.
Since in the end-to-end path, the wireless link is typically the
bottleneck, and given
the anticipated traffic asymmetry, the simulator focuses on resource
contention in the
downlink (i.e., the path BSC ??BTS ??MS) of the radio interface. Because
of the anticipated traffic asymmetry the amount of uplink traffic, e.g.
induced by
acknowledgments, is assumed to be negligible. In the study we focus on
the radio
interface. The functionality of the GPRS core network is not included.
The arrival
stream of packets is modeled at the IP layer. Let N be the number of
physical channels available in the cell. We evaluate the performance of
two types of radio resource sharing schemes, which specify how the cell
capacity is shared by GSM and GPRS users:
??the static scheme; that is the cell capacity of N physical channels is
split into
NGPRS channels reserved for GPRS data transfer and NGSM = N – NGPRS
channels
reserved for GSM circuit-switched connections.
??the dynamic scheme; that is the N physical channels are shared by GSM
and
GPRS services, with priority for GSM calls; given n voice calls, the
remaining
N-n channels are fairly shared by all packets in transfer.
In both schemes, the PDCHs are fairly shared by all packets in transfer
up to a
maximum of 8 PDCHs per IP packet (“multislot mode”) and a maximum of 8
packets
per PDCH [6].
The software architecture of the simulator follows the network
architecture of the
GPRS Network [14]. To accurately model the communication over the radio
interface, we include the functionality of a BSC and a BTS. IP packets
that arrive at
the BSC are logically organized in two distinct queues. The transfer
queue can hold
up to Q n ????8 packets that are served according to a processor sharing
service
discipline, with n the number of physical channels that are potentially
available for
data transfer, i.e. n = NGPRS under the static scheme and n = N under
the dynamic
scheme. The processor sharing service discipline fairly shares the
available channel
capacity over the packets in the transfer queue. An arriving IP packet
that cannot enter
the transfer queue immediately is held in a first-come first-served (in
case of one
priority) access queue that can store up to K packets. The access queue
models the
BSC buffer in the GPRS network. Upon termination of a packet transfer,
the IP
packet at the head of the access queue is polled into the transfer
queue, where it
immediately shares in the assignment of available PDCHs. For this study,
we fix the
modulation and coding scheme to CS-2 [14]. It allows a data transfer
rate of 13,4
kbit/sec on one PDCH. Figure 1 depicts the software architecture of the
simulator.
Figure 1. Software Architecture of GSM/GPRS Simulator
To model the different quality of service profiles GPRS provides, the
simulator
implemented a Weighted Fair Queueing (WFQ) strategy. The WFQ scheduling
algorithm can easily be adopted to provide multiple data service classes
by assigning
each traffic source a weight determined by its class. The weight
controls the amount
of traffic a source may deliver relative to other active sources during
some period of
time. From the scheduling algorithm’s point of view, a source is
considered to be
active if it has data queued at the BSC. For an active packet transfer
with weight wi
the portion of the bandwidth ?i(t) allocated at time t to this transfer
should be
( ) ( ) ?????
where the sum over all active packet transfers at time t. The overall
bandwidth at time
t is denoted by B(t) which is independent of t in the static channel
allocation scheme.
The workload model used in the GPRS simulator is a Markov-modulated
Poisson
Process (MMPP) [7]. It is used to generate the IP traffic for each
individual user in
the system. The MMPP has been extensively used for modeling arrival
processes,
because it qualitatively models the time-varying arrival rate and
captures some of the
important correlations between the interarrival times. It is shown to be
an accurate
model for Internet traffic which usually shows self-similarity among
different time
scales. For our purpose the MMPP is parameterized by the two-state
continuous-time
Markov chain with infinitesimal generator matrix Q and rate matrix ?:
0
The two states represent bursty mode and non-bursty mode of the arrival
process.
The process resides in bursty mode for a mean time of 1/??and in
non-bursty mode for
a mean time of 1/??respectively. Such an MMPP is characterized by the
average
arrival rate of packets, ?avg and the degree of burstiness, B. The
former is given by:
1 2
The degree of burstiness is computed by the ratio between the bursty
arrival rate and
the average arrival rate, i.e., B = ?1/?avg.
4 Simulation Results
Table 1 summarizes the parameter settings underlying the performance
experiments.
We group the parameters into three classes: network model, mobility
model, and
traffic model. The overall number of physical channels in a cell is set
to N = 20
among which at least one channel is reserved for GPRS. The overall
number of GPRS
users that can be managed by a cell is set to M = 20. As base value, we
assume that
5% of the arriving calls correspond to GPRS users and the remaining 95%
are GSM
calls. GSM call duration is set to 120 seconds and call dwell time to 60
seconds, so
that users make 1-2 handovers on average. For GPRS sessions the average
session
duration is set to 5 minutes and the dwell time is 120 seconds. Thus, we
assume
longer “online times” and slower movement of GPRS users than for GSM
users. The
average arrival rate of data is set to 6 Kbit/sec per GPRS user
corresponding to 0.73
IP packets per second of size 1 Kbyte.
Parameter
Figure 2 presents curves for carried data traffic and packet loss
probabilities due to
buffer overflow in the BSC for the static channel allocation scheme and
one packet
priority. For GPRS 1, 2, and 4 PDCHs are reserved, respectively. The
remaining
channels can be used by GSM calls. With 4 PDCHs the system overloads at
an arrival
rate of 0.8 GSM/GPRS users per second. This corresponds to an average of
12 GPRS
users in the cell (see Figure 7). In Figure 3 we present corresponding
curves for the
dynamic channel allocation scheme. For GPRS 1, 2, and 4 PDCHs are
reserved,
respectively but more PDCHs can be reserved “on demand”. That means that
additional PDCHs can be reserved if they are not used for GSM voice
service. From
Figure 3 we observe that for low traffic in the considered cell GPRS
makes
effectively use of the on demand PDCHs. For example if 1 PDCH is
reserved GPRS
utilizes up to 2 PDCHs at an arrival rate of 0.4 GSM/GPRS users per
second. But
with increasing load the overall performance of GPRS decreases because
of
concurrency among GPRS users, and more important, priority of GSM users
over the
radio interface. In comparison with the static channel allocation scheme
we conclude
that the combination of reserved PDCHs and on demand PDCH leads to a
better
utilization of the scarce radio frequencies. The only advantage of the
static channel
allocation scheme is that it can be realized more easily.
Figure 4 presents a comparison of overall channel utilization and
average
throughput per GPRS user for the static and dynamic channel allocation
scheme. For
the static scheme we reserved 2 and 4 PDCHs respectively and for the
dynamic
scheme only 1 PDCH. We observe a higher overall utilization of physical
channels by
the dynamic scheme. Comparing the dynamic with the static scheme for 2
PDCHs we
detect a slightly higher throughput for low traffic load for dynamic
channel allocation.
This results from the high radio channel capacity available to GPRS
users in this case.
They can utilize up to 8 PDCHs for their transfer (in contrast to 2
PDCHs in the static
scheme). When load increases, GSM calls allocate most of the physical
channels.
Thus, throughput for GPRS users decreases very fast. In the static
scheme (4 PDCHs)
the decrease in throughput is not so fast, because GSM calls do not
effect the PDCHs.
In an additional experiment, we study the performance loss in the GSM
voice
service due to the introduction of GPRS. Figure 5 plots the carried
voice traffic and
voice blocking probability for different numbers of reserved PDCHs. The
results are
valid for both channel allocation schemes because of the priority of GSM
voice
service over GPRS. The presented curves indicate that the decrease in
channel
capacity and, thus, the increase of the blocking probability of the GSM
voice service
is negligible compared to the benefit of reserving additional PDCHs for
GPRS users.
Figure 6 shows carried data traffic and packet loss probabilities for
the dynamic
channel allocation scheme and different packet priorities. For GPRS 1
PDCH is
reserved. Weights for packets with priority 1 (high), 2 (medium), and 3
(low) and
percentages of GPRS users utilizing these priorities are given in Table
1. We observe
that for low traffic in the considered cell most channels are covered by
packets of low
priority. This is due to the high portion of low priority packets (60%)
among all
packets sharing the radio interface. With increasing load medium
priority packets and
at last high priority packets suppress packets of lower priority and
therefore the
utilization of PDCHs for low and medium priority packets decreases. For
a call arrival
rate of up to 2 calls per second the loss probability of high priority
packets is still less
than 10-5 and therefore the corresponding curve is omitted in Figure 6.
Figure 7 presents curves for average number of GPRS users in the cell
and
blocking probabilities of GPRS session requests due to reaching the
limit of M active
GPRS sessions. We observe that for 2% GPRS users the maximum number of
20
active GPRS sessions is not reached. Therefore, the blocking probability
remains very
low. For 10% GPRS users and increasing call arrival rate, the average
number of
sessions approaches its maximum. Thus, some GPRS users will be rejected.
It is
important to note that the curves of Figure 7 can be utilized for
determining the
average number of GPRS users in the cell for a given call arrival rate.
In fact, together
with the curves of Figure 2 and 3, we can provide estimates for the
maximum number
of GPRS users that can be managed by the cell without degradation of
quality of
service. For example, for 5% GPRS users and 1 PDCHs reserved, in the
static
allocation scheme a packet loss probability of 10-3 can be guarantied
until the call
arrival rate exceeds 0.4 calls per second, i.e., until there are on the
average 6 active
GPRS users in the cell. For the dynamic allocation scheme a packet loss
probability of
10-3 can be guarantied until the call arrival rate exceeds 0.6 calls per
second
corresponding to 9 active GPRS users in the cell on average. Figure 8
investigates the impact of the maximum number of GPRS user per cell to
the performance of GPRS for the dynamic channel allocation scheme with 1
PDCH reserved. Of course, the expected number of GPRS users should be
less than the maximum number in order to avoid the rejection of new GPRS
sessions. On the other hand, the maximum number of active GPRS sessions
must be limited for guaranteeing quality of service for every active
GPRS session even under high traffic. The tradeoff between increasing
performance for allowing more active GPRS sessions and the
increasing blocking probability for GPRS users is illustrated by the
curves of Figure 8.
Conclusions
This paper presented a discrete-event simulator on the IP level for the
General Packet Radio Service (GPRS). With the simulator, we provided a
comprehensive performance study of the radio resource sharing by circuit
switched GSM connections and packet switched GPRS sessions under a
static and a dynamic channel allocation
scheme. In the dynamic scheme we assumed a reserved 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. In the static scheme no ondemand channels exist. We
investigated the impact of the number of packet data
channels reserved for GPRS users on the performance of the cellular
network. Furthermore, three different QoS profiles modeled by a weighted
fair queueing scheme were considered. Comparing both channel allocation
schemes, we concluded that the dynamic scheme is preferable at all. The
only advantage of the static scheme lies in its easy implementation.
Next, we studied the impact of introducing GPRS on GSM voice service and
observed that the decrease in channel capacity for GSM is negligible
compared to the benefit of reserving additional packet data channels for
GPRS. With the curves presented we provide estimates for the maximum
number of GPRS users that can be managed by the cell without degradation
of quality of service. Such results give valuable hints for network
designers on how many packet data channels should be allocated for GPRS
and how many GPRS session should be allowed for a given amount of
traffic in order to guarantee appropriate quality of service.
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