Evaluating the GPRS Radio Interface for Different Quality of Service Profiles

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
We consider a cluster comprising of sever hexadiagonal cells in an
integrated GSM/GPRS network, serving circuit-switched voice and packet-
switched data calls. The performance studies presented in Section 4 were
conducted for the innermost cell of the seven cell cluster. We assume that
GSM and GPRS calls arrive in each cell according to two mutually
independent Poisson processes, with arrival rates ?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
number of active customers within the cell. On the other hand, the handover
rate into
the cell depends on the number of customers in the neighboring cells. Thus,
the
iterative procedure introduced in [2] is used to balance the incoming and
outgoing
handover rates, assuming that the incoming handover rate ?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
 ( ) ( ) ’ ? S
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.