Evaluating the GPRS Radio Interface for Different Quality of Service Profiles
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 ëGSM and ëGPRS, 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/µh,GSM for GSM calls and 1/µh,GPRS for GPRS calls. The call
durations are
also exponentially distributed
with mean values 1/µGSM and 1/µGPRS 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 ëh
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 Âi(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 Ë:
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/á and
in non-bursty mode for
a mean time of 1/â respectively. Such an MMPP is
characterized by the average
arrival rate of packets, ëavg 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 = ë1/ëavg.
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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