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Simulation of Quality of Service Mechanisms in the UMTS Terrestrial Radio Access Network

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Simulation of Quality of Service Mechanisms in the

UMTS Terrestrial Radio Access Network

A. B. García, M. Álvarez-Campana, E. Vázquez and J. Berrocal

Departamento de Ingeniería de Sistemas Telemáticos, Ciudad Universitaria s/n, 28040 Madrid, Spain {abgarcia, mac, enrique, berrocal}@dit.upm.es

Abstract - Supporting multiple traffic classes with different QoS (Quality of Service) constraints in third generation mobile systems is not a straightforward problem. This issue becomes critical at the access network interfaces, where transmission resources are usually expensive. One of the most important interfaces is the one that connects each base station with its controller, because there can be many instances of it. We have approached the dimensioning of this interface from the perspective of simulation, concretely for the UMTS (Universal Mobile Telecommunications System) access network. At this respect we have developed a simulation model capable of representing multi-service scenarios inside this particular UTMS interface. This tool can be fed with traffic of different types, representing the different traffic classes defined for UMTS, and is able to simulate traffic differentiation both at ATM (Asynchronous Transfer Mode) and AAL2 (ATM Adaptation Layer 2) level. In the paper we present simulation results showing how the simulator can aid us in the task of access network dimensioning with QoS constraints.

Keywords: UMTS; Radio Access Network; ATM; AAL2; Quality of Service; Simulation.

1. INTRODUCTION

Third generation (3G) mobile communications systems will offer high-speed mobile access to a great variety of services in a world-wide scope. Some of these services require certain QoS (Quality of Service) constraints to be met by the network in order to function properly (for instance, multimedia applications). In the case of UMTS (Universal Mobile Telecommunications System), the 3GPP (3rd Generation Partnership Project) has decided not to standardize a closed group of services; instead, an open QoS architecture is specified, including the definition of four QoS traffic classes and a group of QoS parameters (tolerance to delay and losses among them).

The UMTS QoS classes are: conversational, streaming, interactive and background; they are defined in 3GPP specification TS 23.107 [1], together with their QoS parameter values. To efficiently accommodate the different traffic classes, a careful network dimensioning process should be carried out. This process should take into account several factors, including the services demand forecast, traffic parameters and QoS constraints of the applications, and network topology.

In particular, the access network is one of the most critical parts of the system (see [2]), since both the air interface and the terrestrial transmission resources inside it can be considered scarce, and the QoS constraints are tight.

For the first release of UMTS, 3GPP has decided to specify ATM (Asynchronous Transfer Mode) as the transport technology inside the access network (UTRAN – UMTS Terrestrial Radio Access Network). ATM can be seen as a more flexible technology than traditional circuit switching methods. However, in order to take advantage of this flexibility, new dimensioning methods have to be envisaged for the access network, since models used in second generation (2G) systems are no longer adequate.

Due to the many factors to be considered and to the technological change with respect to 2G systems, analytical solutions for the UTRAN dimensioning problem seem difficult to obtain. This is why we have developed a simulation model, suitable for the performance analysis and dimensioning of the UTRAN interface connecting each base station (or Node-B) and its controller (or RNC – Radio Network Controller). After giving a brief description of the simulation model, we will present some results obtained with this tool under specific traffic and network scenarios.

2. SIMULATION MODEL

2.1. User Plane Protocol Stack

UTRAN terrestrial protocols are structured in two layers [3]: Radio Network Layer (RNL), consisting of all the UMTS-specific protocols, and Transport Network Layer (TNL), which includes generic protocols, and is in charge of conveying RNL data across the terrestrial interfaces with the necessary QoS.

Our simulator models the behavior of TNL at the User Plane of the Iub interface (the one between each Node-B and its controlling RNC). As we can see in Fig. 1, AAL2 (ATM Adaptation Layer 2) and ATM protocols are used at the TNL of Iub, above the physical layer (e.g. an E1 line).

We will suppose that each user session (whether packet switched or circuit switched) makes use of a dedicated transport channel (DCH), and the resulting DCH Frame Protocol (DCH FP) frames are carried by an AAL2 connection across Iub.

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PHY ATM AAL2 FP RNL TNL PHY ATM AAL2 FP Radio Protocols Peer protocols at the User Equipment Node-B RNC PHY ATM AAL2 FP RNL TNL PHY ATM AAL2 FP Radio Protocols Peer protocols at the User Equipment Node-B RNC

Fig. 1. Iub interface: User Plane protocol stack. 2.2. Traffic Characterization

For us every user is in an active state (e.g. during a voice call or a web browsing session). We assume that the number of simultaneously active users of each kind is a previously known parameter, which can be obtained as a result of the radio planning phase, for instance. Also, when the application exhibits an asymmetric behavior, we implicitly model downstream traffic, since typically the majority of the traffic is sent to the user equipment.

Since our goal is to study the TNL, the source model should include not only the end applications’ statistical properties, but also the peculiarities of the radio protocols used to convey the data. We decided to use the same kind of source model for the four UMTS QoS classes. However, its parameterization is specific to each traffic class. We distinguish two layers, called burst and packet, shown in Fig. 2 together with the relevant model parameters. In contrast with several traffic parameterization studies ([4], [5], [6]), our model does not include a session level, since, as stated above, every user is supposed to be inside an active session.

Each source follows a “High-Low” (or “generic” ON-OFF) pattern, modeling the variable-rate nature of many applications. The packet generation process parameters have to be set separately for each state. A packet represents a DCH FP frame at Iub; in the rest of the paper the terms packet and frame will be used without distinction.

High state duration Low state duration Packet size, High state Time between packets,

High state

Packet size, Low state Time between packets,

Low state Burst Packet High state duration Low state duration Packet size, High state Time between packets,

High state

Packet size, Low state Time between packets,

Low state Burst

Packet

Fig. 2. Source model in two layers.

The simulation results shown later include two applications, voice and web browsing, representing the conversational and interactive UMTS QoS classes respectively. An AMR (Adaptive Multi Rate) codec in 12.2 mode with SID (Silence Insertion Description) frames is used for voice. Web browsing has a download rate (speed at which a web document is downloaded) of 64 kbit/s. The statistical distribution of burst state durations is exponential, while a constant distribution has been used both for time between packets and packet sizes. Table 1 shows the mean values for each parameter distribution. These values have been derived from the recommendations given in Annex A of UMTS technical report TR 25.933 [7], AMR codec specification [8], an ETSI technical report to be used in UMTS evaluation phases [6], and relevant protocol overheads and characteristics.

TABLE 1

TRAFFIC CHARACTERIZATION PARAMETERS: MEAN VALUES

Voice Web Burst level parameters

High state duration 3 s 1.5 s Low state duration 3 s 412 s

Packet level parameters

Time between packets (High and Low) 20 ms 40 ms Packet size (High) 40 B 325 B

Packet size (Low) 13 B 0 B

2.3. Simulator Architecture

Fig. 3 shows the overall simulator architecture at the sending side. There is also a receiving module in charge of computing the relevant source, AAL2 and ATM results.

Src. Group 1 Src. Group n AAL2 Mux. AAL2 Mux. ATM VCC 1 ATM VCC m From other AAL2 mux’s To physical Line (B bit/s) L1 cells Lm cells P cells

•Sends cells at no more than PCR •Scheduling: FIFO or PRIOR

•Sends cells at the beginning of cell slots

•Scheduling: FIFO or PRIOR •Standard AAL2 segmentation

and multiplexing •Supports Timer_CU •Does not introduce losses

Src. Group 1 Src. Group n AAL2 Mux. AAL2 Mux. ATM VCC 1 ATM VCC m From other AAL2 mux’s To physical Line (B bit/s) L1 cells Lm cells P cells

•Sends cells at no more than PCR •Scheduling: FIFO or PRIOR

•Sends cells at the beginning of cell slots

•Scheduling: FIFO or PRIOR •Standard AAL2 segmentation

and multiplexing •Supports Timer_CU •Does not introduce losses

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Although we have represented only one physical line, several lines can be actually configured, each with its own bit rate (B bit/s). This bit rate determines the duration of one ATM cell slot (the time it takes to transmit one cell at B bit/s). The physical-line server will start sending each ATM cell (when available) at the beginning of a slot, and has a limited buffer space of P cells.

We can define one or several (m) ATM VCCs (Virtual Channel Connections) to be multiplexed into the same physical line, each one with an independent PCR (Peak Cell Rate). Other parameters that can be configured for a VCC are the buffer size (Li cells for the ith VCC) and an absolute priority which is meaningful among all the VCCs sharing the same physical line. If different priorities are assigned to several VCCs, the physical-line server will perform PRIOR (absolute priorities) scheduling accordingly.

The tool also allows us to define as many source groups as we want. A source group consists of a set of independent and identical traffic generators with the relevant parameters set as described in a previous subsection. Each group is assigned an AAL2 multiplexer that performs standard AAL2 segmentation and multiplexing tasks, including Timer_CU handling, as specified by ITU-T [9]. These two processes do not introduce any loss. However, the value of the AAL2 Timer_CU can affect the delay experienced by the frames.

We can also multiplex the output of several AAL2 multiplexers (i.e. the data of several source groups) into an ATM VCC. Again, in a similar manner as stated above for VCCs, each source group being multiplexed in a VCC can be assigned an absolute priority. This can be seen as a basic AAL2-level traffic differentiation, in line (although with a different method) with what has also been proposed in some related papers, for instance, [10], [11], [12], [13] and [14].

3. SIMULATION RESULTS

In this section we present results obtained with the simulator described above. All the experiments were performed with a physical-line bit rate B = 1 984 000 bit/s, corresponding to an E1 line, a physical line buffer length P = 10 cells, and Timer_CU = 1 ms. On one hand, these results show how the values of several parameters (e.g. the PCR of ATM VCCs) affect the main QoS parameters of the application frames in the studied UTRAN interface (basically loss ratio and delay). On the other hand, a post-processing of the data allows us to obtain dimensioning rules such as the minimum (with a certain tolerance) PCR that, under specific traffic scenarios, guarantees that the QoS constraints of the applications are met.

3.1. Single Traffic Class Scenarios

A set of simulations has been performed including users of a single traffic class in each one. Each experiment was

configured with specific values of VCC bit rate and number of users. In all cases, the VCC buffer size was set to a number of ATM cells equal to the number of users. The figures of this section are referred to FLR (Frame Loss Ratio) as QoS parameter. Analogous results could be obtained taking different objective parameters, such as frame delay (95th percentile).

Fig. 4 shows the frame loss ratio for voice traffic as a function of the VCC capacity (normalized to the peak bit rate of one voice source). The simulation results are compared with the estimated values provided by an analytical fluid-flow approximation [15]; these analytical estimations are plotted with dashed lines. From this kind of results we can derive the minimum VCC capacity necessary for a given number of voice users in order to meet specific FLR requirements, as can be seen in Fig. 5. Similar results are provided in Fig. 6 for web traffic. This kind of curves can be useful for dimensioning purposes with QoS constraints.

1E-05 1E-04 1E-03 1E-02 1E-01 1E+00 0 10 20 30 40 50 60 70 80 90 100 110 120 Normalized VCC capacity

Frame Loss Ratio

25 users 50 75 100 125 150

Analytical Simulation

Fig. 4. Frame loss ratio as a function of VCC capacity (voice).

0 500 1000 1500 2000 2500 25 50 75 100 125 150

Number of voice users

Minimum required capacity (kbit/s)

FLR < 10-1

FLR < 10-5

FLR < 10-3

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100 200 300 400 500 25 50 75 100 125 150

Number of web users

Minimum required capacity (kbit/s) FLR < 10

-2

FLR < 10-3

FLR < 10-4

FLR < 10-5

Fig. 6. Minimum VCC capacity to meet FLR requirements (web). 3.2. Traffic Mix Scenarios

We have also performed simulation runs in multi-service scenarios. Our goal in this case is to investigate the possible bandwidth savings that can be achieved by multiplexing different traffic classes in a single ATM VCC.

More specifically, in this section we show the results corresponding to a mix of 50 voice users and 50 web users. All of them are multiplexed into the same VCC, giving absolute priority to voice users, since their delay requirements are typically more stringent. The VCC buffer size is

L1 = 100 cells. Table 2 shows the QoS objectives at the Iub interface established as criteria for our analysis. Note that the FLR requirement is tighter for voice than for web. The reason for this is that we have supposed that RLC (Radio Link Control) error recovery procedures can be used for web traffic, but not for voice because the end to end delay would suffer inadmissible increments. Values at the last row of the table refer to the minimum VCC capacity required to meet the QoS requirements when using separated VCCs (i.e. in single-class scenarios).

The experiments carried out for the shared VCC strategy started by considering an aggregated capacity equal to the sum of capacities required for separated VCCs with 50 users. Then, we proceeded to decrease the aggregated capacity in steps of 5% (of the initial value). The results are shown in table 3. The single class column corresponds to the VCC capacities given in the last row of Table 2.

TABLE 2

QOS OBJECTIVES AND MINIMUM VCC CAPACITY TO MEET THEM

Voice Web

FLR < 10-4 < 10-3

Frame Delay (95th percentile) < 25 ms n/a Minimum VCC capacity (50 users) 756 kbit/s 257 kbit/s

TABLE 3

QOS PARAMETER VALUES OBTAINED IN SINGLE CLASS AND SHARED VCC SCENARIOS

Shared VCC Single Class 100% 95% 90% FLR 4.2×10-5 2.8×10-6 5.5×10-6 1.5×10-5 Frame Delay (mean) 5.0 ms 2.6 ms 3.0 ms 3.3 ms Voice Frame Delay (95th perc.) 8.5 ms 4.5 ms 5.0 ms 6.5 ms Web FLR 8.2×10 -5 1.8×10-4 4.2×10-4 1.2×10-3

The results prove that with a 5% of capacity reduction, all the QoS requirements are still met (for both traffic classes). However, for a 10% reduction, the web FLR exceeds the maximum allowed value. Therefore, we can conclude that the VCC sharing strategy does not lead to significant bandwidth savings.

Nevertheless, a more in depth analysis of the results reveals a beneficial effect of the shared VCC strategy, which is the reduction of frame delay for voice traffic. This phenomenon is a consequence of giving absolute priority to voice over web traffic. Furthermore, we observe a reduction on the voice frame delay variation (jitter), which is specially advantageous when dealing with packed voice.

Fig. 7 represents the delay histograms for voice frames obtained with the shared VCC strategy corresponding to 100%, 95% and 90% of the sum of capacities required for separated VCCs. The histogram for the only-voice scenario is shown in the upper left corner for comparison. As the capacity of the shared VCC decreases, the jitter reduction tends to vanish.

4. CONCLUSIONS

In this paper we have presented a simulation model suitable for the dimensioning of the UMTS access network interface between each Node-B and its controlling RNC. This tool supports multi-service scenarios and implements basic forms of both ATM and AAL2-level traffic differentiation. It provides a wide range of results, including the most significant performance measurements (frame loss ratio and delay among them), allowing for QoS-aware dimensioning figures to be obtained.

Several simulation results have been also shown, both for single class and for multi-class scenarios. Variable rate nature of some applications makes it possible to obtain statistical multiplexing gain (i.e. bandwidth savings) if they use a common transmission capacity. Bandwidth savings can be slightly increased if several traffic classes share resources. This strategy, however, poses a more important advantage, which is the possibility of reducing jitter for real time traffic.

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Only voice 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 0,00 2,00 4,00 6,00 8,00 10,00 Frame delay (ms) Shared VCC (95%) 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 0,00 2,00 4,00 6,00 8,00 10,00 Frame delay (ms) Shared VCC (100%) 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 0,00 2,00 4,00 6,00 8,00 10,00 Frame delay (ms) Shared VCC (90%) 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 0,00 2,00 4,00 6,00 8,00 10,00 Frame delay (ms)

Fig. 7. Voice frame delay histogram: single class (voice) and shared scenarios (100%, 95% and 90% of VCC capacity sum).

REFERENCES

[1] 3GPP, “QoS Concept and Architecture,” 3GPP TS 23.107.

[2] S. Dixit, Y. Guo, and Z. Antoniou, “Resource Management and Quality of Service in Third-Generation Wireless Networks,” IEEE Communications Magazine, pp 125-133, February 2001.

[3] 3GPP, “UTRAN Overall Description,” 3GPP TS 25.401.

[4] A. Reyes-Lecuona, E. González-Parada, E. Casilari, J. C. Casasola, and A. Díaz-Estrella, “A page-oriented WWW traffic model for wireless system simulations,” Proceedings of the 16th International Teletraffic Congress (ITC'16), Edinburgh, United Kingdom, pp. 1271-1280, June 1999.

[5] A. Klemm, C. Lindemann, and M. Lohmann, “Traffic Modeling and Characterization for UMTS Networks,” Proceedings of the Globecom, Internet Performance Symposium, San Antonio TX, November 2001. [6] ETSI, “Universal Mobile Telecommunications System (UMTS);

Selection procedures for the choice of radio transmission technologies of the UMTS,” TR 101 112 V3.2.0, April 1998.

[7] 3GPP, “IP Transport in UTRAN,” 3GPP TR 25.933.

[8] 3GPP, “AMR Speech Codec; General Description,” 3GPP TS 26.071. [9] ITU-T Recommendation I.363.2, “B-ISDN ATM Adaptation layer

specification: Type 2 AAL,” November 2000.

[10] O. Isnard, J.-M. Calmel, A.-L. Beylot, and G. Pujolle, “Handling Traffic Classes at AAL2 / ATM layer over the Logical Interfaces of the UMTS Terrestrial Access Network,” 11th IEEE International Symposium on Personal, Indoor and Mobile Radio Communication, PIMRC, London, England, Vol. 2, pp. 1464-1468, September 2000. [11] G. Eneroth, G. Fodor, G. Leijonhufvud, A. Rácz, and I. Szabó,

“Applying ATM/AAL2 as a Switching Technology in Third-Generation Mobile Access Networks,” IEEE Communications, Vol. 37 No. 6, pp. 112-122, June 1999.

[12] S.-K. Yoo and H.-S. Park, “Quality-of-Service Provisioning for Mobile Voice and Data Services over ATM Network using AAL2,” 3rd ICACT, Muju, Korea, 2001.

[13] H. Lim, S. Lee, D. Lee, K. Kim, K. Song, and C. Oh, “A New AAL2 Scheduling Algorithm for Mobile Voice and Data Services over ATM,” ITC-CSCC 2000, Pusan, Korea, vol. 1, pp. 229-232, July 2000. [14] J.-L.C. Wu, C.-H. Huang, and R.-T. Sheu, “Performance study of

AAL2 protocol for low-bit-rate multimedia services,” 15th International Conference on Information Networking, Proceedings, pp. 793–798, 2001.

[15] O. Hersent, D. Gurle, J.-P. Petit, “IP Telephony - Packet-based multimedia communications systems,” Chapter 7, Addison-Wesley, 2000.

References

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