LTE Speech Traffic Estimation for Network Dimensioning

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LTE Speech Traffic Estimation for Network Dimensioning UTRAN Traffic Performance Approach Elias Jailani, Muhamad Ibrahim Faculty of Electrical Engineering Universiti Teknologi MARA, 40450 Shah Alam,

1 LTE Speech Traffic Estimation for Network Dimensioning UTRAN Traffic Performance Approach Elias Jailani, Muhamad Ibrahim Faculty of Electrical Engineering Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia Ruhani Ab Rahman Faculty of Electrical Engineering Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia Abstract- LTE voice application is based on packet IP which is known as VoLTE by GSMA. To estimate VoLTE traffic, the UTRAN voice calls need to correctly convert into full-ip traffic and impose with a correct LTE codec. The knowledge to estimate data rate requirements is very important for network planning & dimensioning. This paper provides a dimensioning approach for LTE network using the readily available UTRAN traffic data from the speech traffic perspective. The estimated traffic throughput requirement per enodeb as well as overall LTE network cluster is presented and can be used to dimension the eutran as well as IP Transport nodes. Keywords- Long Term Evolution (LTE), UMTS Terrestrial Radio Access Network (UTRAN), Third Generation (3G), Voice over LTE (VoLTE), Worldwide Interoperability for Microwave Access (WiMAX)) I. INTRODUCTION Long Term Evolution (LTE) was designed with high-speed mobility to cater originally with the requirement for subscribers riding in high speed train/vehicle in Europe. Both WiMAX and LTE share the same radio technology of OFDMA (Orthogonal Frequency Division Multiple Access) [1]. With LTE RAN capable to deliver 300Mbps on the downlink and 75Mbps on the uplink, thus the user access portion may not a crucial factor during early deployment. Operators may face issue on how to transport this huge application data over the existing IP infrastructure, especially when existing UTRAN subscribers start migrating to LTE network. LTE services are pure packet type of services. Figure 1 shows the LTE network topology. II. METHODOLOGY A. Study on UTRAN traffic Data The research study was conducted in urban area of Kuantan, a town in the east coast of Peninsular of Malaysia. A total of 173 cells were gathered for a duration period of 3 weeks: from 23 March to 14 April Data collection is carried out using Network Performance Optimizer (NPO) tool that are available in operator s central Network Management System reside in Petaling Jaya [3]. NPO capable to provide variety of performance data nearing ten thousand indicators and counters that were subgroup into [4]: Accessibility, Feature Monitoring, HSDPA/HSUPA, Load & Capacity, Mobility, Quality, Retain ability, Topology and Traffic. For this work, only Traffic counters/indicators are used. Further study indicates only 10 counters are required specifically related to traffic. Figure 2 shows the study approach. Figure 2: Research Study approach flow-diagram B. Definition of UTRAN Traffic Counters Table 1 shows a table definition on 10 counters used in this work. Voice Adaptive Multi-Rate (AMR) CS Erlang and Video CS Erlang is defined by calculating as follows: AMR Erlang = VS.DlAsConfIdAvgNbrEstablished. Cum * 0.1 / Figure 1: LTE Network Topology native IP [2] For example, if a single user establishes a call that lasts for the entire hour, 1 Erlang of traffic is generated according to the definition. Going back to the formula above, the counter shall give: /12/$ Crown 315

2 DlAsCnfCsSpeechNbLrAmr_Cum [t=0] = 0; DlAsCnfCsSpeechNbLrAmr_Cum [t=100ms] = 1; DlAsCnfCsSpeechNbLrAmr_Cum [t=200ms] = 2; DlAsCnfCsSpeechNbLrAmr_Cum [t= ms] = This is equal to 1 Erlang. The same is applicable for DlAsCnfCsData_Cum counter which is referred to video call. TABLE 1. COUNTER DEFINITION Call Attempts Time Figure 3: Call arrival pattern for speech The call arrival on Figure 3 is for cell EW10010_1_1 that shows that speech service resemble the Random call Arrival Pattern. Thus, Erlang traffic model can be used for this case. D. Counters Grouping, Periods & Busy Hour The 10 counters are grouped according to the services on how the counters are incremented. The analyses are made based on the services group called Speech call, Video call and Packet switch call. Hourly data are selected instead of daily data to show the behavior of the traffic during the data period taken. The best situation is to take the busy-hour. The following information can be derived from hourly data are Daily busy hour, Weekly busy hour, Monthly busy hour and Busy hour of a cluster (Group of cells). C. Tele-Traffic Theory & Call Arrival Pattern One of major influencer that identifies the model is Call Arrival Pattern. There are three main call arrival patterns namely, Smooth call Arrival Pattern, Peaked Call Arrival Pattern and Random Call Arrival Pattern. A smooth or hypo-exponential traffic pattern occurs when there is a small amount of variation in traffic. Peaked call or also known as hyper-exponential call pattern with longer spikes from the mean and requires extra capacity to manage peak period. Poisson & exponential distribution is random traffic pattern. The call arrival is unpredictable and arrive in random. Normally this pattern is generated by many callers but each contributed only small traffic [5]. The data gathered have 3 counters related to call arrival to differentiate the three services of speech, video and packetcall. E. Voice over LTE (VoLTE) VoLTE is Voice over packet call & transmitted via IP to evolve Packet Core (epc). The VoIP IMS based profiles defines a minimum mandatory set of features a wireless device and network are required to implement in order to guarantee an interoperable, high quality IMS-based telephony service over LTE radio access [6]. During conversation, the speech signal will be digitized & compressed by a vocoder. The codec used in LTE is AMR which were described in 3GPP TS specification. The total of 160 samples is generated on every encoded AMR speech frame [7]. This encoded voice are packetize into voice frame in the transport layer called Real Time Transport Protocol (RTP). F. Coding, Packet Header and Packet Rate Packetization and coding rate can influence the gross bandwidth throughput of VoLTE. AMR has 8 modes with different code rate where the higher coding rate, the better speech quality. AMR codec samples the waveform at regular interval with 8000 samples per second. These samples are then concatenated into VoIP packet for a fixed period 20ms. Common usage in AMR code is 1 frame per packet. The choice of having more frames per packet is a trade-off between latency and IP transmission overhead. However, 316

3 VoLTE through RFC 3551 specification does not specify this figure. Total header for VoIP packet in VoLTE is 78 octets. The IP packet rate can be calculated using: Packet rate = Total load (octet) x packet per second number of frame per packet where; Total load = Total header + payload, Total header = Ethernet header + IP header + UDP header + RTP header Payload = frame octet x number of frame. For AMR mode 0: Payload = (95/8) x 1 = octets Total header = = 78 octets Total load = = octets Thus; Packet rate = ( x 50) / 1 = octets/sec = kbps The above calculation is based on bandwidth-efficient mode, whereby the payload is consists of digitized speech bit. For more robust, octet aligned mode is chosen. In this mode, the remaining bit in an octet of payload is filled with padding bit. For octet aligned mode: Payload = 12 octets Total header = 78 octets Total load = 90 octets Resulted; Packet rate = (90 x 50) / 1 = 4500 octets/sec = 36 kbps transmission data during silence period. During silence period the UE will send Silent Indicator frame in the payload instead of speech payload together with a timestamp [6]. Similarly, the UE will insert silence mode called Discontinuous Transmission (DTX) in RAN. I. Codec Mode Adaptation Codec Mode Adaptation was aimed to maintain the AMR speech call under a wide range of transmission media. This technique enables UE and network to adapt the codec mode according to the link transmission quality. If the IP link media is bad, source coding is reduced and coding mode is increased. To perform mode adaptation, the decoder (speech receiver) needs to signal the encoder (speech sender) the new mode it prefers [7]. The signaling is called Codec Mode Request (CMR) and transmitted over the speech frame. III. RESULTS AND DISCUSSION The IP packet rate for VoLTE calculated on both AMR and AMR-WB are shown in Table 3. These figures are used to estimate VoLTE traffic on S1-U interface. TABLE 3: RESULTED IP BANDWIDTH CONSUMPTION FOR AMR AND AMR-WB G. Robust Header Compression (RoHC) RoHC was specified in 3GPP TS IETF RFC 3095 and RFC 4815 which explained the compression scheme for IP/UDP/RTP header [8][9]. The VoLTE IP/UDP/RTP is a constant 40 octets in length as shown in Figure 4. This size is large as compared with the payload of 12 octets for AMR mode 0. This header value corresponds to 51% of the total frame size. Figure 4: Header and payload size for AMR Mode 0 with & without RoHC H. Voice Activity Detection (VAD) AMR like others codec such as G.729B and G.723.1A has the capability to detect talk-spurts and silence gaps. Voice Activity Detection (VAD) suppresses If RoHC is used the better IP bandwidth consumption will be achieved. Figure 5 & 6 show a comparison of bandwidth 317

consumption with and without RoHC and the bandwidth consumption saving generated.

4/1/2010 4/2/2010 4/3/2010 4/4/2010 4/5/2010 4/6/201 0 4/7/2010 Figure 8: Traffic carried during busy hour for different cells EB10056_1_7 EB10058_1_7 EB10058_1_8 EB10094_1_7 EW10002_1_1 EW10002_1_2

Capacity shifting can be implemented from the constantly low traffic of cell EB10058_1_7 to high traffic cell EB10056_1_7.

4 consumption with and without RoHC and the bandwidth consumption saving generated Figure 5: Calculated AMR IP bandwidth consumption saving with Robust Header Compression /24/2010 3/25/2010 3/26/2010 3/27/2010 3/28/2010 3/29/ /30/2010 3/31/2010 4/1/2010 4/2/2010 4/3/2010 4/4/2010 4/5/2010 4/6/ /7/2010 Figure 8: Traffic carried during busy hour for different cells EB10056_1_7 EB10058_1_7 EB10058_1_8 EB10094_1_7 EW10002_1_1 EW10002_1_2 Figure 7 shows daily busy hour trend for 6 different cells and Figure 8 shows the corresponding speech carried traffic in Erlang on the 6 cells. Capacity shifting can be implemented from the constantly low traffic of cell EB10058_1_7 to high traffic cell EB10056_1_7. Therefore, all traffic dimensioning including for future LTE network must be based on the analysis of busy-hour for the worst-case traffic scenario. Figure 6: Calculated AMR-WB IP bandwidth consumption saving with Robust Header Compression A. Determination of Busy-Hour Dimensioning a network such as LTE must be based on peak-hour or busy-hour traffic. Balancing between busy-hour and non-busy hour at different cells enable mobile operator to implement capacity shifting from one cell to the other to cater for a specific busy hour. (i.e. capacity requirement during peak hour of the month). For cluster network dimensioning, overall summation of cells traffic at every hours within monitoring period need to be used to determine the cluster busy hour. Figure 9 indicates the busiest hour for Kuantan town cluster falls at 10:00-hour with Erlangs. This figure will be used to estimate the requirement on transport node /24/2010 3/25/2010 3/26/2010 3/27/2010 3/28/2010 3/29/2010 3/30/2010 3/31/2010 4/1/2010 4/2/2010 4/3/2010 4/4/2010 4/5/2010 4/6/2010 4/7/2010 Figure 7: Cell Busy hour of the day for 6 different cells EB10056_1_7 EB10058_1_7 EB10058_1_8 EB10094_1_7 EW10002_1_1 EW10002_1_2 Figure 9: Total Erlangs figure for all cells in Kuantan town cluster. B. Converting to LTE speech traffic -VoLT The UTRAN Erlang traffic will be converted based on 1 Erlang that is equal to 1 active voice for 1 hour (3600 seconds) and divided by 8 bits to get the unit in kbytes. The data used is based on daily busy hour to reflect the peak period which can be used for dimensioning purposes. Figure 10 shows UTRAN speech traffic for the selected 5 sites with a total 15 cells. 318

Figure 10: UTRAN Erlang value during busy hour of the day The selected sites are chosen based on their high traffic behavior throughout the monitoring period.

Figure 12: Comparison of speech data requirement at cell EW10072_1_1 for different type of AMR Codec used with and without RoHC and VAD D.

Figure 11: LTE VoLTE Traffic using AMR Mode 0 during busy hour of the day Analysis on cell VoLTE traffic can be used to determine the requirements for LTE cell and enodeb IP transport capacity. C.

We can also see the differences in-terms of capacity requirement if RoHC & VAD is used.

5 Figure 10: UTRAN Erlang value during busy hour of the day The selected sites are chosen based on their high traffic behavior throughout the monitoring period. Site names: EW10003_1, EW10006_1, EW10010_1, EW10029_1, EW10072_1. Figure 11 is the corresponding traffic data after implementing using packet data VoLTE. Figure 12: Comparison of speech data requirement at cell EW10072_1_1 for different type of AMR Codec used with and without RoHC and VAD D. Dimensioning of LTE Network Figure 13 shows the total bandwidth requirement for site EW10072_1. This site consists of 3 sector cells of EW10072_1_1, EW10072_1_2 and EW10072_1_3. Figure 11: LTE VoLTE Traffic using AMR Mode 0 during busy hour of the day Analysis on cell VoLTE traffic can be used to determine the requirements for LTE cell and enodeb IP transport capacity. C. Comparison on bandwidth consumption (with & without RoHC & VAD) In order to know total traffic requirement for single enodeb, all the three shared cells of the same enodeb need to be summed. We can also see the differences in-terms of capacity requirement if RoHC & VAD is used. Figure 12 shows the comparison of speech data requirement at cell EW10072_1_1 for different type of AMR Codec used with and without RoHC and VAD. Figure 13: Resulted bandwidth requirement for speech at site EW10072_1 for AMR Mode 7 codec The dimensioning on this site is based on 7 April busyhour date which is the busiest day during the monitoring period. Total bandwidth requirement is Kbytes in one hour or kbps for using AMR mode 7 codec rate with Header Compression & VAD activated. To determine how much IP bandwidth is needed on S1-U interface, a collection of all cells traffic on every hour for a total 24 hours need to be analyzed. The busiest hour traffic of this summation data is taken for the calculation. We can easily see the advantage of using RoHC and VAD. Thus, higher AMR coding rate is possible with lower Ethernet bandwidth as shown in Figure

FUTURE RESEARCH RECOMMENDATION This study on LTE dimensioning only covers the speech traffic; signaling, video and other application traffics should be addressed as well to determine LTE network

CONCLUSION UTRAN mobile operator requires the initial dimensioning so that the operator will not over-spend into the new technology, instead to adequately deploy the new technology network according

6 FUTURE RESEARCH RECOMMENDATION This study on LTE dimensioning only covers the speech traffic; signaling, video and other application traffics should be addressed as well to determine LTE network requirements. CONCLUSION UTRAN mobile operator requires the initial dimensioning so that the operator will not over-spend into the new technology, instead to adequately deploy the new technology network according the end-users demand. In addition, mobile operator will be able to gauge level of readiness on it IP network to take the new LTE traffic load. It has been observed that the gross bandwidth throughput of VoLTE depends on the packetization method and coding rate chosen. Figure 14: Total speech traffic for Kuantan cells cluster based on AMR Mode 7 with and without RoHC and VAD The busiest hour period is at 10 am with Mbytes traffic, which is equivalent to 65Mbit/s total bandwidth requirement in the IP network. This is shown in Figure 14. Thus, dimensioning needed to cater for speech traffic in the area of Kuantan town cluster is: kbps on enodeb for site EW10072_1 (as example) 65 Mbit/s on S1-U interface connected to Packet Gateway (P-GW) Figure 15: Resultant total bandwidth requirement on S1-U interface with different AMR codec modes. ACKNOWLEDGMENT I am grateful to Alcatel-Lucent NPI Asia Pacific team for providing the cell data. REFERENCES [1] Erik Dahlman, Hannes Ekström, Anders Furuskär, Ylva Jading, Jonas Karlsson, Magnus Lundevall, Stefan Parkvall of Ericsson Research, The 3G Long-Term Evolution Radio Interface Concepts and Performance Evaluation, IEEE [2] Alcatel-Lucent, Long Term Evolution (LTE) Overview, October 2008 [3] Alcatel-Lucent, NPO Introduction: NPO Document Concept Guide Release M3,2009. [4] Alcatel-Lucent, Indicators Reference Guide: Baseline Indicators, [5] Cisco Press, Traffic Analysis for Voice over IP, [6] V1.0.0, One voice, One Voice; Voice over IMS profile, [7] J. Sjoberg, M. Westerlund, A. Lakaniemi & Q. Xie. Network Working Group RFC 4867, April [8] C. Bormann, C. Burmeister, M. Degermark,H. Fukushima,H. Hannu, L- E. Jonsson, R. Hakenberg, T. Koren, K. Le, Z. Liu, A. Martensson, A. Miyazaki, K. Svanbro, T. Wiebke, T. Yoshimura, H. Zheng, Network Working Group RFC 3095, July [9] L-E. Jonsson, K. Sandlund, G. Pelletier, P. Kremer, Network Working Group RFC 4815, February [10] M. Wernersson, Scheduling of Multi-Media over 3GPP LTE, Lulea Tekniska Universitet, March [11] S.Pracht, D. Hardman, Voice Quality in Converging Telephony and IP Networks, Agilent Technologies, October

Request for Comments: 3243 Category: Informational April 2002

Request for Comments: 3243 Category: Informational April 2002 Network Working Group L-E. Jonsson Request for Comments: 3243 Ericsson Category: Informational April 2002 RObust Header Compression (ROHC): Requirements and Assumptions for 0-byte IP/UDP/RTP Compression