Study of Signaling Overhead Caused by Keep-Alive Message in LTE Network

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1 Study of Signaling Overhead Caused by eep-alive Message in LE Network Ziqi Zhang*, Zhuyan Zhao, Hao Guan, Deshan Miao, Zhenhui an* * Institute of Broadband Wireless Mobile Communications, Beijing Jiaotong University, Beijing, China Nokia Siemens Networks, Beijing, China *{, zhhtan}@bjtu.edu.cn {zhuyan.zhao, hao.guan, deshan.miao}@nsn.com Abstract eep-alive messages generated from always-on type applications on smartphones can lead to frequent transitions between Long erm Evolution (LE) Radio Resource Control (RRC) states, pushing significant increase of signaling traffic. In this paper, we investigate the performance of signaling overhead caused by keep-alive messages based on LE RRC state transition procedure for various RRC inactivity timer settings. We propose a series of metrics for evaluating the signaling load in terms of system resource consumption on data and control channels for both uplink and downlink. he results show that although the RRC signaling overhead make keep-alive messages transmission with low efficiency, it will not consume the system resource dramatically and overload LE network. When using short inactivity timer, keep-alive messages consumed 9% of Physical Uplink Shared Channel (), while when using long inactivity timer, total resource in Physical Uplink Control Channel (PUCCH) they consumed is the most % of total uplink resource. he results also show that proper inactivity timer setting will reduce the signaling overhead and improve the keep-alive message transmission efficiency. I. INRODUCION he explosive growth of smartphones and modern mobile data applications in both volume and diversity has posed a challenge to cellular networks nowadays. Although the average smartphone consumes only % of the data traffic that a laptop does, it can connect to the network in background hundreds or even thousands of times daily, especially driven by always-on applications including instant messaging and social networking which need to be constantly reachable by the network for incoming communications, pushing significant increase of signaling traffic []. As the next step in wireless technologies, LE is designed to enable fixed to mobile migration of internet applications and provide the capacity to support the explosion in demand for connectivity, it is crucial to study the impact of the signaling overhead caused by smartphones in LE network. eep-alive messages generated from always-on applications refer to the autonomous exchange of user plane data packets between the UE and the network in order to maintain IP connection because of the presence of firewalls and Network Address ranslator (NA), in the absence of a specific user interaction with device. his kind of traffic is generally low in volume and comprises packets that may be widely dispersed in time []. he presence of keep-alive messages will trigger frequent transitions between LE RRC states, i.e. RRC_Connected and RRC_Idle, controlled by RRC inactivity timer within the evolved Node B (enb), thus causing corresponding extra signaling traffic from the network perspective. Similar problems were studied in []-[]. he RRC connection frequency as a function of inactivity timer has given in [], showing that the frequency drops dramatically as the increase of inactivity timer. In [], the distribution of the instantaneous number of smartphones in RRC_Connected and other statistics of RRC state transition based on uplink background traffic have been calculated. Signaling messages per cell per second with different inactivity timer along with user velocity and the tradeoff between them have been studied in []. But how the keep-alive message transmission impact the resource usage of LE cell was not studied by []-[]. On the other hand, most of research recently mainly focus on analyzing power consumption due to keep-alive messages from the user perspective [-]. In this paper, we investigate the performance of signaling overhead caused by keep-alive messages based on LE RRC state transition procedure for various RRC inactivity timer settings. he signaling load on data and control channels for both uplink and downlink is represented by resource consumption according to a series metrics we proposed. he remainder of this paper is structured as follows. Section II introduces the system model. Section III provides the simulation parameters and assumptions, followed by the simulation results. Finally, section IV contains the conclusions and direction of future work. II. SYSEM MODEL A. RRC Protocol he Radio Resource Control (RRC) protocol is responsible for assigning radio resources between user equipment (UE) and the network. here are two LE RRC states as shown in Fig.. Fig.. LE RRC UE state handling

2 At RRC_Connected state, UE is ready for transmitting and receiving data. It also sends periodic measurements to enb in order to stay synchronized with a base station. From capacity evaluation point of view, RRC_Connected UE is considered as active user even if there is no actively scheduled data transfers. While at RRC_Idle state, UE monitors paging, but the enb is not aware of the UE s context information. After data transmission is completed both uplink and downlink radio schedulers start counting RRC inactivity timer. When a threshold is reached, the enb switches UE from RRC_Connected to RRC_Idle, releasing all the radio bearers established on the air interface. he inactivity timer restarts every time when there is a packet arrival before the timer expires. herefore longer inactivity timer may result in too many connected UEs which the enb will not be able to handle. On the other hand an extremely short break to RRC_Idle state will increase the amount of related signaling. As the state transition control parameters, RRC inactivity timer plays an important role in balancing the network signaling load and UE power consumption. When a keep-alive message is sent from uplink by UE/ downlink by networks, not only the keep-alive message itself but also the related RRC singling message are sent in the shared data channels. At the same time, resource of control channel is required for scheduling and feedback information. In LE network, Physical Downlink Shared Channel () and Physical Uplink Shared Channel () are used for data traffic transmission, Physical Downlink Control Channel (), Physical Uplink Control Channel (PUCCH) and Physical Random Access Channel () are control channel. For a better understanding of the cost of each RRC state transition, we use ABLE I based upon [] to represent an example of RRC connection setup/release sequence, including the specific contents of each signaling message as well as the size of them. hen the system resource consumption of shared data channels in terms of numbers or fractions of Physical Resource Blocks (PRBs) of the corresponding signaling messages evolved in one RRC state transition can be evaluated. he number of control channel resources (CCE) in is able to be evaluated by referring to another example of RRC Connection Setup/Release Sequence in []. B. Performance Metrics We provide a series of metrics to evaluate the influence of signaling overhead on LE network. Resource consumption is used to measure the signaling load on respective physical channels. Since both signaling traffic appeared in ABLE I and data traffic of keep-alive messages are transferred in shared data channel, we define E and E given in () and (), representing the efficiency of resource utilization during keepalive messages transmission, by calculating the ratio of resource consumed by signaling to data traffic in downlink (DL) and uplink (UL), respectively. For, we assume that one message is needed to transfer a data packet for indicating its physical resource, and define E in (), representing the efficiency of resource consumption in, by calculating the ratio of resource consumed by signaling traffic to data traffic in both direction. PRBs utilized by DL signaling PRBs utilized by DL data packets E PRBs utilized by UL signaling PRBs utilized by UL data packets E CCEs utilized by messages for RRC state transition E CCEs utilized by messages for data transfer () Moreover, we define the peak value of resource utilization caused by parallel keep alive message transmission in a LE cell to indicate the signaling load in the worst case. peak,, peak, and peak, given in ()-() are represent the maximum value of the ratio of resource consumption caused by keep-alive message to total resource of a LE cell in a sliding observation wiondow. peak, peak, peak, max(prbs utilized by signaling and data packets in DL) in PRBs available to max(prbs utilized by signaling and data packets in UL) in PRBs available to max(cces utilized by control signaling and data transfer) in CCEs available to () () () () () In the simulation, we assume that all UEs use the random access procedure to request resource for uplink transmission by sending a preamble, and the statistic of resource on is expressed in terms of the number of preamble. herefore the metric for peak resource consumption in can be expressed as follow: peak, max(preambles ) in otal preambles available to PUCCH is used for Scheduling Request (SR), Channel Quality Indicator (CQI) reports and Acknowledgement /Negative Acknowledgement (AC/NAC). he metric of PUCCH signaling overhead is defined as the ratio of resource consumed by dedicated usage to total UL resource, which is expressed as follow: () PRBs utilized by dedicated usage () PUCCH otal UL resource Based upon [], PRBs occupied by different kinds of usage can be expressed as follow: SR NUE MPUCCH SR (9) CQI NUE MPUCCH CQI () AN / NUE M PUCCH () where nprb represents the number of PRBs consumed by PUCCH for corresponding usage, M PUCCH is the number massages that one PRB can accommodate, N UE denotes the maximum number of connected state UEs within a cell and SR, CQI denotes the periodicities of SR, CQI resources respectively.

3 ABLE I. RRC CONNECION SEUP/RELEASE SEQUENCE MAC Step UL PDU Size Contents (of MAC PDU) (Bytes) DL UL DL Preamble Random Access Response -- RRC Connection Request -- RRC Connection Setup + UE Contention Resolution Identity -- MAC CE Buffer Status Report -- RRC Connection Setup Complete ( + NAS Service Request) -- RLC Status Report -- Security Mode Command -- 9 RRC Connection Reconfiguration ( + NAS: Activate Dedicated EPS -- Bearer Context Req ) Buffer Status Report -- Security Mode Complete -- RRC Connection Reconfiguration Complete -- RLC Status Report -- Buffer Status Report -- ULInformation ransfer ( + NAS: Activate Dedicated EPS Bearer -- Context Req ) RLC Status Report -- RRC Connection Release -- RLC Status Report -- otal Bytes 9 C. eep-alive Message raffic Model he methodology of generating keep-alive messages adopted in the simulation is based on [], in which a burst model has been used. Background traffic may be divided into light background and heavier background respectively []. Light background traffic corresponds to generally lower mean data rates and a lower mean number of packets per second as well as less burst of packets, and the heavier background traffic behaves on the contrary. In the simulation, we consider the aggregation traffic including both UL and DL packets with correlation between them, which is determined by some inherent issues of specific application, e.g. CP/IP protocol, UE and remote server interaction. D. Study Scenario ypical LE macro cell in dense urban area is considered. In order to obtain the comprehensive and practical results, two scenarios with different UE numbers and smartphone usage profiles are defined in ABLE II, where Scenario reflects the situation of most of cities nowadays, representing the relative light traffic scenario, while Scenario represents the forecast of future cell in big cities, which is relative heavy traffic scenario. In LE cell, smartphone subscribers may adopt more than one always-on applications simultaneously, each of them producing its own independent keep-alive messages. herefore we employ several combinations of light background and heavier background traffic to imitating the practical scenario. ABLE II. SUDY SCENARIOS Scenarios UE numbers Smartphone penetration rate % % Application numbers per smartphone Light traffic smartphone Heavier traffic smartphone Light traffic applications Light traffic applications Light traffic application & Heavier traffic application Light trafffic applications & Heavier traffic application % % % % III. SIMULAION RESULS Simulations were run in two scenarios based upon related models described in Section II. We first generate keep-alive messages from all users smartphones in the cell with different application configuration categories, and then simulate the RRC state transition procedure triggered by both UL and DL packets arrivals immediately without enabling DRX. After recording all state transition trace within the simulation time, the corresponding signaling load can be calculated by using parameters given in ABLE I and then finally obtain the performance of metrics defined in ()-(). Main simulation parameters and assumptions have been presented in ABLE III. ABLE III. GENERAL SIMULAION PARAMEERS Parameters Values Simulation time Direction of traffic Simulation resolution Bandwidth Data rate inactivity timer PUCCH Observation for peak value hours UL + DL ms M Uplink: kbps Downlink: kbps.s, s, s, s, s, s. CCE per message messages per RRC state transition message per data packet transmission M PUCCH M PUCCH SR ms CQI ms preambles for total resource For,, : wiondow ms For : wiondow ms

4 Ratio of Signaling to Data (%) Ratio of Signaling to Data (%) A. eep-alive Messages ransmission Efficiency Fig. illustrates the performance of keep-alive messages transmission efficiency in terms of resource consumption ratio of signaling to data traffic in two scenarios for various inactivity timer settings., and indicate E, E and E given in ()-(). ake the results in Fig. (a) when inactivity timer is s as example, E is %, E is % while E is %, indicating that keep-alive messages cause far more signaling traffic than data traffic itself due to RRC Connection setup, and therefore making the keep-alive messages transmission become inefficient, especially in Control-Plane as the ratios of stand out in the picture. It can be observed that when increasing inactivity timer from.s to s, more than half of signaling load has been reduced in Scenario, along with % in Scenario, showing that RRC inactivity timer has more influence in heavy traffic scenario. By taking advantage of traffic characteristics in different scenarios, specific RRC inactivity timer configuration can be used to reducing signaling overhead and improving the network transmission efficiency at the same time. B. Peak resource Consumption Fig. depicts the peak ratios of resource consumption of both signaling traffic and data traffic due to the presence of keep-alive messages to total resource within a sliding observation in two scenarios,,, and indicate peak,, peak,, peak, and peak, given in ()-(). It can be observed that peak ratios decrease with increase of inactivity timer as well. he highest resource consumption appears in as compared to other channels. ake results at s as example, in Scenario the peak resource consumed by keep-alive messages is.% of,.% of,.% of and % of, respectively, along with.% of,.9% of,.% of and.% of, respectively, in Scenario. he results indicate that the resource consumption caused by keep-alive messages in LE network is not as significant as in GSM and HSDPA due to the special resource sharing and RRC protocol design. According to the results in Fig. (b), the peak resource consumption which is 9% resource of still need some focus when planning network capacity. C. Signaling Overhead in PUCCH Fig. provides the signaling overhead of PUCCH in terms of the ratios of resource consumed by SR, CQI report and AC/NAC dedicatedly to total UL resource, defined in (). For AC/NAC, we assume that there is maximum resource consumption by mapping all resources to the same number of UEs to maintaining the robustness of the system, therefore a constant value has been calculated by using (). It can be observed that as the increases of inactivity timer, the signaling load for both SR and CQI report increase either, since the longer IP connected duration is, the more number of UEs stay in RRC_Connected state, and thus the more resource they consumed.. (a) Scenario. (b) Scenario Fig.. Resource consumption ratios of signaling to data. In Fig. (a), resource consumption in PUCCH is dominated by AC/NAC, which is.%. he resource consumed by total usages increases from.% to.% when increasing inactivity timer from.s to s. In heavy traffic scenario, CQI report actually dominates the PUCCH capacity by using long inactivity timer. In Fig. (b), the maximum resource consumption appears when inactivity timer is s, which is.% by SR along with.% by CQI report, and the maximum resource consumed by total usages up to % of total UL resource. It can be observed that the resource consumption of keep-alive messages transmission is acceptable theoretically when assuming %-% of total resource are allocated for PUCCH generally. But when considering the remaining nonsmartphone users and other types of applications in the LE cell, PUCCH capacity will be challenged. IV. CONCLUSION In this paper, we studied the influence of signaling overhead caused by keep-alive messages on LE network based on RRC state transition procedure. he results indicate that the RRC signaling load makes keep-alive messages

5 Peak Ratio of eep-alive Messages to otal Resource (%) Peak Ratio of eep-alive Messages to otal Resource (%) Ratio of signaling to otal Resource(%) Ratio of signaling to otal Resource (%) transmission with low efficiency, especially in control channel. In the case that a shorter inactivity timer is used, 9% resource of is consumed by keep-alive messages transmission in heavy traffic scenario where % of users are smartphone subscribers, in the worst case, when inactivity timer is.s. In the case that a longer inactivity timer is used, the total resource consumption in PUCCH is % of total UL resource when inactivity timer is s, which is acceptable with the assumption that %-% of resource are allocated for PUCCH. But when considering non-smartphone users and other types of applications in the LE cell, PUCCH capacity will be challenged. At the same time, RRC inactivity timer has more influence on signaling overhead in heavy traffic scenario than light traffic scenario. By using long inactivity timer, RRC state transition related signaling load in,, and can be reduced, but in PUCCH, will be increased. he simulation results do not consider the handover related signaling and power consumption in terminals, which will be involved in our future work. SR CQI AC/NA total usage. SR CQI AC/NA total usage (a) Scenario 9. (a) Scenario. (b) Scenario Fig.. Resource consumption of dedicated usage in PUCCH. ACNOWLEDGMEN his work is supported by the National S Major Project (ZX--), the National Natural Science Foundation of China (No. ) and the International S Cooperation Program of China (DFB).. (b) Scenario Fig.. Peak ratio of resource consumption by keep-alive messages (including signaling traffic and data traffic) to total resource. REFERENCES [] Understanding Smartphone Behavior in the Network White Paper, Nokia Siemens Networks Smart Labs, Apr.. [] LE RAN Enhancements for Diverse Data Applications, GPP R. v.., Sept.. [] S. Zhang, Z. Zhao, H. Guan, D. Miao and H. Yang, Statistics of RRC State ransition Caused by the Background raffic in LE networks, in Proc. IEEE WCNC, Apr., in press [] J. Puttonen, E. Virtej, I. eskitalo and E. Malkamäki, On LE Performance rade-off Between Connected and Idle States with Always-on ype Applications, in Proc. IEEE PIMRC, Sept., pp [] H. Haverinen, J. Siren, and P. Eronen, Energy Consumption of Always- On Applications in WCDMA Networks, in Proc. IEEE VC -Spring, Apr., pp [] M. Gupta, S. Jha, A. oc and R. Vannithamby, Energy Impact of Emerging Mobile Internet Applications on LE Networks: Issues and Solutions. IEEE Commun. Mag, vol., no., pp. 9-9, Feb..