Simulation-based Evaluation of ETSI ITS-G5 and Cellular-VCS in a Real-World Road Traffic Scenario

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1 Simulation-based Evaluation of ETSI ITS-G5 and Cellular-VCS in a Real-World Road Traffic Scenario Sebastian Kühlmorgen, Patrick Schmager, Andreas Festag, Gerhard Fettweis Vodafone Chair Mobile Communications Systems, Technische Universität Dresden, Germany {sebastian.kuehlmorgen,patrick.schmager,gerhard.fettweis}@tu-dresden.de Center of Automotive Research on Integrated Safety Systems and Measurement Area, Technische Hochschule Ingolstadt, Germany andreas.festag@thi.de Abstract In recent years, two candidates for vehicular communications have evolved for the support of road safety and traffic efficiency applications. On the one hand, ad-hoc networks exist based on the ITS-G5/802.11p protocol stack, and on the other hand, there are cellular network infrastructures based on an extended LTE stack, which we refer to as Cellular-based Vehicular Communication Systems (Cellular-VCS). The most important extension of the classical LTE stack is a direct link among vehicles which is also called Device-to-Device (D2D) communication. Both approaches meet the requirements on vehicular communications but show technology-inherent mechanisms that result in different performances. ITS-G5 features a small latency at a small network load whereas Cellular-VCS promises a highly reliable packet transmission. One of the main difference of both approaches lies in the channel access which is random-based for ITS-G5 and centrally scheduled for Cellular-VCS. This contribution studies the performance of the two named technologies in a realworld road traffic scenario through comprehensive simulations. The simulation scenario makes use of real road traffic density measurements for modeling the mobility of the vehicles. Mixed network data traffic of periodically and event-based messages is generated through particular generation rules. The results prove that both technologies work stable at moderate road traffic conditions but have significant differences in the quantified communication parameters. I. INTRODUCTION Nowadays, modern vehicles are equipped with hundreds of sensors for recognizing their surrounding environment. The collected information is used to improve road safety, traffic efficiency and driving comfort applications. Wireless communication among vehicles and their infrastructure, also referred to as Vehicle-to-Everything (V2X), extends the awareness range of each road user for both, Line-of-Sight (LOS) and None-Line-of-Sight (NLOS) conditions, which can drastically improve the quality of the overall road safety. Moreover, vehicular communication is a key component for enabling efficient and safe autonomous driving on a large scale. In the last years, two major approaches for V2X communication took shape and will be deployed in the next years. On the one hand, there are ITS-G5/802.11p-based protocols for ad-hoc networks; on the other hand, cellular-based networks exist utilizing the available cellular infrastructure, which we refer to as Cellular-based Vehicular Communication Systems (Cellular-VCS). In Europe, the ITS-G5 protocol is the standard for vehicular ad-hoc communication, which is characterized by its fast but random-based channel access, referred to as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). At the physical layer, this protocol relies on Time-Division Multiplexing (TDM) resulting in a single node transmission at each time demanding the whole channel resources. The ITS-G5 protocol is matured and the standardization process of most parts completed. Different vendors offer standard conform hardware and several field tests in different settings were successfully completed. On the contrary, the deployed cellular technologies in their current expansion stage are not suitable for all V2X applications because requirements on latency and coverage are not fulfilled yet on a large scale. For example, direct packet transmission and a low signaling overhead are essential components to meet these requirements. However, the cellular approach is promising because the managed and therefore collision-free channel access, based on Frequency- Division Multiplexing (FDM), results in a more reliable packet transmission, which is crucial for safety applications. Many effort has been invested into adopting cellular technologies for V2X communication purposes. Since 3GPP Release 12, many features have been added to the standard aiming to realize V2X, but the standardization process is still ongoing. One proposal for direct communication is Device-to-Device (D2D) communication [1], where vehicles directly send their messages to neighbors and the Base Station (BS) is just responsible for the scheduling. Another important function is broadcast communication, which is not supported by most current cellular network infrastructures. We describe Cellular- VCS as a system which makes use of direct and broadcast communication meeting the requirements on V2X applications. At higher protocol layers, different message types with distinct priorities serve for the various applications and services. Periodically sent Cooperative Awareness Messages (CAMs) or beacons contain information about the vehicle state, such as position, speed and heading, and are distributed to neighboring vehicles. In addition, Decentralized Environmental Notification Messages (DENMs) are generated at safety critical events such as lane changes or braking processes. Due to their safety related content, DENMs have a higher priority than CAMs requiring a specialized scheduler if both message types are considered in a mixed setting. All this information is collected at the facilities layer and provided to different applications for emergency braking or overtaking warning among others. In this paper we study the performance of ITS-G5 and Cellular-VCS within a real-world road traffic scenario, which is calibrated with real road traffic volume measurements. Based on both, CAMs and DENMs, we simulate realistic data traffic and evaluate meaningful communication parameters for an objective technology comparison. Hereby, we simulate the full protocol stacks from the physical up to the facilities layer in

2 Application Entities (APP) Facilities (FAC) Networking & Transport (NET) Access (ACC) Applications CAM DENM BTP GeoNetworking EDCA OFDM ITS-G5 Management & Security LTE protocol architecture C-ITS Application Entities (APP) Facilities (FAC) PDCP RLC MAC PHY Applications CAM DENM Ciphering Segmentation MAC Multiplexing Coding & Modulation Resource Mapping Payload selection Priority Handling Modulation Scheme Resource Assignment Scheduler (Base Station) Fig. 1. C-ITS protocol stack with ETSI ITS-G5 (ETSI EN ). BTP means basic transport protocol. order to evaluate the performance under this mixed data traffic. The coupled road and data traffic simulation on this detailed level provides helpful insights into the strengths and drawbacks of both technologies. The remainder of this paper is as follows: Sec. II evaluates related publications. Sec. III provides an overview of the considered communication protocols and Sec. IV presents the simulation scenario and the evaluation method. In Sec. V we present results for our performance evaluation before we conclude the paper in Sec. VI. II. RELATED WORK Many investigations based on simulations have been performed aiming to study the performance of different ITS-G5/ p-based and cellular-based V2X protocols [2], [3], [4] in specific settings. The relevant publications divide into two parts: (i) comparative studies are conducted highlighting the advantages and disadvantages of both technologies, (ii) the inter-working of both approaches is analyzed. A comprehensive and comparative study is presented in [2], which utilizes network and road traffic simulations. The full communication stacks for p-based and classical LTEbased (Long Term Evolution) message exchange are simulated. However, no direct communication mode (D2D) is considered for the cellular case and no detailed assumptions about the road traffic are made. Investigations in [4] show an equal performance of both technologies for short distances (under 200 m) and a better performance for the cellular approach in case of longer distances. These results rely on link level simulations within a simplified highway road traffic scenario. In [3] and [5], detailed analytical models of the corresponding technologies are derived and used for simulations on the link level while higher communication layers are abstracted. Quantitative measurements are obtained through the simulation of a low-complex highway scenario for the road traffic. An application specific comparison is analyzed in [6], examining a highway platooning scenario. In this study, the road traffic data is also abstracted and no detailed information about the data traffic simulation is provided because the focus lies on the platooning performance. Similarly, the simulations in [7] rest on simplified road traffic scenarios and a detailed description of the data traffic simulation is neglected. Some Fig. 2. Cellular-VCS protocol with LTE protocol architecture based on [9]. The scheduler is only located in the BS, which is the controlling entity. C-ITS refers to Cooperative Intelligent Transport Systems. studies investigate the inter-working of ITS-G5 and cellularbased technologies through indirect comparisons while trying to combine the advantages, e.g. in [8]. However, only a conceptual view is provided and no quantitative analysis is conducted. This paper extends previous work by considering important key components in one simulation scenario. This means that not only mixed data traffic of CAMs and DENMs is considered but also a real-world road traffic scenario which models the mobility of the network nodes in detail. Additionally, the D2D capabilities of Cellular-VCS within a full protocol stack are evaluated and compared to ITS-G5. III. SYSTEM SPECIFICATION In this section, a brief description of the evaluated vehicular communication protocols is provided focusing on mechanisms which are important for V2X applications. The main characteristics are summarized in TABLE I. A. ETSI ITS-G5 The decentralized communication standard ETSI ITS-G5 1 is based on IEEE p which relies on CSMA/CA for a random-based medium access. On top of ITS-G5 is Decentralized Congestion Control (DCC), Geo-Networking with Contention-Based Forwarding (CBF) and the facilities layer, including CAMs and DENMs. ITS-G5 enables Quality of Service (QoS) through Enhanced Distributed Carrier Access Medium access Direct Link (V2V) Broadcast TABLE I. ITS-G5 Random based (CSMA/CA) Natively supported Natively supported TECHNOLOGY COMPARISON Cellular-VCS Centrally scheduled (UL/DL/SL) D2D mode (SL) Centrally scheduled (DL, SL) Signaling Low (OCB mode) High (control information) QoS Distributed control (EDCA), no guarantee 1 ITS-G5 is introduced in ETSI EN Guarantee

3 stem Architecture D2D SL(DENM) Cellular UL (CAM) Control Cellular DL (CAM) Core Base Station Network Base Station 2 S 3 1 Fig. 3. System architecture for Cellular-VCS based on CAMs (BS broadcast) and DENMs (D2D broadcast). The base station controls the communication. CAMs are processed on a server in the core network. (EDCA), which works with different queues of different priorities. However, Sebastian due tokühlmorgen the random-based channel access, QoS cannot be guaranteed. In order to achieve low delays for vehicular applications, the signaling of the protocol is reduced by a disabled authentication and association procedure, which is called Outside the Context of a BSS (OCB). A detailed description of all these features is conducted in [10]. Fig. 1 depicts the corresponding protocol stack. B. Cellular-VCS The considered cellular-based protocol for vehicle communication is based on a classical LTE protocol stack including several extensions for V2X specific communications. The centralized communication architecture provides a managed channel access which avoids packet collisions completely. Specific for the protocol are the Radio Link Control (RLC) and Packet Data Convergence Protocol (PDCP) sub-layers, which manage the signaling procedure including channel access, retransmissions or hand-overs. The basic signaling procedure relies on the exchange of Buffer Status Report (BSR), Uplink Channel Information (UCI) and Downlink Channel Information (DCI). The BSR, transmitted over a transport channel, is part of the MAC control element and used for requesting resources from the BS depending on the buffer state. If resources are available, DCIs and UCIs will be exchanged over logical channels (PDCCH/PUCCH) containing all necessary information for scheduling the proper resource allocation. Compared to classical LTE, we used the extended protocol aiming to achieve broadcast and direct transmission functionalities (D2D) which are crucial for efficient V2X applications. Indirect broadcast messages are transmitted via the cellular Uplink (UL) / Downlink (DL) interface (Uu) whereby direct broadcast D2D messages use the Sidelink (SL) interface (PC5). The broadcast mode is centrally scheduled while all registered nodes in a cell are informed via an unique DCI enabling them to receive the subsequent broadcast message. According to the standard, the protocol uses an adaptive Modulation and Coding Scheme (MCS) depending on the channel conditions, but for the broadcast we fixed the MCS because many nodes with different individual channels will receive the corresponding packet. Therefore, the MCS has to be chosen as a tradeoff between communication range and data rate. CAMs are broadcasted via Uu interface in our scenario, which means they Fig. 4. Map detail of the simulated road traffic scenario near Dresden International Airport with a BS at the central junction. Slide 1 use the channel twice (UL/DL). The motivation is that different CAMs can be simultaneously transmitted to the BS and are aggregated before they are distributed in the cell, reducing the DL resources. Additionally, the use of the Uu interface for CAMs avoids the half-duplex constraint, compared to the D2D mode, allowing a higher packet throughput. Our implementation of the centrally managed D2D mode, which means that the BS schedules the available resources, is inspired by the description in [11]. According to the specification in [4], our D2D mode corresponds to LTE-V, also named LTE-V2X or cellular V2X, Mode 3 based on a dynamic scheduling instead of a semi-persistent scheduling. In the dynamically scheduled mode, nodes request resources from the BS at each packet transmission whereas in the semipersistent scheduled mode nodes reserve the same resources for subsequent, periodic transmissions, resulting in a smaller signaling overhead. The D2D mode requires a modified physical layer with SL functionality which uses the UL resources for receiving direct packets. The medium access layer must be able to schedule both, cellular and direct packets. Direct packets are recognized by a special flag within the corresponding DCI. We extend the BSR with information of the number of CAMs and DENMs in the vehicle buffer so that the scheduler in the BS can distinguish between cellular (UL/DL) and D2D (SL) mode. D2D packets are broadcasted to neighboring nodes and are used to distribute DENMs in our scenario. DENMs are exchanged via the sidelink, which shows a much smaller delay, beneficial for safety-critical DENMs. A schematic system architecture is provided in Fig. 3, which highlights the connection between cellular broadcast and CAMs as well as D2D broadcast and DENMs. The scheduler works with a simple Round Robin mechanism, where CAMs (BS broadcast) and DENMs (D2D broadcast) are alternated. In future work, the scheduler should be extended to cope with different priorities of packets. An overview of the implemented protocol stack is illustrated in Fig. 2. Since our simulation scenario is covered by only one BS, the influence of spatial resource usage, inter-cell interference among neighboring cells and resource re-allocation during hand-overs are neglected in our model. In addition, in-band emissions do not occur in our scenario because the D2D mode is centrally scheduled.

4 IV. Parameter TABLE II. SIMULATION PARAMETERS Value General Simulation duration 500 s Penetration Rate 10 to 100 % Packet size (with header) CAM : 535 bytes, DENM: 585 bytes Path loss model Log-distance: channel exp. α = 2.1 CAM generation CAM generation rules (ETSI EN ) DENM generation Poisson: event rate λ = 1/50 s 1 ITS-G5 specific Transmit power 23 dbm Channel bandwidth 10 MHz at 5.9 GHz Fading model Nakagami-m: m = 1 Modulation & coding QPSK, code-rate 0.5 Cellular-VCS specific Transmit power Channel bandwidth Fading model Modulation & coding BS: 46 dbm vehicle: 23 dbm 5 MHz at UL: 1.93 GHz DL: 2.12 GHz Multi-path Rayleigh fading: Vehicular A model 16-QAM, code-rate (MCS=12) EVALUATION SCENARIO, SIMULATION PARAMETERS AND METRICS We conducted our performance simulation with the network simulator ns-3 2 (version: ns-3.26) and the road traffic simulator SUMO 3 (version: ). The ns-3 module LTE- LENA is utilized and extended, and modules for D2D communication and facilities layer were created in order to model the protocol stacks as described in Sec. III. We created an additional ns-3 module which implements a bidirectional coupling between both simulators with help of the TraCI API provided by SUMO. The coupled simulation realizes a dynamic mapping between each SUMO vehicle and a corresponding ns-3 node. Consequently, the positions of the network nodes are periodically updated by the information retrieved from the road traffic simulation. The other way around, the network simulator is able to influence SUMO, which can be used to, e.g., influence the speed of vehicles. The described module for the bidirectional coupling is available online 4. A. Scenario Our road traffic scenario is located in a rural area near Dresden International Airport and is based on real measurements of the corresponding traffic densities allowing to model realistic road traffic data. These measurements were carried out weekdays between 8:00 and 10:00 am sensing a typical traffic volume. The scenario comprises more than 3 km of a four-lane per direction freeway, about 6 km of a two-lane per direction highway as well as several junctions and freeway entrance ramps. The number of vehicles within the scenario varies between 150 and 250, the vehicle velocity between 0 and 47 m/s with an average of 17 m/s. Smaller congestion emerge around the central junction. A map view of the described scenario is provided in Fig. 4. One BS is located in the scenario positioned at the center point of the largely illustrated communication range near the central junction, enabling the cellular communication. The radius of the illustrated ranges do not correspond to successful transmissions but only serve for visualization purposes. 2 URL: 3 Simulation of Urban MObility. URL: 4 URL: We model realistic, mixed data traffic for vehicular communication based on CAMs and DENMs. For comparability, we only consider single-hop messages in our scenario. CAMs are generated periodically depending on the vehicle movement according to the CAM generation rules 5. These messages contain information about vehicle position, speed and heading among others. Contrary, DENMs are event-driven messages triggered at safety critical events such as lane changes. We model this behavior by means of a Poisson distribution in order to generate such events. Each triggered event results in a repetitive transmission of 10 messages with an interval of 100 ms. For both message types, a circular geographical area with a radius of 400 m is assumed within the messages are distributed. Because DENMs are more important for safety critical applications they have a higher scheduling priority in the ITS-G5 protocol. In Cellular-VCS, the CAMs are sent over the Uu interface (UL/DL) and the DENMs over the direct PC5 interface (SL). The scheduling follows a simple Round Robin scheme, which means that CAMs and DENMs are transmitted alternately. All simulation specific parameters are summarized in TA- BLE II. We have chosen a frequency independent log-distance channel model which is suitable for this rural scenario with LOS conditions. Therefore, it has no influence that ITS-G5 is modeled at a carrier frequency of 5.9 GHz and Cellular-VCS at 2 GHz. Besides, different fading models as well as MCSs are used as a result of limitations in the corresponding ns-3 modules, which only causes small statistical differences in the resulting channel models. For comparable packet statistics, we only consider the data traffic within a 1 km radius around the BS. Our modeled channel shows a Packet Success Ratio (PSR) of 90 % at a distance of 1700 m for the channel exponent 2.1 with a transmit power 23 dbm. The PSR represents the empirical probability of successful packet reception between two nodes and can be used to quantify channel conditions. B. Metrics End-to-End Delay (t E2ED ) is defined as the average time of a received packet within the geographical area and is given as t E2ED = 1 N Rx (t Rx,i t Tx ), N Rx i=1 where t Tx is the generation time, t Rx,i vehicle i s reception time at the facilities layer and N Rx the overall vehicle number which received the packet. Packet Inter-Reception Time (t IRT ) is the averaged time difference between two subsequent CAM receptions regarding a pair of vehicles. Therefore, the IRT at a receiving node is defined as t IRT = 1 K 1 (t k t k 1 ). K 1 k=1 K means the number of received CAMs and t k the time of reception. This metric is infeasible for event-generated DENMs. 5 See ETSI EN , clause

5 1 0.8 Cellular-VCS UL & SL Cellular-VCS DL ITS-G Cellular-VCS CAM Cellular-VCS DENM ITS-G5 CAM ITS-G5 DENM CBR Fig Penetration Rate (%) Channel busy ratio (CBR) over vehicle penetration rate. Node Miss Ratio (NMR) describes the ratio between vehicles that have not received a certain packet and all vehicles within a geographical area: NMR = # of non-receiving vehicles in geo-area. # of all vehicles in geo-area Channel Busy Ratio (CBR) measures the resource utilization of the channel as ratio between used resources to a certain measurement interval. For ITS-G5, each node measures the corresponding time where the channel is sensed busy t busy in an interval t int, CBR ITS-G5 = 1 L L l=1 t busy,l t int, with L as the number of measurements (intervals) within the simulation duration. In case of Cellular-VCS, we count the allocated sub-frames per frame for determining the CBR. V. PERFORMANCE EVALUATION This section provides a summary of the conducted evaluations. In each case, the global parameters for the specified scenario are considered represented by their average and standard deviation. Channel Busy Ratio The CBR is used for quantifying the utilization of the channel resources, which increases linearly with the penetration rate. For Cellular-VCS, the resources for UL and DL are approximately the same because in our LOS scenario all mobile nodes are within the communication range of the BS. The UL & SL resources (SL uses UL resources) show a slightly higher utilization because these are used for CAM and DENM transmissions, where the number of sent CAMs is 24 times higher than the number of DENMs. The transmitted packets are scheduled to sub-frames with a length of 1 ms, called Transmission Time Intervals (TTIs), whereas 10 sub-frames form a frame. In this case, the CBR shows the fraction of used sub-frames per frame. According to the standard, large packets are separated and filled to different sub-frames achieving a dense packaging and optimal channel utilization. In our case, the packets are smaller than the sub-frames that causes subframes which are not filled entirely, resulting in a sub-optimal NMR Fig Penetration Rate (%) Node miss ratio (NMR) over vehicle penetration rate. channel utilization and higher CBR. However, this depends on the amount of data traffic and is an implementation-based drawback, which will be solved in the future. In Cellular- VCS, a CAM broadcast causes two packet transmissions, one in the UL and one in the DL. A DENM causes just one transmission in the SL. ITS-G5 is not restricted to sub-frames and a transmission takes only 0.76 ms in average. Additionally, simultaneous transmissions may occur due to the decentralized channel access, which could cause collisions and decrease the CBR further. Node Miss Ratio Fig. 6 reveals a smaller NMR for Cellular-VCS compared to ITS-G5, because the centralized scheduling avoids packet collisions. The generally small NMR is explained by the used LOS channel model. In Cellular-VCS, both message types show a nearly similar performance which is quite constant over the penetration rate. On the contrary, ITS-G5 shows an increasing dependence over the penetration rate, which can be traced back to increased packet collisions. In addition, for ITS-G5, DENMs have a slightly smaller NMR due to their higher priority during the scheduling procedure. End-to-End Delay and Packet Inter-Reception Time ITS-G5 shows a superior performance for t E2ED compared to Cellular-VCS. The ITS-G5 protocol is designed towards a minimum on signaling overhead, for example by eliminating the authentication procedure, which reduces t E2ED drastically. Nevertheless, an exponential increase (linear in log-scale) of t E2ED over the penetration rate can be observed resulting from the shared channel access with possible collisions. The slope for CAMs is higher because more messages of this type are generated while simultaneously having a smaller scheduling priority. On the opposite, Cellular-VCS is characterized by a constant curve shape for both broadcast and D2D mode which demonstrates its independence from the traffic load as long as free resources are available. The offset between both is exactly 32 ms which can be traced back to protocol specific processing steps between BS and server in the core network [12], which do not occur in the D2D mode. This is one advantage for D2D because it is independent of the core network delay. The lower bound for the delay in D2D mode is 12 ms due to the protocol specific signaling. In particular, it takes a node up to 5 ms until a successful request for resource allocation is transmitted to the BS, further 2 ms for the BS scheduling and respond and

6 te2ed (ms) tirt (ms) Cellular-VCS CAM Cellular-VCS DENM ITS-G5 CAM ITS-G5 DENM Cellular-VCS ITS-G5 CAM Penetration Rate (%) Fig. 7. End-to-End delay and Packet Inter-Reception Time over vehicle penetration rate. finally another 5 ms for the node to transmit the corresponding D2D packet. t IRT was only measured for periodic CAMs because DENMs are event-driven, which is infeasible for t IRT. For Cellular-VCS the parameter t IRT is quite constant because the centrally managed channel access ensures a reliable packet transmission. This is not the case for ITS-G5 where the packet inter-reception time increases linearly with the penetration rate. Again, the CSMA/CA procedure cannot avoid collisions, which increases t IRT with the traffic load. In our simulations, we have used the speed dependent CAM generation rules, which explains the high standard deviation in both cases because the speed within the scenario varies drastically. Evaluation Summary The evaluated communication parameters reveal that there are clear performance differences between the analyzed V2X technologies, determining their application. ITS-G5 is featured by small delays down to 1 ms resulting from the minimum on signaling, whereas the lower limit for Cellular-VCS is 12 ms in the D2D mode. Cellular-VCS shows a higher reliability below a NMR of 10 3 because of the centralized scheduling and the collision-free communication. In ITS-G5, collisions may occur especially under higher network load, which results in a NMR up to 0.3. This finding is also confirmed by the smaller t IRT of Cellular-VCS under growing network traffic. The dependence of most communication parameters from the amount of data traffic is an adverse effect for ITS-G5, which cannot be observed for Cellular-VCS under the considered real-world road traffic. ITS-G5 shows a smaller CBR because a CAM does not cause two packet transmissions as it is the case for Cellular-VCS. VI. CONCLUSION Our simulations show the performance of ITS-G5 and Cellular-VCS within a real-world road traffic scenario. The measured global communication parameters prove that both technologies work stable in such a setting with realistic data traffic load. The evaluated quantities reveal clear differences in the performance of the two approaches. Considering the node miss ratio and inter-reception time, we can conclude that Cellular-VCS shows a better behavior for reliable packet transmissions. In contrast, CAMs sent via Cellular-VCS require two packet transmissions resulting in a doubled channel load compared to ITS-G5, which could be mitigated with the possibility by stacking of CAMs. Additionally, with ITS-G5 a much smaller delay can be achieved because of its minimum on signaling. Currently, there exist different efforts in optimizing LTE for V2X applications to achieve smaller delays 6. 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