Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Transcription

1 1 Heteogeneous V2V Communications in Multi-Link and Multi-RAT Vehicula Netwoks Miguel Sepulce and Javie Gozalvez Abstact Connected and automated vehicles will enable advanced taffic safety and efficiency applications thanks to the dynamic exchange of infomation between vehicles, and between vehicles and infastuctue nodes. Connected vehicles will utilize IEEE p fo vehicle-to-vehicle (V2V) and vehicle-to-infastuctue (V2I) communications. Howeve, a widespead deployment of connected vehicles and the intoduction of connected automated diving applications will notably incease the bandwidth and scalability equiements of the vehicula netwok. This pape poposes to addess these challenges though the adoption of heteogeneous V2V communications in multi-link and multi-rat vehicula netwoks. In paticula, the pape poposes the fist distibuted (and decentalized) context-awae heteogeneous V2V communications algoithm that is technology and application agnostic, and that allows each vehicle to autonomously and dynamically select its communications technology taking into account its application equiements and the context conditions. This study demonstates the potential of heteogeneous V2V communications, and the capability of the poposed algoithm to satisfy the vehicles application equiements while appoaching the estimated uppe bound netwok capacity. Index Tems Connected vehicles; connected automated vehicles; coopeative ITS; V2V; vehicle-to-vehicle; heteogeneous communications; heteogeneous V2V; multi-rat; multi-link; multi-channel; multi-band; VANET; vehicula netwoks 1 INTRODUCTION C onnected vehicles will impove taffic safety and efficiency thanks to the wieless exchange of infomation between vehicles (Vehicle-to-Vehicle o V2V communications), and between vehicles and infastuctue nodes (Vehicle-to-Infastuctue o V2I communications). Coopeative active safety applications (e.g. emegency electonic bake lights, intesection collision avoidance o lane change waning) geneally equie the peiodic tansmission and eception of beacons that include basic positioning and status infomation; these beacons ae known as CAMs (Coopeative Awaeness Messages) in Euope and BSMs (Basic Safety Messages) in the US. These messages can be tansmitted using IEEE p, also known as ITS-G5 in Euope and DSRC in the US and Japan [1]. The intoduction of connected automated vehicles will incease the eliability, latency and bandwidth equiements of vehicula communications [2]. Connected automated vehicles will benefit fom the implementation of coopeative diving maneuves whee neaby vehicles exchange infomation to safely coodinate diving maneuves such as enteing a oundabout/highway o changing lanes. This exchange equies vey eliable and low latency V2V communications. Also, the exchange of ich senso data between vehicles can impove thei capacity to collaboatively detect, estimate and chaacteize the local suounding o envionment (efeed to as coopeative peception o sensing). Exchanging this infomation can equie lage communication bandwidths. A connected vehicle tansmitting CAMs/BSMs (~200Bytes) Miguel Sepulce and Javie Gozalvez ae with Univesidad Miguel Henandez de Elche (UMH), Avda. Univesidad s/n, 03202, Elche (Alicante), Spain. msepulce@umh.es, j.gozalvez@umh.es. at 10Hz equies a communications link of ~16Kbps. Howeve, the thoughput equied by connected automated vehicles can be in the ode of Mbps [3], which esults in moe stingent equiements in tems of channel load and bandwidth. An appoach to suppot connected automated vehicles and its highe communication equiements is the development of heteogeneous V2X communications and netwoks [4]. Heteogeneous wieless netwoking has been utilized in cellula netwoks to incease the communication bandwidth and impove the netwoks scalability [5]. Coopeative ITS standads fo V2X (Vehicle-to- Eveything) communications allow fo the implementation of heteogeneous vehicula communications. Fo example, the ITS station efeence achitectue standadized by ISO [6] consides the possibility to use diffeent Radio Access Technologies (RATs) at the physical and MAC layes. This achitectue has been adapted to the Euopean context by ETSI [7]. ETSI cuently uns two study items to investigate futhe enhancements to this achitectue in ode to suppot communications between vehicles with multiple RATs [8][9]. The active study items ae cuently analyzing diffeent implementation and deployment options including a multi-link and multi- RAT scenaio whee all vehicles can simultaneously eceive messages using diffeent RATs. In this scenaio, vehicles can dynamically select the technology they use to tansmit. 3GPP also consides the use of multiple RATs to suppot the 5G ev2x applications (including autonomous diving) and equiements identified in Release 15 [3]. 5G- PPP also highlights V2X multi-link and multi-rat connectivity [2] as a pomising appoach to suppot the stingent equiements of futue automotive use cases. Multi-link is defined as the capability of a device to com-

2 2 municate via multiple wieless links. Standads have defined the main components needed fo the implementation of heteogeneous V2X communications [10], but do not define specific heteogeneous V2X algoithms. To date, heteogeneous vehicula netwoking has been mainly applied to V2I communications (e.g. [11], [12]) since most cuent bandwidth-demanding applications ae Intenet-based and equie the connection to the infastuctue. Howeve, V2V communications will also be challenged (both in tems of eliability and bandwidth) unde dense deployment scenaios, and with the intoduction of connected automated vehicles that will have highe bandwidth demands. In this context, this pape poposes to exploit heteogeneous V2V communications to suppot connected and automated vehicles. To this aim, the pape pesents CARHet (Context-AwaRe Heteogeneous V2V communications), the fist decentalized heteogeneous V2V communications algoithm fo multilink and multi-rat vehicula netwoks that is technology and application agnostic. CARHet allows each vehicle to dynamically select its adio access technology taking into account its context and application equiements. The conducted evaluation demonstates the potential of heteogeneous V2V communications, and the capacity of the poposed algoithm to satisfy the application equiements while appoaching the estimated uppe bound vehicula netwok capacity with a low computational cost. 2 STATE OF THE ART Heteogeneous netwoking has been lagely investigated in the context of cellula systems. In cellula systems, the coe netwok selects the most suitable communications technology o RAT fo each device. The selection usually takes into account context infomation available at the coe netwok and obtained fom the devices. Seveal studies have demonstated the significant gains that heteogeneous netwoking can povide, fo example, highe bit ates, netwok capacity and availability [13]. Seveal studies have highlighted the benefits of applying heteogeneous netwoking to V2I communications [14], and fist algoithms to select the most adequate V2I communications technology at each point in time have been poposed in the liteatue. Fo example, [11] poposes a method to select the communications technology (WiFi o LTE in thei study) that maximizes QoE (Quality of Expeience) duing a vehicle s oute. The method takes into account the sevice type, the vehicle s oute, and the taffic dynamics ove the backhaul links of each technology. The selection algoithm poposed in [12] takes into account use pefeences, and selects the technology that bette fulfils the application equiements. The algoithm exploits location and navigation infomation in ode to minimize the numbe of handoves between technologies duing the vehicle s oute. The algoithm pesented in [15] focuses on the intewoking of cellula and WiFi netwoks. The study concludes that it is possible to minimize the tansmission time if vehicles switch fom cellula to WiFi when appoaching WiFi access points, but only when vehicles move at low speeds. The authos pesented in [16] a netwok-assisted heteogeneous V2I algoithm designed to impove both the individual and system pefomance. The selection pocess takes into account context infomation such as the position of base stations and oad-side units, the vehicle s oute, the tavel time and taffic density. Limited wok has been done to date to apply heteogeneous netwoking to V2V communications. This is patly due to the fact that IEEE p has geneally been consideed as the de-facto technology fo V2V communications. Howeve, the limitations of IEEE p and the emegence of othe device-to-device technologies (e.g. LTE-V [17], WiFi-Diect [18], TV White Space [19] o even Visible Light Communications [20]) paves the way fo applying heteogeneous netwoking to V2V communications in ode to impove the eliability, bandwidth and scalability of vehicula netwoks. It is impotant noting that the algoithms and conclusions deived fom heteogeneous V2I studies cannot diectly be applied to heteogeneous V2V communications. This is the case because the applications and communication equiements ae diffeent, and also because many of the assumptions made fo V2I scenaios ae not valid, e.g. the static position of the taget communicating nodes. Fist studies consideing the use of diffeent RATs fo V2V communications have poposed the use of cellula technologies as a backup when IEEE p-based V2V multi-hop connections cannot be established [21]. Fo example, [22] suggests using cellula D2D (Device-to- Device) communications as a failove ecovey solution in multi-hop V2V connections. Conventional infastuctuebased cellula communications have also been poposed to impove the V2V connectivity in the case of low IEEE p penetation ates. Fo example, [23] poposes an application laye handoff that simultaneously tansmits event-diven messages though IEEE p (V2V) and LTE (V2I-I2V) in ode to disseminate safety-citical collision waning messages to neaby vehicles. Similaly, [24] poposes a hybid achitectue fo safety message dissemination that oganizes the IEEE p netwok in clustes using V2V communications. Cluste heads opeate using dual adio intefaces in ode to connect the IEEE p sub-netwoks to the LTE netwok. [25] is one of the fist studies that has poposed using diffeent V2V communication technologies fo connected automated vehicula applications, in paticula to manage platoons. The study poposes that only platoon leades should use IEEE p while following vehicles in a platoon should communicate using Visible Light Communications. The objective is to impove the eliability and scalability of vehicula netwoks by educing the use of IEEE p. Existing studies have povided fist insights into the potential of applying heteogeneous netwoking concepts to V2V communications. These studies have poposed policies to decide when each communication technology should be utilized. This pape complements the existing state of the at by pesenting what is, to the authos knowledge, the fist heteogeneous V2V communications algoithm fo multi-link and multi-rat vehicula net-

3 SEPULCRE ET AL.: HETEROGENEOUS V2V COMMUNICATIONS IN MULTI-LINK AND MULTI-RAT VEHICULAR NETWORKS 3 woks that is technology and application agnostic. The poposed algoithm allows each vehicle to autonomously and dynamically select its V2V communications technology (with a low computational cost) based on its application equiements and context conditions. 3 HETEROGENEOUS V2V COMMUNICATIONS: FRAMEWORK AND MOTIVATION This study poposes the use of heteogeneous V2V communications in ode to help addess the bandwidth demands of futue connected automated vehicles, and suppot the implementation of coopeative peception and diving applications. To this aim, we popose a distibuted heteogeneous V2V communications algoithm that allows each vehicle to dynamically select the RAT that is moe suitable at each point in time. This study consides a multi-link and multi-rat vehicula scenaio whee all vehicles ae equipped with diffeent RATs opeating in diffeent bands 1. In line with 5G-PPP [2], 3GPP [3] and ETSI [9], we conside that vehicles ae able to simultaneously use diffeent RATs fo data tansmission and/o eception. A vehicle can then tansmit data using one RAT and simultaneously eceive infomation though all available RATs as illustated in Fig. 1. Management CARHet Application equiements Context infomation Context infomation Context infomation RAT selection Fig. 1. Achitectue and concept illustation of heteogeneous V2V communications. DSRC is used fo data tansmission and eception, while all othe RATs ae used fo eceiving data only. This section illustates the capacity of heteogeneous V2V communications to incease the communication bandwidth and theefoe the netwok capacity. To this aim, the section assumes that all vehicles have the same bandwidth demand, and compaes the uppe-bound of the taffic density that could be suppoted when using a single RAT pe vehicle, and when implementing heteogeneous V2V communications at each vehicle. The maximum taffic density can be estimated as a function of the channel load. The channel load is typically measued using the CBR (Channel Busy Ratio) metic, which epesents the pecentage of time that the channel is sensed as busy. The CBR expeienced at a given position x when using a adio access technology can be estimated as the summation of the load contibution of all neighboing vehicles that also tansmit using technology : i i CBR ( x) n t PSR ( x x) (1) 1 Each RAT utilizes a single and pe-defined channel. i Applications Facilities Tanspot & Netwok Rx Rx & Tx Rx Rx Rx WiFi DSRC TVWS VLC LTE-V i whee x i epesents the position of vehicle i, n i the numbe of packets vehicle i tansmits pe second, and t i the time duation of each tansmitted packet. PSR (Packet Sensing Ratio) is a distance-dependent function that epesents the pobability that a packet is sensed at a given distance to the tansmitte. The PSR function depends on diffeent factos such as the tansmission powe, the adio popagation conditions and the caie sense theshold. Without loss of geneality, if we conside that all vehicles ae unifomly distibuted (with an inte-vehicle distance of d), and they all tansmit the same numbe of packets pe second (n pkt = n i ) with the same duation (t pkt = t i ), equation (1) can be tansfomed fo x=0 into: i CBR n t PSR ( i) pkt pkt whee β=1/d epesents the vehicle density and is expessed in vehicles pe mete if the distance between vehicles (d) is expessed in metes. The same esults would be obtained fo othe values of x. If we conside that the maximum CBR that can be expeienced with a given adio access technology is max CBR, then the maximum taffic density that could be suppoted by this technology is: max n pkt t CBR max pkt i PSR ( i) The maximum taffic density that can be suppoted inceases with the numbe of RATs available at each vehicle to tansmit data. Let s conside that each vehicle has N RAT adio access technologies. If data tansmissions ae adequately distibuted ove the diffeent RATs, the maximum taffic density that can be suppoted when implementing heteogeneous V2V communications can be appoximated by: max N RAT 1 max Without loss of geneality, this wok consides that each vehicle is equipped with 5 RATs: DSRC (IEEE p) opeating at 5.9GHz, DSRC (IEEE p) opeating at 700MHz, WiFi opeating at 5.6GHz, WiFi opeating at 2.4GHz, and an OFDM-like technology opeating in the TVWS band at 460MHz. Table I epots the main communication paametes fo each RAT. These paametes ae fixed in this study since ou objective is not to optimize the opeation of each RAT, but instead illustate the potential of heteogeneous V2V communications. The minimum signal level needed to coectly eceive a packet (i.e. the eception theshold) has been set 3dB highe than the noise powe fo all RATs. The tansmission powe levels have been configued to the maximum values fo each RAT. The popagation conditions ae modeled using the Winne+ B1 popagation model ecommended by METIS fo D2D/V2V [26]. This model is valid fo the fequency ange GHz. Winne+ B1 includes a log-distance pathloss model fo the aveage popagation loss as a function of the distance between tansmitte and eceive. A log-nomal andom vaiable is used to model the shadowing effect caused by suounding obstacles. The model diffeentiates between LOS (Line-of- (2) (3) (4)

4 4 Sight) and Non-LOS conditions. Using the Winne+ B1 model and the paametes in Table I, we have deived the PSR cuves fo each RAT that ae needed to estimate the maximum taffic density suppoted by heteogeneous V2V communications. The PSR cuves ae shown in Fig. 2. communications. Achieving such gains equies the design of an heteogeneous V2V communications algoithm that adequately distibutes data tansmissions ove the diffeent RATs. This is exactly the objective of the CARHet poposal that is pesented in the next section. TABLE I. COMMUNICATION PARAMETERS Paamete DSRC DSRC WiFi WiFi 0.7GHz 5.9GHz 2.4GHz 5.6GHz TVWS Caie feq. [GHz] Bandwidth [MHz] Tx. powe [dbm] Noise [dbm] Rx. Theshold [dbm] Data ate [Mbps] max [veh/km] (a) QPSK 1/2 (b) Highest MCS Fig. 3. Uppe-bound of the maximum numbe of suppoted vehicles/km. max [veh/km] PSR Fig. 2. PSR (Packet Sensing Ratio) fo diffeent RATs. Fig. 3 compaes the maximum taffic density (β max ) that could be suppoted with heteogeneous V2V communications and with each one of the 5 available RATs when utilized individually. Fig. 3 has been obtained using eq. (4) and Fig. 2, and setting the maximum CBR fo all RATs to 0.6 [27]. Fig. 3a depicts the esults consideing the same MCS (Modulation and Coding Scheme) - QPSK ½ - fo all RATs. This MCS coesponds to a data ate of 6Mbps fo IEEE p at 5.9GHz, which is the default MCS poposed by the IEEE standad. Fig. 3b plots the esults using the highest MCS fo each RAT (i.e. the data ates shown in Table I); inceasing the data ate augments the maximum taffic density. Independently of the MCS utilized, Fig. 3 clealy illustates the capacity gains that can be obtained with heteogeneous V2V communications. In all the scenaios consideed, using heteogeneous V2V communications could incease the capacity by appoximately 8x compaed to when using only IEEE p at 5.9GHz. Fo example, in a scenaio with all vehicles tansmitting 0.5Mbps and using the highest MCS, IEEE p at 5.9GHz (DSRC59) could suppot only 35 vehicles/km. This numbe can incease to up to 280 vehicles/km when using heteogeneous V2V communications. It is also inteesting to note that the same gain is achieved when consideing connected vehicles tansmitting CAMs/BSMs (i.e. aound 200Bytes at 10Hz o 16Kbps). In this case, the estimated maximum taffic density suppoted by DSRC59 would be 265 vehicles/km. This value could incease to moe than 2200 vehicles/km (with QPSK ½) with heteogeneous V2V communications. These esults illustate the capacity gains that can be achieved with heteogeneous V2V 4 HETEROGENEOUS V2V PROPOSAL An heteogeneous V2V communications algoithm should be able to dynamically select fo each vehicle the most adequate RAT in ode to satisfy its application equiements and maximize the netwok capacity. Finding the optimum solution to this selection poblem can be a challenging task given the lage numbe of possible solutions and the stict latency equiements that geneally chaacteize V2V applications. A scenaio with v vehicles and i RATs pe vehicle has i v possible solutions. Even when consideing a medium to low density of vehicles (e.g. v=20), the numbe of possible solutions (~ ) is quite significant with only 3 possible RATs. The poposed heteogeneous V2V communications algoithm (CARHet) educes this computational cost by taking decisions locally at each vehicle. Each vehicle seeks its local optimal solution taking into account the decisions peviously taken by its neighbo vehicles, and the impact that its decision could have on its neighbo vehicles. In paticula, each vehicle dynamically selects fo data tansmission the RAT that satisfies its application equiements with the minimum cost. The application geneates R bps that ae tansmitted in 1-hop boadcast packets. The application equies that at least P% of the tansmitted packets ae coectly eceived at distance D (i.e. it equies a thoughput highe o equal than P R at distances lowe o equal than D). Othe equiements could be consideed although the selected ones ae elevant fo coopeative peception sevices that equie vehicles to exchange senso data. The cost is hee measued as the channel load, but othe metics could also be valid. Vehicles implementing CARHet take into account the communications context of neighbo vehicles to select thei RAT. To this aim, vehicles peiodically shae infomation about the status of thei RATs. When a vehicle needs to select a RAT, it estimates the pefomance it could achieve with evey available RAT, the cost (o channel load) it will expeience if selecting such RAT, and also the cost that selecting such RAT could geneate on neighbo vehicles. Fig. 4 depicts the flow chat of CARHet that could be implemented in the tansvesal management laye defined in the ITS sta-

5 SEPULCRE ET AL.: HETEROGENEOUS V2V COMMUNICATIONS IN MULTI-LINK AND MULTI-RAT VEHICULAR NETWORKS 5 tion efeence achitectue (Fig. 1). Its main modules ae next detailed. A eceives the infomation of D though vehicle C. To maintain the table updated, evey time a vehicle eceives a CIS packet, it updates the RT (Reception Time) and the UT (Update Time) paametes in the table. RT epesents the time when the last packet was eceived fom a given vehicle, and UT the last time the infomation was updated fo each 1-hop and 2-hop neighbo. RT is equal to UT fo 1-hop neighbos, and NaN fo 2-hop neighbos since thei context infomation is eceived though othe vehicles. A vehicle is deleted fom the table if its infomation is not updated (diectly o indiectly) duing the last T neigh, i.e. if RT and UT ae highe than T neigh. The infomation that vehicle A would include in its CIS packet is shaded in Table II. In this table, X and Y epesent the latitude and longitude of the vehicles. Modules I and II descibe the context acquisition and shaing pocesses included in CARHet. TABLE II. EXAMPLE OF CONTEXT TABLE Vehicle RT UT Position CBR RAT1 RAT2 RAT3 A s XA, YA 32% 5% 6% B (1-hop) 5.36s 5.36s XB, YB 20% 56% 36% C (1-hop) 5.27s 5.27s XC, YC 37% 45% 35% D (2-hops) NaN 5.27s XD, YD 44% 25% 24% Fig. 4. Flow chat of CARHet. Context acquisition and Context shaing (Modules I and II). With CARHet, vehicles peiodically measue and exchange the channel load they sense on all available RATs. Moe specifically, vehicles estimate the channel load using the CBR and exchange it evey T meas using time t m in Fig. 4. This infomation is boadcasted in a CIS (Context Infomation Shaing) packet using the RAT selected fo data tansmission. The CIS packet also includes the position of the tansmitting vehicle, and the position and channel load measuements of its 1-hop neighbos. The infomation of the 1- hop neighbos is e-boadcasted so that each vehicle takes into account the context of its 2-hop neighbos when selecting its RAT. This is done because the tansmissions of a given vehicle can intefee up to 2 hops, as consideed in e.g. [28]. Using eceived CIS packets, each vehicle ceates its own context table that includes the position and channel load measued by its 1-and 2-hops neighbos. Table II shows an example of a context table built by a given vehicle A in a scenaio with 4 vehicles (A, B, C and D) and 3 RATs (RAT 1, RAT 2, RAT 3 ). This example assumes that vehicles B and C ae 1-hop neighbos of A, and D is a 2-hop neighbo. Vehicle MODULE I. CONTEXT ACQUISITION Input: CIS packet eceived fom a 1-hop vehicle neighbo Output: updated context table Execution: when a CIS packet is eceived 1. Fo each vehicle i whose data is included in the packet do 2. If UT i eceived in the CIS>UT i in the context table then 3. Update UT i in the table 4. Update position of vehicle i in the table 5. Fo each RAT j with 1 j N RAT do 6. Update in the table the load in RAT j fo vehicle i 7. End Fo 8. End if 9. End Fo MODULE II. CONTEXT SHARING Input: context table Output: CIS packet Execution: evey T meas. 1. Fo each 1-hop neighbo i in the table & own vehicle do 2. Add UT of vehicle i to the CIS packet 3. Add position of vehicle i to the CIS packet 4. Fo each RAT j with 1 j N RAT do 5. Add load in RAT j by vehicle i to the CIS packet 6. End Fo 7. End Fo RAT pe-selection (Module III). This pocess is in chage of identifying and pe-selecting the available RATs that can satisfy the application equiements wheneve CARHet is executed. In this study, the application equies that at least P=90% of the tansmitted packets ae coectly eceived at distance D. A RAT is hence consideed to satisfy the application equiements if the PDR (Packet Delivey Ratio) is high-

6 6 e o equal than 0.9 at the distance D. The PDR is influenced by the channel load and intefeence. We have hence deived PDR cuves fo each RAT fo diffeent CBR levels 2. Fig. 5 epesents a PDR example fo DSRC at 5.9GHz and CBR levels vaying between 0 and 0.9. Simila cuves have been deived fo all the implemented RATs. The RAT peselection pocess woks as follows. If a vehicle has to select a RAT, it will measue the CBR expeienced in all available RATs. Fo each RAT and expeienced CBR level, the vehicle deives the PDR at distance D. CARHet then pe-selects those RATs that ae capable to satisfy a PDR equal o highe than 0.9 at distance D. PDR Fig. 5. PDR (Packet Delivey Ratio) fo DSRC at 5.9GHz fo CBR levels vaying between 0 and 0.9. MODULE III. RAT PRE-SELECTION Inputs: D, R and PDR cuves fo the cuent CBR Output: each RAT is pe-selected o not as candidate RAT Execution: evey T update 1. Fo each RAT j with 1 j N RAT do 2. If PDR j (D)>0.9 then 3. Pe-select RAT j as candidate RAT 4. End If 5. End Fo Cost estimation (Module IV). CARHet computes then the cost associated to the use of each pe-selected RAT that is able to satisfy the application equiements. In this study, the cost is measued as the CBR that a RAT would expeience if it is selected by the vehicle that is executing CARHet. In paticula, the cost of using a given RAT j is equal to the maximum CBR that would be expeienced by any 1-hop and 2-hop neighbos. This cost is epesented by c j =max{l ij } with L ij epesenting the CBR that would be expeienced by neighbo i if RAT j is selected. To compute the cost, the vehicle needs to estimate L ij fo each one of its 1-hop and 2-hop neighbos and all RATs as specified in Module IV. L ij can be computed as: L ij LE ij LG whee LE ij is the CBR expeienced by neighbo i with RAT j, and LG ij is the additional CBR the vehicle executing CARHet would geneate to neighbo i if RAT j is selected. LE ij is measued by neighbo i and is included in its CIS packets; the infomation is hence stoed in the context table (Table II). LG ij can be estimated as follows: 2 The PDR cuves ae obtained using the simulato pesented in Section 5. ij (5) LG nt PSR ij j j ( i d ) whee n epesents the numbe of packets geneated pe second, t j the packet duation, d i the distance between the tansmitting vehicle and its neighbo i, and PSR j (d i ) the packet sensing atio at distance d i fo RAT j. MODULE IV. COST ESTIMATION Inputs: D, R, context table and PSR Output: c j Execution: evey T update. 1. Fo each RAT j with 1 j N RAT do 2. If RAT j was pe-selected in Module III then 3. Initialize the maximum channel load c j as 0 4. Fo each 1-hop and 2-hop vehicle neighbos i do 5. Compute LG ij using equation (7) 6. Extact LE ij fom the context table 7. Compute L ij using equation (6) 8. If L ij > c j then 9. Set c j equal to L ij 10. End If 11. End Fo 12. Else 13. Set the maximum channel load c j as 100% 14. End if 15. End Fo RAT selection (Module V). The RAT selection pocess identifies the pe-selected RAT that minimizes the maximum channel load c j. The pocess computes then the diffeence between the maximum load expeienced with the identified RAT and with the one cuently utilized by the vehicle executing CARHet. The RAT is only changed if this diffeence is highe than α (low values should nomally be selected fo α). This citeia avoids RAT oscillations when the load impovement is minimal. MODULE V. RAT SELECTION Inputs: c j fo each RAT Output: selected RAT Execution: evey T update. 1. Initialize c as 100% 2. Fo each RAT j with 1 j N RAT do 3. If c j < c then 4. Set c equal to c j 5. Set RAT j as the selected RAT fo data tansmission 6. End if 7. End Fo Decision shaing. Multiple vehicles can take the same decision (i.e. select the same RAT) if they execute CARHet aound the same time. This cicumstance could geneate instability if all vehicles ty to educe the load of a cetain RAT simultaneously. In this case, they could oveload a diffeent RAT, and equie quickly changing the RAT again. To addess this poblem, CARHet equies vehicles changing thei RAT to infom neaby vehicle by including the CIS flag in the next CIS packet they boadcast. CARHet popa- (6)

7 SEPULCRE ET AL.: HETEROGENEOUS V2V COMMUNICATIONS IN MULTI-LINK AND MULTI-RAT VEHICULAR NETWORKS 7 gates the CIS flag up to two hops (always attached to CIS packets) since we assume that the load geneated by a vehicle affects vehicles up to two hops. All vehicles eceiving this infomation (active CIS flag) postpone the RAT selection pocess by T meas. To do so, the intenal vaiable f l is used in Fig. 4. CARHet tiggeing. The RAT selection pocess is executed evey T seconds in this study (poactive appoach) using time t u (see Fig. 4). T is a andom vaiable unifomly distibuted between T update and T update (n changes +1). T update is a constant paamete that is common to all vehicles. n changes is the numbe of consecutive RAT changes pefomed by a vehicle. This andomization educes the pobability to poduce an instable situation whee multiple vehicles e-evaluate (and maybe change) thei RAT nealy at the same time. This situation could still be poduced if a CIS packet containing an active CIS flag is lost due to popagation o intefeence. To combat instabilities, the length of the andomization inteval inceases if the instability inceases since the inteval is a function of n changes. 5 SIMULATION SCENARIOS AND SETTINGS CARHet has been evaluated using VEINS, an open souce famewok fo vehicula netwok simulations that utilizes OMNeT++ and SUMO. A highway taffic scenaio with 4 lanes (2 lanes pe diving diection) has been simulated using mobility pattens geneated by SUMO. Vehicles move at a maximum speed of 100km/h. Diffeent taffic densities ae simulated: 10, 20 and 30 veh/km/lane (equivalent to 40, 80 and 120 veh/km espectively). Each vehicle is equipped with 5 RATs (Table I) that can be simultaneously used: DSRC (IEEE p) opeating at 5.9GHz, DSRC (IEEE p) opeating at 700MHz, WiFi opeating at 5.6GHz, WiFi opeating at 2.4GHz, and an OFDM-like technology opeating in the TVWS band at 460MHz. A vehicle can tansmit data using one RAT and simultaneously eceive infomation though all available RATs. These technologies have only been selected fo the pupose of illustating the potential of heteogeneous V2V communications in multilink and multi-rat scenaios. The popagation conditions ae modeled using the Winne+ B1 model peviously descibed and that has been implemented in VEINS. Two application scenaios have been simulated in this study. In both scenaios, vehicles peiodically boadcast packets of 1024 bytes and the applications equie to coectly eceive 90% of the tansmitted packets at D. In the fist scenaio, D is set equal to 40m, and all vehicles in the scenaio tansmit the same amount of infomation R bps. Simulations have been done fo R equal to 0.5Mbps, 1Mbps and 1.5Mbps. In the second scenaio, 50% of the vehicles ae configued with R=1.5Mbps and D=40m, 25% of the vehicles with R=1.0Mbps and D=80m, and the emaining 25% of vehicles with R=0.5Mbps and D=120m. This scenaio has been chosen to emulate coopeative peception applications that equie vehicles at shot distances to exchange moe senso data (and theefoe need highe thoughput) than vehicles at lage distances. CARHet is compaed in this study to a technique that andomly selects the RAT of each vehicle evey T update (to the authos knowledge, no othe efeence schemes ae available in the liteatue). Randomly selecting the RAT distibutes the vehicles among the diffeent technologies, has vey low computational complexity, and does not equie any signaling. The esults ae also compaed to the case in which vehicles only utilize IEEE p at 5.9GHz in ode to highlight the limitations to suppot connected automated vehicles and the need fo heteogeneous V2V communications. Table IV pesents the main simulation paametes, including the configuation values of CARHet. Relatively low values fo T meas and T update have been selected so that CARHet can quickly eact to changing context conditions. Lage values would educe the fequency of RAT changes, but would also esult in vehicles not using the best RAT fo longe peiods of time. Scenaio CARHet 6 EVALUATION TABLE IV. SIMULATION CONDITIONS Paamete Values Highway length [km] 3 Taffic density [veh/km] 40, 80, 120 Numbe of lanes 4 (2 in each diection) Maximum speed [km/h] 100 Simulation time [s] 250 T meas [s] 0.2 T update [s] 1 T neigh [s] 1 α 5% Fig. 6 depicts the CBR and thoughput expeienced pe vehicle when all vehicles use DSRC at 5.9GHz and have the same application equiements (tansmit R=0.5Mbps and equie that at least P=90% of the tansmitted packets ae coectly eceived within D=40m). The esults ae pesented using box plots, which ae widely used in desciptive statistics to gaphically depict goups of numeical data. In each box plot, the top and bottom of the box ae the 25th and 75th pecentiles and theefoe the distance between them is the intequatile ange. The ed hoizontal line inside the box epesents the median. The whiskes ae lines extending above and below each box and epesent the 5th and 95th pecentiles. Fig. 6a highlights the satuation of IEEE p as the CBR exceeds the ecommended value of 0.6 [27] fo all taffic densities. These esults ae in line with the estimations in Fig. 3b that indicated that the maximum taffic density suppoted by IEEE p if all vehicles tansmit 0.5Mbps is CBR (a) CBR (Channel Busy Ratio) (b) Thoughput pe vehicle Fig. 6. CBR and thoughput pe vehicle when all vehicles use DSRC at 5.9GHz and they equie R=0.5Mbps and D=40m. Thoughput pe vehicle [Mbps]

8 8 35 veh/km. Fig. 6b shows that only fo a taffic density of 40 veh/km, vehicles can satisfy thoughput values aound R=0.5Mbps at distances lowe than D=40m. IEEE p cannot satisfy the application equiements if the taffic density o value of R inceases. Fig. 7 and Fig. 8 plot the CBR and thoughput, espectively, when all vehicles equie D=40m and R=1.0Mbps, and andomly select the RAT o implement CARHet. Fig. 7a shows that a andom (and hence unifom) distibution of vehicles between RATs esults in a diffeent channel load pe RAT since each RAT has diffeent communication paametes (Table I), in paticula diffeent bandwidth. This unequal distibution of the channel load among RATs esults in a vey diffeent thoughput pe vehicle, with the diffeences inceasing with the taffic density (Fig. 8a). This esults in that thee is a significant pecentage of vehicles that cannot satisfy the application equiements when they andomly select thei RAT (Fig. 9). A vehicle is consideed to be satisfied if its thoughput is equal o highe than 0.9 R at distances equal and lowe than D=40m. Fig. 7b shows that CARHet is capable to balance the channel load among RATs despite thei diffeent chaacteistics. This is paticulaly noticeable when compaing the median of the CBR 3. A moe balanced channel usage among RATs esults in significantly highe (and moe homogeneous) thoughput values pe vehicle with CARHet (Fig. 8b) compaed to the case in which vehicles andomly select thei RAT (Fig. 8a). This esults in a significantly highe pecentage of vehicles satisfied when implementing CARHet compaed to when andomly selecting the RAT (Fig. 9). A compaison of Fig. 9 and Fig. 3b shows that CARHet can appoximate the maximum taffic densities estimated in Section III. Fo example, Fig. 3b estimated the maximum taffic density fo R=1.0Mbps to be equal to appoximately 140veh/km. Fig. 9b shows that CARHet appoaches this maximum capacity as it can satisfy appoximately 90% of the vehicles when the taffic density is equal to 120veh/km. The maximum taffic density estimated fo R=1.5Mbps was appoximately 90veh/km (Fig. 3b). Fig. 9c shows that CARHet can satisfy moe than 90% of vehicles fo 80veh/km, but the pecentage of vehicles satisfied stongly deceases fo 120veh/km. CBR CBR (a) Random RAT (b) CARHet Fig. 8. Thoughput pe vehicle when vehicles andomly select a RAT o implement CARHet when all vehicles equie R=1.0Mbps and D=40m. Vehicles satisfied [%] Thoughput pe vehicle [Mbps] Vehicles satisfied [%] (a) R=0.5Mbps (b) R=1.0Mbps (c) R=1.5Mbps Fig. 9. Pecentage of vehicles satisfied when all vehicles equie D=40m. The RAT selection algoithms (Random and CARHet) ae executed by each vehicle evey T update =1s. This value was chosen so that the selection pocess can adequately follow elevant changes in the context conditions. Fig. 10 shows that CARHet guaantees a stable opeation that pevents vehicles constantly changing the RAT if such change has little impact on the capacity to satisfy the application equiements. Fig. 10 depicts the time between RAT changes pe vehicle (τ) when vehicles andomly select the RAT evey T update (Fig. 10a) and when they implement CARHet (Fig. 10b). With the andom scheme, the pobability that a vehicle changes its RAT is equal to 4/5. This is equivalent to appoximately changing the RAT evey 1.25s. CARHet significantly educes the numbe of RAT changes pe second pe vehicle 4 as vehicles tend to change the RAT evey 50-80s on aveage (Fig. 10b). The esults in Fig. 10b and Fig. 9 suggest that CARHet is capable to limit the RAT changes to those that have a positive impact on the capacity to satisfy the application equiements. Thoughput pe vehicle [Mbps] Vehicles satisfied [%] (a) Random RAT (b) CARHet Fig. 7. CBR when vehicles andomly select a RAT o implement CARHet. All vehicles equie R=1.0Mbps and D=40m. Taffic density: 80 veh/km. 3 Vehicles moving in opposite diections esult in changes of the channel load ove space and time. The vaiations incease as the bandwidth and data ate decease (TVWS is the most affected RAT), which explains the box plot diffeences in Fig. 7b. 4 RAT changes ae executed at the vehicle level with no additional signaling equied at the netwok level.

9 SEPULCRE ET AL.: HETEROGENEOUS V2V COMMUNICATIONS IN MULTI-LINK AND MULTI-RAT VEHICULAR NETWORKS 9 [seconds] [seconds] Vehicles satisfied [%] (a) Random RAT (b) CARHet Fig. 10. Time between RAT changes pe vehicle (τ) in seconds when all vehicles equie R=1.0Mbps and D=40m. The pevious esults wee obtained with all the vehicles in equie the same R and D. Fig. 11 depicts the thoughput pe vehicle obtained when vehicles have diffeent application equiements as detailed ealie in this section. In this case, 50% of the vehicles ae configued with R=1.5Mbps and D=40m, 25% of the vehicles with R=1.0Mbps and D=80m, and the emaining 25% of the vehicles ae configued with R=0.5Mbps and D=120m. Fig. 11a shows that a andom selection of the RAT esults in that a non-negligible pecentage of vehicles expeience a thoughput significantly lowe than demanded by the application. In this scenaio, this esult is not only due to the fact that andomly selecting the RAT can oveload cetain channels, but also to the fact that not all RATs can satisfy the application equiements. The thoughput pefomance depicted in Fig. 11a is at the oigin of the low pecentage of vehicles satisfied when andomly selecting the RAT (Fig. 12). Fig. 12 shows that CAR- Het is capable to satisfy a significant pecentage of vehicles also in the scenaios whee vehicles have mixed application equiements. The highe satisfaction levels obtained with CARHet esult fom the fact that CARHet is capable to match vehicles with the RATs that ae capable to satisfy thei application equiements. This esults in the highe aveage thoughput values pe vehicle expeienced with CARHet and its lowe thoughput intequatile ange (Fig. 11b). This low ange indicates that CARHet is capable to povide simila QoS levels to the majoity of vehicles. Thoughput pe vehicle [Mbps] (a) Random RAT (b) CARHet Fig. 11. Thoughput pe vehicle when vehicles andomly select a RAT o implement CARHet. The thoughput is shown as a function of the application equiements. The scenaio consides mixed application equiements and a taffic density of 80veh/km. Thoughput pe vehicle [Mbps] Fig. 12. Pecentage of vehicles satisfied when vehicles have mixed application equiements. 7 COMPUTATIONAL COST This section analyzes the computational cost of CARHet, and hence its feasibility. Table V epots the numbe of CPU cycles needed to execute CARHet. The infomation is pesented sepaately fo each one of the CARHet modules detailed in Section IV. Each tem in the sums coespond to the numbe of cycles needed to execute each line of the modules pseudo-code. The values shown in Table V coespond to uppe bounds since they have been estimated consideing that all the conditions evaluated in Module I to Module V ae met, and hence the instuctions inside the fo o if loops ae executed. The numbe of CPU cycles needed to execute CARHet depends on the numbe of RATs available at each vehicle (N RAT ), the numbe of vehicle neighbos at 1 hop (N 1 ), and the numbe of vehicle neighbos at 2 hops (N 2 ). It also depends on the T meas and T update paametes since these paametes influence how often CARHet is executed and how often CIS packets ae tansmitted. The numbe of CPU cycles has been computed consideing Intel CPU achitectues [29]. In this case, the multiplication of two floating point numbes equies 5 CPU cycles, and thei addition equies 3 cycles. Fig. 13 shows an example of the impact that executing CARHet will have on the CPU of a vehicle. In paticula, the figue plots an uppe-bound of the amount of CPU usage (o pecentage of the CPU's capacity) consumed by CARHet fo diffeent CPU speeds and numbe of neighbo vehicles. The figue has been deived consideing N RAT =5 and N 1 =N 2 =N (i.e. each vehicle has the same numbe of 1-hop and 2-hop neighbos). Fig. 13 shows that, even fo the highest numbe of neighbos, CARHet s CPU usage is less than 0.3%. These esults demonstate the low computational equiements to un CARHet. TABLE V. CARHET COMPUTATIONAL COST PER VEHICLE Module Execution feq. (Hz) Numbe of CPU cycles I. Context acquisition N 1 /T meas 2N 1 + N 1 + N 1 +2N 1 + 2N 1 N RAT + N 1 N RAT II. Context shaing 1/T meas 2N 1 + N 1 + 2N 1 + 2N 1 N RAT + N 1 N RAT III. RAT peselection 1/T update 2N RAT + 2N RAT + N RAT IV. Cost estimation V. RAT selection 1/T update 2N RAT + N RAT + N RAT + 2N RAT (N 1 + N 2 ) + 11N RAT (N 1 + N 2 ) + N RAT (N 1 + N 2 ) + 3N RAT (N 1 + N 2 ) + N RAT (N 1 + N 2 ) + N RAT (N 1 + N 2 ) 1/T update 1 + 2N RAT + N RAT + N RAT + N RAT

10 10 CPU usage [%] MHz 1000MHz 1200MHz Numbe of 1-hop and 2-hop neighbos, N Fig. 13. Uppe-bound of CARHet s CPU usage fo diffeent pocesso speeds, and consideing NRAT=5 and N1=N2=N. 8 CONCLUSIONS AND DISCUSSION A widespead deployment of connected vehicles and the intoduction of connected automated diving applications will notably incease the bandwidth and scalability equiements of vehicula netwoks. This pape poposes to addess this challenge by adopting heteogeneous netwoking fo V2X communications in multi-link and multi-rat vehicula scenaios. In paticula, this pape poposes and evaluates CARHet, a novel context-awae heteogeneous V2V communications algoithm that allows each vehicle to autonomously and dynamically select its communications technology (o RAT) based on its application equiements and the context conditions. To the autho s knowledge, this is the fist heteogeneous V2V communications algoithm poposed in the liteatue that is technology and application agnostic, and that allows each vehicle to autonomously and dynamically select the communications technology fo its V2V tansmissions. CARHet has been evaluated consideing a given set of communications technology fo illustation puposes. Howeve, it could well be extended to conside othe RATs. The conducted study has demonstated that heteogeneous V2V communications can help addess the bandwidth and scalability equiements that futue vehicula netwoks will face. The study has also shown that CAR- Het is capable to adequately distibute the load among RATs, and ensue high and homogenous QoS levels acoss the netwok with a low computational cost. As a esult, CARHet can satisfy the application equiements fo a lage pecentage of vehicles while appoximating the estimated uppe bound netwok capacity. CARHet is a fist poposal towads the design of futue heteogeneous V2V solutions, with still many contibutions to be expected fom the community. Fo example, heteogeneous V2V algoithms can be designed with othe objectives in mind (e.g. eliability athe than scalability), and hence with diffeent pefomance and cost functions. Solutions will need to be poposed fo scenaios in which all vehicles do not have the same RATs on boad 5. This pape has evaluated a poactive implementation of CARHet whee the RAT selection pocess is tiggeed peiodically. Howeve, eactive o hybid implementations would also be possible. In fact, 5 Simila challenges can actually be foeseen once fist V2X technologies ae deployed (e.g. DSRC based on IEEE p) and new V2X standads (e.g. 5G V2X) ae poposed fo inceasing the communications capabilities and enable additional functionality. In this case, new vehicles will have moe V2X technologies on boad than olde vehicles. eactive algoithms could educe the numbe of RAT changes by limiting them to situations in which the context conditions affecting the pefomance and cost change. ACKNOWLEDGMENT M. Sepulce and J. Gozalvez acknowledge in pat the suppot of the Spanish Ministy of Economy and Competitiveness and FEDER funds unde the poject TEC R and TEC R. REFERENCES [1] N. Lu, N. Cheng, N. Zhang, X, Shen, J.W Mak, Connected Vehicles: Solutions and Challenges, IEEE Intenet of Things Jounal, vol. 1, no. 4, pp , Aug [2] 5G-PPP, 5G Automotive Vision, White Pape on Automotive Vetical Secto, Oct Online: [3] 3GPP, Technical Specification Goup Sevices and System Aspects; Study on enhancement of 3GPP Suppot fo 5G V2X Sevices (Release 15), 3GPP TR V15.1.0, Mach [4] K. 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