10.1. Introduction

Vehicular communication needs will evolve and experience a boom due to the ascent of connected and autonomous vehicles (AV). Connectivity of various types such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and more broadly vehicle to X enables the deployment of a wide variety of applications aiming first of all to improve road safety, and also user comfort during travel. In the context of AVs, new services will emerge, such as platooning, which enables a human-driven car to guide AVs gathered in a convoy through the urban network. Car sharing and the Internet of Things will further broaden the range of services offered. Although currently few, these applications will have various demands in terms of quality of service (QoS) and communications security, which will need to be addressed. Communication link reliability will require self-adaptation of radio access technology, which is made possible by cognitive radio (CR), a technology that is able to detect free frequency bands and adapt its transmission parameters to communication needs and constraints. This concept was introduced by Mitola and Maguire (1999).

CR is defined by its perception, adaptation and cognition capacities. It is characterized by its capacities in terms of spectral interoperability, efficiency, optimization of radio resources and improvement of communications reliability, which are of significant interest to the connected AV. The first prototypes were successfully developed to meet the demands of military communications and those of public security. Besides being able to perceive and adapt to its radio-electrical environment, CR also has learning capacities via artificial intelligence techniques.

Section 10.2 introduces the AV and its main components. Section 10.3 describes applications of vehicle communication and their constraints in terms of QoS. Section 10.4 presents various communication architectures. Section 10.5 highlights the contribution of CR to the vehicular field. This section also presents a state-of-the-art of the main research works in the CR field for vehicular networks. Section 10.6 clarifies our positioning within SERENA project. Finally, section 10.7 concludes this chapter.

10.2. Autonomous vehicles

The AV, the driving of which is partly or fully automated, is currently one of the main technological challenges. Its ascent is expected to radically change the mobility, safety and behavior of road users.

Research on AVs, or rather on automated and connected vehicles, is very active and covers many domains, such as connectivity through digital infrastructure, environment sensing technologies, safe and accurate localization, high definition cartography, the legal field with all regulatory and legal aspects related to responsibility (vehicle owners, manufacturers, State, communities) but also philosophy and ethics. What is the moral responsibility of the developer of artificial intelligence behind the vehicle?

10.2.2. The main components

A car is referred to as autonomous if its driving is partly or fully automated through various sensors enabling the perception of the environment and connectivity to road infrastructure in order to anticipate the events on the road. The various elements enabling AV to perceive, localize and communicate are summarized in Figure 10.1.

The forward and back perception systems are able to perceive the physical environment in order to prevent a potential collision. These systems are of a multisensor type and include anti-collision radar, lidar and imaging systems in the visible spectrum and sometimes in the infrared spectrum. Certain systems also consider audio sensors. The data collection and processing module is responsible for data recovery. It is directly linked to the control area network (CAN) bus of the vehicle and enables the access to data such as speed, acceleration or temperature and humidity, but also to the state of the vehicle and its environment.

SAE level	Name	Description	Driving execution (steering wheel, acceleration, braking)		Monitoring of driving environment	Driving take-over	System capacity
Human monitoring of the driving environment
0	No automation	Full time execution by the human driver of all aspects of dynamic driving task, even when reinforced by alert or intervention systems	Human driver	Human driver	Human driver	N/A
1	Driving assistance	Execution (specific driving mode) by the driving-aid system of maneuvers, either on direction, or on acceleration/deceleration, by means of information on the driving environment. The human driver executes all the other aspects of the dynamic driving task.	Human and system driver	Human driver	Human driver	Certain driving modes
2	Partial automation	Execution (specific driving mode) by one or several driving-aid systems of actions both on the direction and on acceleration/deceleration by means of information from the driving environment. The human driver executes all the other aspects of the dynamic driving task.	System	Human driver	Human driver	Certain driving modes
The automated driving system monitors the driving environment
3	Conditional automation	Execution (specific driving mode) of all the aspects of the dynamic driving task by an automated driving system with the expectation that the human driver will appropriately respond to an intervention request	System	System	Human driver	Certain driving modes
4	Strong automation	Execution (specific driving mode) of all the aspects of the dynamic driving task by an automated driving system, even though the human driver does not appropriately respond to the intervention request	System	System	System	Certain driving modes
5	Full automation	Full time execution by an automated driving system of all the aspects of the dynamic driving task under all road and environmental conditions that could be managed by a human driver	System	System	System	All driving modes

Nowadays, communication equipment relies on the ITS-G5 system and the existing cellular systems. It enables communication between vehicles and infrastructure, but also intervehicle communication. It is particularly responsible for the transmission of alerts to control centers in case of detection of potentially dangerous situations.

The human-machine interface can be used by the driver to interact with the vehicle.

The localization system enables the geo-positioning of the AV, which will be all the more accurate as it is associated with high definition mapping and merging of data issued from multiple sensors.

For further details, the reader is referred to the thematic report “Perspective on the autonomous vehicle” conducted by IFSTTAR (Institut français des sciences et technologies des transports, de l’aménagement et des réseaux [French Institute of Science and Technology for Transport, Development and Networks])¹.

10.3. Connected vehicle

Cooperative Intelligent Transport Systems (C-ITSs) based on vehicular communications enable the deployment of new applications. These can be classified into several categories; road safety and entertainment are worth being mentioned here. The needs in terms of QoS and the performances vary depending on the type of application.

10.4. Communication architectures

C-ITS vehicular networks rely on a set of ISO (International Organization for Standardization) and ETSI (European Telecommunication Standards Institute) standards defining the architecture of each component (vehicle, roadside unit, road management center, etc., see Figure 10.2) on the basis of an ITS (Intelligent Transport System) protocol stack and an OSI (Open Systems Interconnexion) protocol stack.

Each subsystem illustrated in Figure 10.2 is defined from the same architecture, which is composed of (see Figure 10.3):

• – an “Access” layer, which represents OSI layers 1 and 2;
• – a “Network and transport” layer, which represents OSI layers 3 and 4;
• – an “Installation” layer, which represents OSI layers 5, 6 and 7;
• – an “Application” layer, which manages the production of C-ITS service;
• – a cross “Security” layer for message securing;
• – a cross “Management” layer, which manages the interaction between various layers.

In terms of access, two types of communication are used:

• – in ad hoc mode, ITS-G5; in this case, the transport is made either in IP or more generally using geo-networking (ETSI 2014);
• – cellular (3G/4G); the link is then exclusively IP.

These two access modes are often coupled in order to increase service coverage; this is the hybrid mode.

10.5. Contribution of CR to vehicular networks

CR is the combination of a so-called “software” radio with a decision-making module. This combination enables the dynamic adaptation of the radio system to its electromagnetic environment. As already mentioned, the concept of CR was introduced by J. Mitola in 1999 (Mitola and Maguire 1999). CR can be used in the vehicular domain in order to always remain connected despite the mobility and the variation of the type of telecommunication network available at a given moment along the vehicle path. This section first describes the specificities of CR, then presents its contribution to vehicular networks. This is followed by a state-of-the-art of the main research works in the field of CR for vehicular networks.

10.5.1. Cognitive radio

The main contribution of CR is better management of the radio-electrical spectrum, using frequency bands that are left vacant at a given instant, because of dynamic access to the spectrum. For this purpose, CR generally distinguishes between two user categories. The users referred to as primary can use the licensed bands (frequency bands of mobile operators, etc.) at any moment, based on adequate subscription. The second category of users is that of secondary users. In this category, the users cannot use the licensed bands unless they are free at the moment of use, there is no risk of generating interference and it is possible to change the band as soon as a primary user shows up. To be able to dynamically access the spectrum, the CR system must make its decisions based on the policy of spectrum allocation defined by the regulatory authorities. In general terms, CR can adapt to its environment because of electromagnetic environment sensing modules, spectrum analysis, wave shape recognition, decision making and the capacity for dynamic reconfiguration of the parameters of the radio system (throughput, frequencies, modulation, coding, etc.). The operation of CR follows a cycle known as a “cognition loop”, as illustrated in Figure 10.4.

CR is a very active research domain. Indeed, the capacity of a radio system to analyze its environment, to choose the bands in which it can emit and to reconfigure are increasingly important characteristics for the cohabitation of various wireless telecommunication systems, for the optimization of services depending on mobility and on network load, for information routing through various networks, etc.

For further details on CR systems, the interested reader is invited to refer to Arslan (2007), Doyle (2009) and Palicot et al. (2011).

In the context of 5G development, the 5GPPP group, financed by the European Commission, has played a very important role in prestandardization since 2015. In this context, dynamic access to various frequency bands and to various radio access technologies is possible (extended Dynamic Spectrum Access [eDSA], MAC framework). The architecture of this new multitechnology access protocol is under development and relies on LTE-A. The centralized radio resources manager (cRRM), the spectrum manager and the operation and administration and management (OAM) play a fundamental role, which could be related to the cognitive manager that will be introduced further on (5G PPP Architecture Working Group 2017).

10.5.2. CR-VANET

CR-VANET, which stands for Cognitive Radio for Vehicular Ad-hoc Network, or CRAVENET, which stands for Cognitive Radio Assisted Vehicular Network, is an evolution of the ad hoc vehicular network.

This type of network uses the capacities of CR to enable a better management of the radio spectrum, guaranteed connectivity and improvement in the available bandwidth and QoS.

Singh et al. (2014) and Eze et al. (2017) propose an overview of the research works on CR-VANET. It is a very active domain. In Singh et al. (2014), the authors propose a taxonomy of the main problems studied in the literature. In Eze et al. (2017), work in the routing field, MAC layer and security completes the taxonomy. The work related to simulators and to evaluation platforms is also presented.

Figure 10.5 illustrates the main fields of research dealt with.

The following sections enumerate the main publications in the fields indicated in Figure 10.5, namely those on detection of spectrum sensing, identification of the type of system present in the analyzed band, spectrum management, QoS and network-related work.

10.5.2.1. Detection of spectrum sensing

The main theme of spectrum detection, also referred to as “spectral survey”, is to enable the best possible detection of spectrum sensing by primary users, taking into account the vehicular characteristics. There are many such works. Generally speaking, there are several approaches, such as narrow band or wide band, cooperative or non-cooperative approaches and blind or non-blind approaches. When a user is detected in the surveyed band, it is important to detect the type of modulation in order to be able to identify the network to which the detected primary user belongs.

The field of spectral survey and modulation detection has been very active in recent years. Even a standard was proposed, namely the IEEE 802.22 (IEEE 2019) standard. A certain number of publications focused on radio environment detection when the activity of channels strongly varies in time, particularly in the railway sector. Blind spectral survey methods were developed by Hassan et al. (2014), notably in the presence of impulsive noise, and Bouallegue et al. (2018). Various modulation recognition methods were proposed in Hassan et al. (2010, 2012), Kharbech et al. (2013) and Kharbech (2018). This theme is very important, since a mobile environment composed of vehicles leads to frequent variations of spectrum sensing, particularly depending on the environment (highway, country road, etc.).

The cooperative approach relies on the use of a database or clustering, as proposed in Li et al. (2018). The results of the detection made by several vehicles is combined and analyzed in view of the best allocation of frequencies among vehicles. This approach is all the more interesting as the available frequency bands may vary according to the geographical position of the vehicle. Data can be merged in a distributed manner among vehicles or in a centralized manner, passing through an infrastructure, such as the roadside units.

10.5.2.2. Spectrum and QoS management

In this category, research focuses on the methods enabling the best management of the spectrum, while achieving a certain QoS. The proposed methods are centralized, distributed or hybrid.

The centralized method involves the transmission of various spectrum and QoSrelated information to an infrastructure in charge of collecting and analyzing information in order to decide on the vehicle behavior.

The distributed method involves relying on its pairs and on itself for the best spectrum and QoS management. An example dealing with peer-to-peer and therefore distributed distribution is presented in Bradai et al. (2014). Video content is broadcast between vehicles by choosing the best channel available at an instant t, by conducting a dynamic analysis of the quality of channels and relying on a peer-to-peer network for data transmission. Channel selection relies first of all on the best available dedicated short-range communications (DSRC) channel. Otherwise, CR supports the choice of the best channel among available channels that do not belong to DSRC. The selection criterion is the quality of the received signal strength indication (RSSI) connection.

With regard to the hybrid method, which is used in Niyato et al. (2011), this relies on a structure such as road infrastructure and on the vehicles forming an opportunistic network to best manage the spectrum and the QoS. In Niyato et al. (2011), the authors try to reduce the infrastructure load by relying on the cluster leaders for the communication between clusters and with infrastructure in order to make the best decisions.

10.5.2.3. Network

All the classical network themes are applied here to vehicular networks, namely mobility management, routing, content distribution and security management.

In the context of mobility management, handover is taken into account to maintain an active connection and prevent channel interferences. Hence, in Kumar et al. (2017), the authors propose a solution for spectral handover management.

Several methods are proposed for message routing: based on geographical position, without knowledge on geographical position, etc. Hence, in Usha and Ramakrishnan (2019), the authors improve the MPR OLSR algorithm by taking into account inactive channels in order to use them while choosing relay nodes with the highest number of new neighbors.

The work on content distribution focuses on the use of game theory or the peer-to-peer approach. Hence, in Tian et al. (2019), the authors use an evolutionary game approach that assigns licensed channels to secondary users based on price evolving in time and load balancing using this channel. Concerning the use of the peer-to-peer model, the work of Bradai et al. (2014) is worth mentioning, which relies on the notion of a neighborhood to broadcast video fragments and thus enable video content broadcast through the network.

Finally, network security and the protection of user’s private life are very important research subjects. Indeed, road safety messages are essential for the operation of a CR-VANET network to the same extent as in a VANET network (Mitra et al. 2016; Wei et al. 2016). It is therefore important that these messages (accident management, collision management, etc.) reach their destination. In Mitra et al. (2016), the focus is on the detection of black hole attacks in the network and their elimination. These attacks involve the creation of a fictitious virtual node through which all communications enter but do not exit in order to disrupt communications between vehicles. The proposed solution is the detection of these attacks and, once the black hole is identified, an alternative path is set up, bypassing the black hole and enabling communications to resume. In Wei et al. (2016), the authors proposed the use of a light cloud in association with a roadside unit infrastructure (UBR) for securing communications in a CR-VANET. Communications are also secured by using a new service, referred to as “Spectrum Sensing as a Service” (SaaS), enabling a detection of the cooperative spectrum using the implemented cloud.

The following presents research that we intend to conduct in the field of CR for vehicular networks.

10.6. SERENA project: self-adaptive selection of radio access technologies using CR

SERENA is a joint research project conducted by LaBRI (Laboratoire bordelais de recherche en informatique [Bordeaux Laboratory for Computer Science Research]) and IFSTTAR (Institut français des sciences et technologies des transports, de l’aménagement et des réseaux [French Institute of Science and Technology for Transport, Development and Networks]). It also benefits from the support of the SysNum cluster (Numerical systems) of Idex Bordeaux (Initiative d’excellence de l’université de Bordeaux [Initiative of Excellence of the University of Bordeaux]). The general objective of this project, starting at the end of 2019, is the improvement of vehicular communications to enable the deployment of new services, in particular those required for the autonomous vehicle. This involves, in particular, the definition of a self-adaptive selection mechanism of radio access technologies in order to always be best connected.

The proposed self-adaptation mechanism relies on the specification of new decision-making algorithms that are capable of taking into account various constraints, such as QoS, security, energy consumption or user preferences (such as the cost of the communication, for example). The prediction of the probable evolution of the service level will also enable proactive actions in order to provide service continuity in mobility.

The SERENA project enables the optimization and improvement of the quality of data exchanges between vehicles and with the infrastructure. Moreover, because of the retained approach, the evolutions of radio access technologies can be taken charge of due to the use of CR and the virtualization of network functions.

10.6.2. General architecture being considered

The general architecture of the proposed system relies on the use of CR and the virtualization of network functions.

10.6.2.1. Cognition loop and cognitive manager

As previously mentioned, CR is a technology well adapted to the self-adaptation of radio access technology to meet vehicular communication needs.

The objective here is to adapt the operation of the communication module so that it can, on the one hand, select the best access technology/technologies in the presence of constraints and use cases and, on the other hand, consequently adapt its communication parameters. For this purpose, a cognition loop and an appropriate cognitive manager need to be specified.

Specification of the cognition loop

Using the knowledge resulting from the stages of electromagnetic environment analysis, CR makes reconfiguration decisions in a dynamic manner, depending on certain predefined objectives, so that the efficiency of spectrum use is improved with no human intervention (cognition loop).

Within the SERENA project, decision making may lead, for example, to stopping certain low priority flows in order to protect the more critical flows or to a modification of the level of communications security. It is therefore important to specify the cognition loop so that it can display the theme of the SERENA project, as illustrated in Figure 10.6.

This cognition loop, as already indicated, includes four large stages:

• – collection (observation): in this phase, data useful to decision making are collected. Examples of useful data include speed, position of the vehicle and radio environment (cellular networks present in the environment). Static data such as QoS constraints associated with a type of application are previously stored in the knowledge base;
• – analysis and prediction: an analysis of gathered data is conducted using the knowledge base. Machine learning techniques can be used to predict, for example, the availability time of various frequency bands. An initial filtering can also be done considering only the frequency bands that are expected to meet the communication needs;
• – decision: this stage enables the selection of the radio technology and communication protocols to be used, or the level of security to be applied relying on data resulted from the previous stage. The decision needs to take into account the short term forecast of the availability of connections between two vehicles or the availability of the telecommunications infrastructure in order to guarantee service continuity. Considerations concerning the near future may rely on information on vehicle motion, on the motions of neighboring vehicles or on infrastructure knowledge (information contained in the knowledge base);
• – action: this stage enables a set of actions such as radio reconfiguring (change of frequency band, possible modification of the transmission parameters, etc.) according to the characteristics of the access technology retained during the previous stage, stopping low priority communications to benefit from more bandwidth, increasing the security level, etc.

Cognitive manager specification

A cognitive manager uses various cognitive engines (Ouattara 2014) to set up a cognition loop. These engines are in charge of measurement, analysis, reasoning, learning, decision making and adaptation. The content of each of the various engines required for the set-up of the cognition loop of the SERENA project needs to be specified. It is also important to specify the content of the knowledge base. This contains, on the one hand, static information such as the location of roadside units or the networks present in the area and, on the other hand, calculated or forecast information from the cognitive engines.

10.6.2.2. Network function virtualization

Network function virtualization (NFV) is nowadays a major evolution of networks. In combination with the software-defined networking (SDN) technique, it offers many advantages to network operators, particularly rapid deployment, higher flexibility and better adaptation to context. The introduction of these two technologies is deeply transforming networks and will contribute to accelerating the introduction of the autonomous car (Mendiboure et al. 2019). In particular, 5G relies on SDN/NFV to implement network slicing, which is a logical slicing of the network, to make possible the taking charge of various categories of services such as ITS services with low latency and high availability/reliability. There are still many challenges, such as the orchestration of slices, the management of network functions and the security of SDN networks (Foukas et al. 2017).

The service life of a vehicle is relatively long (several years) in relation to the current evolution of radio technologies. Hence, many new communication technologies may emerge within this period of time. The NFV approach is an important asset for the SERENA project, as it ensures the continued existence of the retained solution in relation to technological evolutions, particularly of new radio access means. Indeed, the virtualization of the vehicle communication module will make it possible to readily add new communication functionalities through the addition of software functions. This approach is all the more facilitated by the fact that radio communications rely within this project on SDR.

10.7. Conclusion

AV is being developed and new applications are emerging. These applications have specific communication needs, either in terms of latency, range or bandwidth. For the best operation of these applications, it is important to select the radio access technology that is best adapted to their needs and therefore to study the advantages and drawbacks of the architectures on which these access technologies rely.

The radio spectrum being poorly used, the use of CR makes it possible to find a solution to the needs of communication continuity in the vehicular environment. This chapter illustrated the main contributions of CR to vehicular domain, such as the improvement of network coverage or the improvement in available bandwidth.

In a CR-VANET, CR can bring its own stakes. For example, in Singh et al. (2014), the stakes may relate to the reliability of urgent message communication, the management of dynamic topology, the scarcity of available bands, the management of new communication technologies and the adaptation to various environments (highway, urban area, rural area, etc.) and spectrum distribution.

Within the SERENA project, CR associated with virtualization (NFV) and reconfiguration (SDN) of network functions will enable the vehicle communication module to select the best radio access technology, adapt its radio parameters and be resilient in front of technological evolutions.

The use of AI tools as decision-aiding tools goes hand-in-hand with the development of the AV and machine learning algorithms, in particular, will attract significant enthusiasm in this field.

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