Opportunities for Intelligent Reflecting Surfaces
in 6G-Empowered V2X Communications

The integration of IRS into V2X communications can significantly improve the system performance. These improvements can be achieved in form of security, capacity, energy efficiency and coverage extension. Some of the potential use case scenarios of V2X communications involving IRS are shown in Fig. 2. Specifically, these use cases are V2I, V2V, V2D and V2S, respectively. Moreover, these use case scenarios are discussed in the following subsections.

Although the application of IRS in wireless communication systems has been widely addressed, its application in vehicular environments has been briefly studied in the literature. In the following, we concisely review the most related works.

For example, the work in [6] has proposed a paradigm for exploiting IRS with non-orthogonal multiple access (NOMA) in drone-enabled wireless networks. The authors simultaneously optimize the phase shift control, dynamic trajectory design, signal decoding order, and power control to minimize the drone’s energy consumption. They design a decaying deep Q-network (DQN)-based algorithm to solve the formulated problem. Simulation findings show that with RIS, the drone energy consumption may be significantly minimized, and IRS with NOMA consumes less energy than IRS with orthogonal multiple access.

Different from other works that mainly focused on communication, in [7], the authors have considered both communication and computation in a mobile edge computing enabled vehicular network involving IRS. They address the problem of task scheduling which includes allocating processor and link resources. They propose a dynamic task scheduling algorithm to maximize the computation throughput. The simulation results demonstrate the effectiveness of their approach in terms of task offloading rate, computing rate, and successful finish rate. Resource allocation for IRS enabled V2X communications has also been addressed in [8], where the authors aim to maximize the V2I capacity while guaranteeing minimum capacity of V2V links. They address a joint power allocation, IRS reflection coefficients, and spectrum allocation problem, proposing an alternating optimization algorithm.

The authors in [9] design a similar 3-plane framework including vehicles, IRS-deployed buildings and BSs, which perform the resource allocation and task processing for the vehicles. The vehicles’ data is transmitted to the BSs through IRS. To reduce energy consumption and latency, the authors propose a deep reinforcement learning (DRL) strategy for efficient resource allocation, which includes the vehicle transmission power, IRS reflection phase shift, and BS detection matrix.

A downlink vehicular communication model is studied in [10]. The authors consider communication from road side unit (RSU) to vehicles through IRS-enabled buildings when the vehicle becomes blocked by obstacle vehicles. They approximate the outage probability based on series expansion and central limit theorem. Finally, in the simulation results, they demonstrate improved vehicle coverage by using IRS and conclude that a larger IRS can reduce the outage probability. In [11], the authors consider an indirect transmission from RSUs through IRS deployed on buildings to some dark zones. Similar to [10], they utilize IRS as an indirect means of transmission due to the blockage of the vehicles. They formulate a joint resource scheduling and RSU passive beamforming aiming at maximizing the minimum average bit rate. They resort to DRL to obtain RSU wireless scheduling and Block Coordinate Descent (BCD) to solve the passive beamforming of the IRS.

In [12], IRS has been integrated into millimeter wave (mmWave) vehicular communication. The authors address the issue risen by high mobility and the challenge of obtaining the accurate channel state information (CSI). They have studied both single and multi-vehicle cases with the objective of maximizing the uplink average achievable sum-rate by jointly optimizing the transmit power, multi-user detection matrix and the RIS reflection phase shift. In the numerical results, it is demonstrated that their transmission framework is robust to the performance loss caused by outdated CSI and keeps the average sum-rate at a favourable level. A similar problem is studied in [13], where apart from the optimization parameters in [12], they also consider the spectrum reuse of V2V links in the joint formulated mixed-integer non-convex optimization problem. Then they approximate the outage probability of the V2V links and decompose the problem into three sub-problems where the alternated updated problem achieves a near-optimal solution.

In [14] the authors study a high mobility communication scenario between passengers and BSs through IRS deployed on the vehicle. They address mitigating the Doppler effect through approximations and tuning the IRS reflection over time.

In [15], the secrecy outage performance of RIS enabled vehicular communications is analyzed, where they consider two scenarios of V2V and V2I. In the V2V scenario the RIS functions as a relay and in the V2I case, it acts as a receiver. In both cases, the authors assume the presence of a passive eavesdropper. They derive the closed-form expression for the secrecy outage probability and demonstrate the effectiveness of the assistance of the RIS in both scenarios in the simulation results.

Table I summarizes the most related works in the area of IRS enhanced V2X communications. Most of the works assume that IRS is intelligently deployed and configured under the control of a ground control station such as a BS. In most of the works, the IRS is considered to be deployed on the building sides, aiding the V2V and V2I vehicular communications. The above studies proved that the application of the IRS can boost the performance of vehicular communication by providing different example scenarios. Nevertheless, the research on IRS-aided V2X is still in its infancy as V2D, and V2S scenarios are not well-investigated and their capacities are not well exploited. In particular, in high mobility scenarios when IRS is equipped with drones or vehicles, resource allocation problems are even more challenging and worth investigating.

We consider a system where $N$ vehicles equipped with low-powered sensors, also called low-powered devices, are randomly distributed over a predefined geographical area. Generally, low-powered devices have minimal computational resources and cannot compute the data requiring extensive computational in a minimal amount of time. As a result, the quality of services of such devices in a high computational vehicular scenario can be highly compromised. To address this issue, vehicles can be efficiently connected to an access point (AP) integrated with a VEC (also stated as VEC AP) through cooperative IRS-enabled drone communications¹¹1In this work, we assume that the direct link between vehicles and VEC AP is nLOS dominant due to large objects. Thus, we consider an IRS enabled cooperative drone communication to assist transmission between vehicles and VEC AP., providing communications and computational resources in an on-demand fashion. The drone is flying at a fixed height of $H$, and IRS consists of $K$ passive reflecting elements. The total bandwidth of the system is denoted as $B$. Due to the high mobility and flexible deployment features, this model considers that IRS is equipped with a drone. The VEC AP allows vehicles to offload their extensive computation using a partial offloading scheme. The channels between vehicles and VEC AP undergo block fading, i.e., it remains constant for an entire period of time. We also assume that the channel state information is perfectly known in the system.

The main goal of this optimization framework is to reduce the computational time of the task in the VEC system involving IRS-enabled cooperative drone communication. In particular, this framework optimizes the computational and communication resources among VEC AP and vehicles subject to various practical constraints such as vehicle energy consumption, VEC AP computational resources, drone placement, and efficient phase shift design at IRS, respectively. This framework exploits a partial offloading scheme such that $\Phi_{n}$ percent of the task is computed locally using local resources while the rest is offloaded to the VEC AP for extensive computation. For instance, the amount of computational resources allocated to vehicle $n$ at the local and VEC AP levels can be represented $\rho_{n}^{l}$ and $\rho_{n}^{e}$, where $n\in N$. Then the optimization problem of computational task minimization is formulated as non-convex because of the non-linear objective function. Moreover, the coupling of decision variables further complicates the optimization problem. To address the challenges mentioned above and make the optimization more tractable, we perform problem transformation first and then decouple into sub-problems, i.e., drone placement optimization, phase shift control, and computational and communication resources between vehicles and VEC AP. Next, a convex optimization method is used to achieve efficient solution. Specifically, the proposed method is iteratively updated to find the best possible solution that meets all system constraints.

To validate the effectiveness of the proposed scheme, extensive simulations are carried out using the parameters given in Table II. The numerical results of the proposed IRS-enabled vehicular hybrid approach scheme, i.e., IRS Enabled VHA are compared with the following benchmark schemes, i.e., 1) IRS-enabled VEC, i.e., IRS Enabled VEC, in which all the tasks of vehicles are offloaded to VEC AP via IRS-enabled drone communication. 2) Without IRS VHA, i.e., Without IRS VHA drone communication, in which tasks are partitioned optimally into two portions, one portion of the task is computed locally. At the same time, the other is offloaded to VEC AP without the assistance of IRS-enabled drone communication.

In the VEC communication network, task segmentation $\Phi_{n}$ is an important parameter that directly influences the performance of the system. $\Phi_{n}$ mainly depends on the computational resources of the vehicles, the number of cycle requirements, and the data size of the task as shown in Fig.3.

Most of the IRS enabled V2X communications works in literature assume that perfect CSI is available at the system while utilizing existing channel estimation techniques. In this regard, the acquisition of highly accurate CSI remains an open issue for V2X communication, especially in the context of IRS. Moreover, three types of channels exist in IRS enabled V2X communications; the first is the direct channel existing between source and destination; in this case, perfect CSI can be obtained with traditional channel estimation techniques executed with ample computational resources at the source side. Furthermore, the second is the indirect channel between source and the IRS, and lastly, the third is the reflection channel constructed between IRS and destination. However, in the second and third cases, obtaining highly accurate CSI is challenging due to the passive nature of IRS elements, which have limited signal processing capabilities. Additionally, the indirect channel estimation is performed by accurately estimating the angle of arrival and departure. In contrast, the reflection channel poses a formidable challenge as environmental conditions, and vehicle location vary over the period of time. Thus, addressing these issues pertaining to the acquisition of accurate CSI is an essential future research direction for IRS enhanced V2X communications.

In future 6G systems, numerous machine learning (ML) techniques are envisioned to perform intrinsic and complicated tasks related to resource allocation and signal processing. In this regard, ML techniques offer a competitive edge over traditional methods or algorithms for seamlessly performing computationally intensive tasks. Moreover, only a handful of works in open technical literature have considered enabling V2X communications via ML techniques despite their wide availability and unlimited capabilities. Furthermore, research on facilitating IRS enabled V2X communications via ML techniques is a viable futuristic research direction. However, it has not come under the limelight and has not received much attention from the research community. In this context, ML techniques could solve various optimization, classification, prediction, and decision-making tasks which could be extremely useful for facilitating IRS enabled V2X communications.

Due to the mobility feature, Drones can be efficiently used in various civil, public, and military applications. In this regard, both control and communications are essential ingredients of a drone system. Moreover, the upcoming 6G systems will support non-terrestrial networks based on drones serving as aerial BSs or relays. According to 3GPP release 15, a drone flying at an altitude of 80 meters or above has a 100% probability of achieving LoS communications capable of mitigating shadowing and signal blockage. Generally, drones are categorized into two types: fixed-wing drones and rotary-wing drones. IRS can be coated on its surface either in a spherical or a flat shape for fixed-wing drones. On the other hand, for rotary-wing drones, IRS could be mounted on it as a separate module. In this regard, to satisfy quality-of-service requirements for 6G services such as ultra-reliable and low latency communications (URLLC), the specific positioning of IRS enabled drone communication is crucial. Therefore, the deployment of IRS enabled drones to support V2X communication is a promising research direction to investigate.

In 6G systems, for both non-terrestrial as well as terrestrial networks utilizing IRS, the optimization of beamforming poses a real challenge. In this regard, for non-terrestrial networks based on drone communications, random jittering of drones attributed to strong gusts of wind could lead to an erroneous estimation of the angle of departure existing between IRS and the vehicles. Thus, an accurate method is required to achieve phase alignment of the IRS elements in real-time. On the other hand, for terrestrial networks, the consideration of continuous phase resolution is inaccurate because the IRS phase shifts are discrete in nature. Consequently, such an assumption could lead to signal misalignment, which in turn leads to an inaccurate IRS beamforming. The quantization technique should be applied to continuous phase shifts to address this issue. Thus, as evidenced by the previous discussion beamforming optimization to support IRS enabled V2X communications is a hot futuristic research area.

The growing high data rate demands requires to explore mmWave and THz frequencies ranges to enable vehicular networks to sustain high traffic volumes. The utilization of frequencies in the mmWave and-THz bands is already being investigated and at 120 GHz, data rates of up to 10 Gb/s across distances of up to 850 m have been demonstrated. The road beyond 300 GHz necessitates the creation of innovative transceivers that can function at these incredibly high frequencies. To overcome the intrinsically significant pathloss at these frequencies, such transceivers must have sufficient power and sensitivity while also achieving low noise figures. The penetration losses, high Doppler spread and blocking are some other issues which limits the use of mmWave and THz band in V2X communications. On one hand, the additive advantage of IRS to overcome such issues, makes it a promising candidate to support mmWave and THz bands in V2X communication. On the other hand, the development of RIS to support mmWave and THz bands increases the challenges such as mutual coupling, electromagnetic interference, and many more. Thus, to enable the use of mmWave and THz bands in IRS enabled V2X communications a significant amount research work is required, which makes it a promising future research area.

On one hand, the use of RIS enabled wireless communications play a pivotal role in establishing reliable communication links between various entities present in transportation systems. On the other hand, the broadcast nature of wireless signals makes it vulnerable to spoofing assaults, jamming, eavesdropping, etc. Physical layer security plays an active role to overcome such vulnerabilities by improving the average secrecy capacity. Recently, it has been shown that the use of RIS in vehicular network can improve the security of legitimate vehicles by improving symbol error probability. Although the literature show some interesting results in terms of utilizing RIS to improve the secrecy capacity, yet the impact of fast optimization of IRS reflection coefficients, advance knowledge of perfect CSI, optimal beamforming, and spectrum allocation are some of open issues that requires attention to find reliable and fast solution in improving physical layer security for IRS enabled V2X communication.