Javier Gozalvez
Editor-in-Chief
This issue of IEEE Vehicular Technology Magazine includes 10 open-call articles addressing nonterrestrial networks (NTNs), artificial intelligence/machine learning (AI/ML) for millimeter-wave (mm-wave) wireless communications, the convergence of the metaverse and mobility systems, free-space optical (FSO) vehicle-to-everything (V2X) communications, maneuver coordination for cooperative driving and connected and automated vehicles (CAVs), and mobile charging systems for electric vehicles (EVs).
NTNs are considered a fundamental component of future 6G networks that can support numerous applications in both rural and urban areas. NTNs include high-altitude platform stations (HAPSs) that can be equipped with 5G or 6G equipment and embed technologies such as massive multiple-input multiple-output and reconfigurable intelligent surface (RIS). However, the design of the HAPS communication payload subsystem impacts the range of supported applications, energy consumption, flight duration, loitering time, and deployment costs. To reduce the energy consumption and extend the HAPS loitering time, the article “Multimode High-Altitude Platform Stations for Next-Generation Wireless Networks: Selection Mechanism, Benefits, and Potential Challenges,” by Alfattani et al. [A1], proposes a multimode HAPS that can adaptively switch between different modes. These modes include a HAPS super macro base station mode for enhanced computing, caching, and communication services; a HAPS relay station mode for active communication; and a HAPS-RIS mode for passive communication. The multimode HAPS provides the opportunity for operations to rely mostly on the passive communication payload while switching to an energy-greedy active mode only when necessary. The article illustrates the envisioned multimode HAPS, discusses its benefits and challenges, and validates its efficiency through a case study.
NTNs include large constellations of small-size low-Earth orbit (LEO) satellites that can provide global coverage with relatively low latency and high throughput compared to geosynchronous orbit satellites. However, increasing numbers of LEO satellites and operators risk generating severe intersatellite interference that can reduce or even eliminate the benefits of LEO satellites for NTNs. In the article “Satellite Clustering for Non-terrestrial Networks: Concept, Architectures, and Applications,” Jung et al. [A2] introduce the concept of satellite clusters to improve network performance. A satellite cluster is a group of nearby satellites that cooperatively transmit and receive signals as if they were a single multiantenna satellite. The authors describe the characteristics, formation types, and transmission schemes of the satellite clusters and evaluate the coverage and capacity improvements that clustered satellite networks can achieve over unclustered ones as the number of satellites increases. The article also reviews viable clustered satellite network architectures based on the 3rd Generation Partnership Project (3GPP) standard and discusses possible future applications of clustered satellite networks.
The article “Taming Aerial Communication With Flight-Assisted Smart Surfaces in the 6G Era: Novel Use Cases, Requirements, and Solutions,” by Devoti et al. [A3], focuses on the potential of RISs to overcome some of the limitations of aerial or air-to-ground (A2G) communications. A2G communications use flying devices such as unmanned aerial vehicles (UAVs), fixed-wing aircraft, or HAPs to facilitate communication between two nodes. A2G networks are expected to be an integral part of future 3D 6G networks as they can easily act as portable on-demand base stations. Despite their potential, A2G networks are constrained by the limited power budget, necessary onboard processing, and maximum load that flying devices can carry. To overcome these challenges, the article discusses the deployment of RIS onboard flying devices to control the terrestrial propagation environment from an elevated viewpoint. The authors identify key optimization aspects to be considered when designing RIS-based aerial networks and the associated control architecture, novel use cases, requirements, technical challenges, and open research directions.
The article “Visual Camouflage and Online Trajectory Planning for Unmanned Aerial Vehicle-Based Disguised Video Surveillance: Recent Advances and a Case Study,” by Hu et al. [A4], discusses the types and technical challenges of visual camouflage for UAVs for applications demanding UAVs to be unnoticeable by targets, e.g., public security. The authors propose a disguising method that confuses the target by constantly changing its relative position in the target’s view and present a new trajectory planning framework for UAV-based disguised tracking that uses model predictive control. The authors numerically illustrate the disguise and energy sustainability benefits of the proposed solution and how it can support both fixed-wing and rotary-wing UAVs on covert video surveillance.
mm-Wave communications utilize beamforming techniques to mitigate the path loss at the mm-wave bands by directionally concentrating the signal energy in narrow beams. Traditional mm-wave beam management algorithms usually require significant channel state information and computational overhead, limiting the widespread deployment of mm-wave communications. In the article “Vision-Assisted millimeter-Wave Beam Management for Next-Generation Wireless Systems: Concepts, Solutions, and Open Challenges,” Zheng et al. [A5] present a vision-assisted mm-wave beam management system where base stations can select the optimal beam for a target user equipment based on its location determined by ML algorithms applied to visual data. This eliminates the need for channel state information. The authors discuss typical deployment scenarios of this new framework as well as the potential ML challenges and solutions. The authors quantify the benefits that can be obtained with the proposed vision-assisted mm-wave beam management solution and discuss open research topics.
The article “Metamobility: Connecting Future Mobility With the Metaverse,” by Wang et al. [A6], introduces the concept of metamobility, a holistic realization of the metaverse in mobility. The metaverse has gained significant traction in different industries as it provides a perpetual, immersive, and shared digital universe that is linked to but beyond the physical reality. The authors present their vision of how metamobility could be fueled by the development of connectivity, autonomous driving, shared mobility, and electrification (CASE) technologies that provide a catalyst for connecting the metaverse with future mobility systems. The authors then present a basic architecture for metamobility and propose two use cases—tactile live maps and meta-empowered advanced driver-assistance systems—to showcase how metamobility will benefit and reshape future mobility systems. Each use case is discussed from the perspective of the technology evolution, key performance indicators, future vision, and critical research challenges. The article also identifies open research issues for the development and deployment of metamobility.
The article “Free Space Optical Communications for Intelligent Transportation Systems: Potentials and Challenges,” by An et al. [A7], discusses the potential of FSO communications for V2X communications given its broad and unlicensed spectrum. The authors discuss the implementation of FSO communications, including the use of adaptive illumination schemes for increased communication range without visual interference. The authors also advocate for the use of mode division multiplexing and wavelength division multiplexing to increase the communication rate as well as the possibilities of FSO for integrated sensing and communications in vehicles. The authors then propose the use of deep learning-enabled heterogeneous systems of FSO and radio frequency to satisfy the complex requirements of communication, illumination, and sensing in traffic scenarios. Finally, the article describes applications of FSO communications in various scenarios and discusses open FSO challenges for V2X communications.
CAVs can use V2X communications to exchange their driving intentions and coordinate their maneuvers. In the article “V2X Communications for Maneuver Coordination in Connected Automated Driving: Message Generation Rules,” Molina-Masegosa et al. [A8] propose new generation rules to decide when CAVs should generate and exchange maneuver coordination messages to coordinate their maneuvers. A frequent transmission of messages would provide updated information about other vehicles’ driving intentions more frequently but risks saturating the communications channel. The design of generation rules should hence guarantee safe and smooth maneuver coordinations while efficiently using the limited bandwidth. In this context, the article proposes the first two sets of V2X message generation rules for maneuver coordination between CAVs. The Risk proposal increases the rate at which vehicles generate maneuver coordination messages when vehicles detect a potential safety risk. With the Tracking Trajectories proposal, vehicles generate a new maneuver coordination message when they significantly modify their trajectory. The evaluation shows that the proposed generation rules efficiently support maneuver coordination and offer a balance between more frequent updates of the driving intentions of CAVs and lower coordination time and better control of the V2X communications channel load. The load can also be controlled using congestion control protocols. However, the article reveals that these protocols can significantly impact maneuver coordination, and their interaction should be carefully codesigned.
Cooperative driving is also the focus of the article “Toward Intelligent Connected E-Mobility: Energy-Aware Cooperative Driving With Deep Multiagent Reinforcement Learning,” by He and Lv [A9]. The article addresses cooperative driving solutions for connected and autonomous EVs (CAEVs) in mixed traffic scenarios where CAEVs coexist and interact with human-driven vehicles on the road. The article discusses the challenge to ensure travel efficiency and driving safety while maximizing energy savings for CAEVs in these mixed scenarios. To address the challenge, the authors propose the use of a deep multiagent reinforcement learning-enabled energy-aware cooperative driving solution for CAEVs using V2X communications to share the vehicle state and learned knowledge (e.g., their state of charge speed and driving policies). The authors also propose a multiagent actor-critic-based optimization scheme to optimize the CAEVs’ driving policies and the overall traffic flow performance. The authors show that their solutions enable CAEVs to learn energy-aware cooperative driving behaviors and adapt their driving in mixed traffic scenarios. Finally, the authors discuss the challenges and potential research directions for future energy-aware cooperative driving systems.
EVs can contribute to more sustainable mobility but still face some significant challenges for widespread adoption. Some of these challenges include limited range and battery capacity and the long times necessary to charge a battery. In “Mobile Charging Services for the Internet of Electric Vehicles: Concepts, Scenarios, and Challenges,” Wang et al. [A10] analyze the potential of mobile charging services (MCSs) as a supplementary charging method for EVs that currently rely on fixed charging stations (FCSs). MCSs use mobile charging vehicles that have a self-contained energy storage system that can be used to replenish the energy of a certain number of EVs. The authors describe the concept of MCSs and discuss their potential as a proactive solution to respond to specific charging requirements—including at the moment of potentially high grid pressure—and as an attractive solution for areas with low EV adoption rates or limited FCS infrastructure. The authors illustrate the ability of MCSs to reduce the average charging time with a real-time response service case study. Finally, the article discusses open technical challenges and research directions for the development of MCSs.
I hope that you will enjoy reading this issue. I would like to take this opportunity to thank our editors and reviewers for their continued support and volunteer contributions to sustain a high-quality peer-review process. It is important that we all contribute to the process to ensure the fastest, smoothest, and highest quality process possible. Please don’t hesitate to get in touch if you have any comments, ideas, or proposals to improve IEEE Vehicular Technology Magazine.
[A1] S. Alfattani, W. Jaafar, H. Yanikomeroglu, and A. Yongaçoglu, “Multimode high-altitude platform stations for next-generation wireless networks: Selection mechanism, benefits, and potential challenges,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 20–28, Sep. 2023, doi: 10.1109/ MVT.2023.3289630.
[A2] D.-H. Jung, G. Im, J.-G. Ryu, S. Park, H. Yu, and J. Choi, “Satellite clustering for non-terrestrial networks: Concept, architectures, and applications,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 29–37, Sep. 2023, doi: 10.1109/ MVT.2023.3262360.
[A3] F. Devoti, P. Mursia, V. Sciancalepore, and X. Costa-Pérez, “Taming aerial communication with flight-assisted smart surfaces in the 6G era: Novel use cases, requirements, and solutions,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 38–47, Sep. 2023, doi: 10.1109/MVT. 2023.3274329.
[A4] S. Hu, X. Yuan, W. Ni, X. Wang, and A. Jamalipour, “Visual camouflage and online trajectory planning for unmanned aerial vehicle-based disguised video surveillance: Recent advances and a case study,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 48–57, Sep. 2023, doi: 10.1109/ MVT.2023.3263329.
[A5] K. Zheng, H. Yang, Z. Ying, P. Wang, and L. Hanzo, “Vision-assisted millimeter-wave beam management for next-generation wireless systems: Concepts, solutions, and open challenges,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 58–68, Sep. 2023, doi: 10.1109/MVT.2023.3262907.
[A6] H. Wang, Z. Wang, D. Chen, Q. Liu, H. Ke, and K. Han, “Metamobility: Connecting future mobility with the metaverse,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 69–79, Sep. 2023, doi: 10.1109/ MVT.2023.3263330.
[A7] N. An, F. Yang, L. Cheng, J. Song, and Z. Han, “Free space optical communications for intelligent transportation systems: Potentials and challenges,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 80–90, Sep. 2023, doi: 10.1109/ MVT.2023.3244032.
[A8] R. Molina-Masegosa et al., “V2X communications for maneuver coordination in connected automated driving: Message generation rules,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 91–100, Sep. 2023, doi: 10.1109/MVT.2023.3284562.
[A9] X. He and C. Lv, “Toward intelligent connected e-mobility: Energy-aware cooperative driving with deep multiagent reinforcement learning,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 101–109, Sep. 2023, doi: 10.1109/MVT.2023.3291171.
[A10] R. Wang, H. Wang, K. Zhu, C. Yi, P. Wang, and D. Niyato, “Mobile charging services for the Internet of Electric Vehicles: Concepts, scenarios, and challenges,” IEEE Veh. Technol. Mag., vol. 18, no. 3, pp. 110–119, Sep. 2023, doi: 10.1109/ MVT.2023.3289302.
Digital Object Identifier 10.1109/MVT.2023.3300608