Connected and fully automated vehicles are expected to revolutionize our mobility in the near future on a global scale, by significantly improving road safety, traffic efficiency, and traveling experience. Enhanced vehicular applications, such as cooperative sensing and maneuvering or vehicle platooning, heavily rely on direct connectivity among vehicles, which is enabled by sidelink communications. In order to set the ground for the core contribution of this paper, we first analyze the main streams of the cellular-vehicle-to-everything (C-V2X) technology evolution within the Third Generation Partnership Project (3GPP), with focus on the sidelink air interface. Then, we provide a comprehensive survey of the related literature, which is classified and critically dissected, considering both the Long-Term Evolution-based solutions and the 5G New Radio-based latest advancements that promise substantial improvements in terms of latency and reliability. The wide literature review is used as a basis to finally identify further challenges and perspectives, which may shape the C-V2X sidelink developments in the next-generation vehicles beyond 5G.

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On the Design of Sidelink for Cellular V2X: A Literature Review and Outlook for Future

—Automotive-Industry 5.0 will use beyond ﬁfth- generation (B5G) technologies to provide robust, computationally intelligent, and energy-efﬁcient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient backscatter communications (AmBC) have gained signiﬁcant interest in the research community for providing battery-free communications. AmBC can modulate useful data and reﬂect it towards near devices using the energy and frequency of existing RF signals. However, obtaining channel state infor- mation (CSI) for AmBC systems would be very challenging due to no pilot sequences and limited power. As one of the latest members of multiple access technology, non-orthogonal multiple access (NOMA) has emerged as a promising solution for connecting large-scale devices over the same spectral resources in B5G wireless networks. Under imperfect CSI, this paper provides a new optimization framework for energy-efﬁcient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks. We simultaneously minimize the total transmit power of the V2X network by optimizing the power allocation at BS and reﬂection coefﬁcient at backscatter sensors while guaranteeing the individual quality of services. The problem of total power minimization is formulated as non-convex optimization and coupled on multiple variables, making it complex and challenging. Therefore, we ﬁrst decouple the original problem into two sub-problems and convert the nonlinear rate constraints into linear constraints. Then, we adopt the iterative sub-gradient method to obtain an efﬁcient solution. For comparison, we also present a conventional NOMA cooperative V2X network without AmBC. Simulation results show the beneﬁts of our proposed AmBC enhanced NOMA cooperative V2X network in terms of total achievable energy efﬁciency. —Beyond 5G, ambient backscatter communica- tions (AmBC), non-orthogonal multiple access (NOMA), vehicle-to-everything (V2X), imperfect channel state information (CSI).

/pdf/energy-efficiency-optimization-for-backscatter-enhanced-noma-2hgu10o2.pdf

Energy Efficiency Optimization for Backscatter Enhanced NOMA Cooperative V2X Communications Under Imperfect CSI

The Terahertz (THz) band (0.3-3.0 THz), spans a great portion of the Radio Frequency (RF) spectrum that is mostly unoccupied and unregulated. It is a potential candidate for application in Sixth-Generation (6G) wireless networks, as it has the capabilities of satisfying the high data rate and capacity requirements of future wireless communication systems. Profound knowledge of the propagation channel is crucial in communication systems design, which nonetheless is still at its infancy, as channel modeling at THz frequencies has been mostly limited to characterizing fixed Point-to-Point (PtP) scenarios up to 300 GHz. Provided the technology matures enough and models adapt to the distinctive characteristics of the THz wave, future wireless communication systems will enable a plethora of new use cases and applications to be realized, in addition to delivering higher spectral efficiencies that would ultimately enhance the Quality-of-Service (QoS) to the end user. In this paper, we provide an insight into THz channel propagation characteristics, measurement capabilities, and modeling techniques for 6G communication applications, along with guidelines and recommendations that will aid future characterization efforts in the THz band. We survey the most recent and important measurement campaigns and modeling efforts found in literature, based on the use cases and system requirements identified. Finally, we discuss the challenges and limitations of measurement and modeling at such high frequencies and contemplate the future research outlook toward realizing the 6G vision.

https://ieeexplore.ieee.org/ielx7/9739/5451756/09882323.pdf

Terahertz Channel Propagation Phenomena, Measurement Techniques and Modeling for 6G Wireless Communication Applications: A Survey, Open Challenges and Future Research Directions

Vision and research directions of 6G technologies and applications

https://hal.archives-ouvertes.fr/hal-03326474/document

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Jianhua He

Papers

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities