164 research outputs found
V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks
Benefited from the widely deployed infrastructure, the LTE network has
recently been considered as a promising candidate to support the
vehicle-to-everything (V2X) services. However, with a massive number of devices
accessing the V2X network in the future, the conventional OFDM-based LTE
network faces the congestion issues due to its low efficiency of orthogonal
access, resulting in significant access delay and posing a great challenge
especially to safety-critical applications. The non-orthogonal multiple access
(NOMA) technique has been well recognized as an effective solution for the
future 5G cellular networks to provide broadband communications and massive
connectivity. In this article, we investigate the applicability of NOMA in
supporting cellular V2X services to achieve low latency and high reliability.
Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
to tackle the technical hurdles in designing high spectral efficient scheduling
and resource allocation schemes in the ultra dense topology. We then extend it
to a more general V2X broadcasting system. Other NOMA-based extended V2X
applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin
5G NR-V2X: Towards Connected and Cooperative Autonomous Driving
This paper is concerned with the key features and fundamental technology
components for 5G New Radio (NR) for genuine realization of connected and
cooperative autonomous driving. We discuss the major functionalities of
physical layer, Sidelink features and its resource allocation, architecture
flexibility, security and privacy mechanisms, and precise positioning
techniques with an evolution path from existing cellular vehicle-to-everything
(V2X) technology towards NR-V2X. Moreover, we envisage and highlight the
potential of machine learning for further enhancement of various NR-V2X
services. Lastly, we show how 5G NR can be configured to support advanced V2X
use cases in autonomous driving
NOMA Enhanced 5G Distributed Vehicle to Vehicle Communication for Connected Autonomous Vehicles
Connected autonomous vehicles (CAV) holds great potentials of improving road safety and efficiency. However ultra reliability and low latency vehicle to everything (V2X) communication service is required to fully unleash the potentials of CAV. In this paper we investigate distributed vehicle to vehicle (V2V) for CAV, which supports not only broadcast but also multicast/unicast communications. Power domain non-orthogonal multiple access (NOMA) is applied to deal with the CAV traffic patterns, which are different from those in the traditional connected vehicle applications. With NOMA the signals for long range broadcast with major power and signals for short range neighbors with small power can be superposed in one transmission. With the application of NOMA the channel load can be reduced and communication reliability and latency will be improved. The framework and operation of NOMA enhanced distributed V2V system are designed. Qualitative and quantitative benefits of the proposed scheme are analyzed. Simulation results show that the proposed scheme can achieve a gain of more than 80% on network capacity under the investigated scenarios, with large performance improvement in terms of communication throughput and reliability
6G Cellular Networks and Connected Autonomous Vehicles
With 5G mobile communication systems been commercially rolled out, research discussions on next generation mobile systems, i.e., 6G, have started. On the other hand, vehicular technologies are also evolving rapidly, from connected vehicles as coined by V2X (vehicle to everything) to autonomous vehicles to the combination of the two, i.e., the networks of connected autonomous vehicles (CAV). How fast the evolution of these two areas will go head-in-head is of great importance, which is the focus of this paper. After a brief overview on technological evolution of V2X to CAV and 6G key technologies, this paper explores two complementary research directions, namely, 6G for CAVs versus CAVs for 6G. The former investigates how various 6G key enablers, such as THz, cell free communication and artificial intelligence (AI), can be utilized to provide CAV mission-critical services. The latter discusses how CAVs can facilitate effective deployment and operation of 6G systems. This paper attempts to investigate the interactions between the two technologies to spark more research efforts in these areas
On the Design of Sidelink for Cellular V2X: A Literature Review and Outlook for Future
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
Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization
Rate-Splitting Multiple Access (RSMA) has recently found favour in the
multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of
Channel State Information at the Transmitter (CSIT), while in achieving high
spectral efficiency and providing security guarantees. These benefits are
particularly important in high-velocity vehicular platoons since their high
Doppler affects the estimation accuracy of the CSIT. To tackle this challenge,
we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly
considers platoon control and FEderated Edge Learning (FEEL) in the downlink.
Specifically, the proposed framework is designed for transmitting the unicast
control messages within the IoV platoon, as well as for privacy-preserving
FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given
this sophisticated framework, a multi-objective optimization problem is
formulated to minimize both the latency of the FEEL downlink and the deviation
of the vehicles within the platoon. To efficiently solve this problem, a Block
Coordinate Descent (BCD) framework is developed for decoupling the main
multi-objective problem into two sub-problems. Then, for solving these
non-convex sub-problems, a Successive Convex Approximation (SCA) and Model
Predictive Control (MPC) method is developed for solving the FEEL-based
downlink problem and platoon control problem, respectively. Our simulation
results show that the proposed RSMA-based IoV system outperforms the
conventional systems
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