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, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, 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 (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

This paper is focused on code-domain non-orthogonal multiple access (CD-NOMA), which is an emerging paradigm to support massive connectivity for future machine-type wireless networks. We take a comparative approach to study two types of overloaded CD-NOMA, i.e., sparse code multiple access (SCMA) and dense code multiple access (DCMA), which are distinctive from each other in terms of their codebooks having sparsity or not. By analysing their individual diversity orders (DO) in Rayleigh fading channels, it is found that DCMA can be designed with the aid of generalized sphere decoder (i.e., a nonlinear multiuser detector) to enjoy full DO which is equal to the maximum number of resource nodes in the system. This is in contrast to SCMA whose error rate suffers from limited DO equal to the codebook sparsity (i.e., the effective number of resource nodes occupied by each user). We conduct theoretical analysis for the codebook design criteria and propose to use generalized sphere decoder for DCMA detection. We numerically evaluate two types of multiple access schemes under “ 4×6” (i.e., six users communicate over four subcarriers) and “ 5×10” NOMA settings and reveal that DCMA gives rise to significantly improved error rate performance in Rayleigh fading channels, whilst having decoding complexity comparable to that of SCMA.

This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum band for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an $l$ -norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches.

This paper studies the affine frequency division multiplexing (AFDM)-empowered sparse code multiple access (SCMA) system, referred to as AFDM-SCMA, for supporting massive connectivity in high-mobility environments. First, by placing the sparse codewords on the AFDM chirp subcarriers, the input-output (I/O) relation of AFDM-SCMA systems is presented. Next, we delve into the generalized receiver design, chirp rate selection, and error rate performance of the proposed AFDM-SCMA The proposed AFDM-SCMA is shown to provide a general framework and subsume the existing OFDM-SCMA as a special case. Third, for efficient transceiver design, we further propose a class of sparse codebooks for simplifying the I/O relation, referred to as I/O relation-inspired codebook design in this paper. Building upon these codebooks, we propose a novel iterative detection and decoding scheme with linear minimum mean square error (LMMSE) estimator for both downlink and uplink channels based on orthogonal approximate message passing principles. Our numerical results demonstrate the superiority of the proposed AFDM-SCMA systems over OFDM-SCMA systems in terms of the error rate performance. We show that the proposed receiver can significantly enhance the error rate performance while reducing the detection complexity.

Non-orthogonal Multiple Access (NOMA) has been envisioned as one of the key enabling techniques to fulfill the requirements of future wireless networks. The primary benefit of NOMA is higher spectrum efficiency compared to Orthogonal Multiple Access (OMA). This paper presents an error rate comparison of two distinct NOMA schemes, i.e., power domain NOMA (PD-NOMA) and Sparse Code Multiple Access (SCMA). In a typical PD-NOMA system, successive interference cancellation (SIC) is utilized at the receiver, which however may lead to error propagation. In comparison, message passing decoding is employed in SCMA. To attain the best error rate performance of PD-NOMA, we optimize the power allocation with the aid of pairwise error probability and then carry out the decoding using generalized sphere decoder (GSD). Our extensive simulation results show that SCMA system with “ $5\times 10$ ” setting (i.e., ten users communicate over five subcarriers, each active over two subcarriers) achieves better uncoded BER and coded BER performance than both typical “ $1\times 2$ ” and “ $2\times 4$ ” PD-NOMA systems in uplink Rayleigh fading channel. Finally, the impacts of channel estimation error on SCMA, SIC and GSD based PD-NOMA and the complexity of multiuser detection schemes are also discussed.