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Register a new userOn the Reliability and Concurrent Unicast Transmission Node Control of 5G NR-V2X Networks
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Added by
Wenpeng Wu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In vehicle-to-everything (V2X) communications, reliability is one of the most important performance metrics in safety-critical applications such as advanced driving, remote driving, and vehicle platooning. In this paper, the link reliability of unicast concurrent transmission in mode 1 (centralized mode) of 5G New Radio based V2X (NR-V2X) is analyzed. The closed-form expression of link reliability for concurrent unicast transmission is firstly derived for a highway scenario under a given interference distance distribution. On this basis, according to the macroscopic configuration of the system, a method to control the number of concurrent transmission nodes is proposed, including the communication range, message packet size, and the number of lanes, etc. The results indicate that the proposed method can maximize the system load on the premise of satisfying the link reliability requirements.
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The future 5G systems are getting closer to be a reality. It is envisioned, indeed, that the roll-out of first 5G network will happen around end of 2018 and beginning of 2019. However, there are still a number of issues and problems that have to be faces and new solutions and methods are needed to solve them. Along these lines, the effects that beamforming and antenna configurations may have on the mobility in 5G New Radio (NR) is still unclear. In fact, with the use of directive antennas and high frequencies (e.g., above 10 GHz), in order to meet the stringent requirements of 5G (e.g., support of 500km/h) it is crucial to understand how the envisioned 5G NR antenna configurations may impact mobility (and thus handovers). In this article, first we will briefly survey mobility enhancements and solution currently under discussion in 3GPP Release 15. In particular, we focus our analysis on the physical layer signals involved in the measurement reporting and the new radio measurement model used in 5G NR to filter the multiple beams typical of directive antenna with a large number of antenna elements. Finally, the critical aspect of mobility identified in the previous sections will be analyzed in more details through the obtained results of an extensive system-level evaluation analysis.
The ever-increasing demand for intelligent, automated, and connected mobility solutions pushes for the development of an innovative sixth Generation (6G) of cellular networks. A radical transformation on the physical layer of vehicular communications is planned, with a paradigm shift towards beam-based millimeter Waves or sub-Terahertz communications, which require precise beam pointing for guaranteeing the communication link, especially in high mobility. A key design aspect is a fast and proactive Initial Access (IA) algorithm to select the optimal beam to be used. In this work, we investigate alternative IA techniques to fasten the current fifth-generation (5G) standard, targeting an efficient 6G design. First, we discuss cooperative position-based schemes that rely on the position information. Then, motivated by the intuition of a non-uniform distribution of the communication directions due to road topology constraints, we design two Probabilistic Codebook (PCB) techniques of prioritized beams. In the first one, the PCBs are built leveraging past collected traffic information, while in the second one, we use the Hough Transform over the digital map to extract dominant road directions. We also show that the information coming from the angular probability distribution allows designing non-uniform codebook quantization, reducing the degradation of the performances compared to uniform one. Numerical simulation on realistic scenarios shows that PCBs-based beam selection outperforms the 5G standard in terms of the number of IA trials, with a performance comparable to position-based methods, without requiring the signaling of sensitive information.
Limited bandwidth resources and higher energy efficiency requirements motivate incorporating multicast and broadcast transmission into the next-generation cellular network architectures, particularly for multimedia streaming applications. Layered division multiplexing (LDM), a form of NOMA, can potentially improve unicast throughput and broadcast coverage with respect to traditional orthogonal frequency division multiplexing (FDM) or time division multiplexing (TDM), by simultaneously using the same frequency and time resources for multiple unicast or broadcast transmissions. In this paper, the performance of LDM-based unicast and broadcast transmission in a cellular network is studied by assuming a single frequency network (SFN) operation for the broadcast layer, while allowing arbitrarily clustered cooperation among the base stations (BSs) for the transmission of unicast data streams. Beamforming and power allocation between unicast and broadcast layers, the so-called injection level in the LDM literature, are optimized with the aim of minimizing the sum-power under constraints on the user-specific unicast rates and on the common broadcast rate. The effects of imperfect channel coding and imperfect CSI are also studied to gain insights into robust implementation in practical systems. The non-convex optimization problem is tackled by means of successive convex approximation (SCA) techniques. Performance upper bounds are also presented by means of the $rm{S}$-procedure followed by semidefinite relaxation (SDR). Finally, a dual decomposition-based solution is proposed to facilitate an efficient distributed implementation of LDM where the optimal unicast beamforming vectors can be obtained locally by the cooperating BSs. Numerical results are presented, which show the tightness of the proposed bounds and hence the near-optimality of the proposed solutions.
The capability to achieve high-precision positioning accuracy has been considered as one of the most critical requirements for vehicle-to-everything (V2X) services in the fifth-generation (5G) cellular networks. The non-line-of-sight (NLOS) connectivity, coverage, reliability requirements, the minimum number of available anchors, and bandwidth limitations are among the main challenges to achieve high accuracy in V2X services. This work provides an overview of the potential solutions to provide the new radio (NR) V2X users (UEs) with high positioning accuracy in the future 3GPP releases. In particular, we propose a novel selective positioning solution to dynamically switch between different positioning technologies to improve the overall positioning accuracy in NR V2X services, taking into account the locations of V2X UEs and the accuracy of the collected measurements. Furthermore, we use high-fidelity system-level simulations to evaluate the performance gains of fusing the positioning measurements from different technologies in NR V2X services. Our numerical results show that the proposed hybridized schemes achieve a positioning error $boldsymbol{leq}$ 3 m with $boldsymbol{approx}$ 76% availability compared to $boldsymbol{approx}$ 55% availability when traditional positioning methods are used. The numerical results also reveal a potential gain of $boldsymbol{approx}$ 56% after leveraging the road-side units (RSUs) to improve the tail of the UEs positioning error distribution, i.e., worst-case scenarios, in NR V2X services.
CA-Polar codes have been selected for all control channel communications in 5G NR, but accurate, computationally feasible decoders are still subject to development. Here we report the performance of a recently proposed class of optimally precise Maximum Likelihood (ML) decoders, GRAND, that can be used with any block-code. As published theoretical results indicate that GRAND is computationally efficient for short-length, high-rate codes and 5G CA-Polar codes are in that class, here we consider GRANDs utility for decoding them. Simulation results indicate that decoding of 5G CA-Polar codes by GRAND, and a simple soft detection variant, is a practical possibility.
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