Vehicular communications face a tremendous challenge in guaranteeing low latency for safety-critical information exchange due to fast varying channels caused by high mobility. Focusing on the tail behavior of random latency experienced by packets, latency violation probability (LVP) deserves particular attention. Based on only large-scale channel information, this paper performs spectrum and power allocation to maximize the sum ergodic capacity of vehicle-to-infrastructure (V2I) links while guaranteeing the LVP for vehicle-to-vehicle (V2V) links. Using the effective capacity theory, we explicitly express the latency constraint with introduced latency exponents. Then, the resource allocation problem is decomposed into a pure power allocation subproblem and a pure spectrum allocation subproblem, both of which can be solved with global optimum in polynomial time. Simulation results show that the effective capacity model can accurately characterize the LVP. In addition, the effectiveness of the proposed algorithm is demonstrated from the perspectives of the capacity of the V2I links and the latency of the V2V links.

Proximity-based communications have been considered as a promising candidate for supporting vehicular communications. However, the high mobility in vehicular communications makes it hard to obtain accurate fast varying channel information, which poses significant challenges on meeting the requirements of high reliability and low latency. Based only on slowly varying large-scale fading channel information, this paper performs a reliability and latency aware resource allocation, which maximizes the throughput of vehicular-to-network (V2N) links while satisfying reliability and latency requirements of vehicular-to-vehicular (V2V) links. First, we obtain steady-state reliability and latency expressions based on queueing analysis for each possible spectrum reusing pair of a V2N link and a V2V link. Then, an optimal power allocation algorithm is developed for each possible spectrum reusing pair. Afterwards, the spectrum reusing pattern is optimized by addressing a polynomial time solvable bipartite matching problem. Simulation results demonstrate the accuracy of the proposed queueing analysis and confirm the effectiveness of the proposed resource allocation comparing with available strategies.

Indoor positioning system (IPS) plays an important role in the applications of Internet of Things (IoT), including intelligent hospital, logistics, and warehousing. Ultrawideband (UWB)-based IPS has shown superior performance due to its strong multipath resistance and high temporal resolution. However, the non-line-of-sight (NLOS) situations noticeably degrade both the positioning accuracy and the communication reliability. To address this issue, we first propose a support vector machine (SVM)-based channel detection method to distinguish the line-of-sight (LOS) and NLOS conditions. Then, one base station (BS)-based distance and angle positioning algorithm with extended Kalman filter (DAPA-EKF) in NLOS environment is proposed. For the LOS environment, least squares (LSs) with EKF processing of acceleration (LS-AEKF) and velocity (LS-VEKF) are developed. To further improve the performance, the combination of time difference of arrival (TDOA) and KF in LOS environment is proposed. Simulation results show that the positioning accuracy of the proposed algorithm is improved in various environments. Finally, validated using more than 1000 testing positions, the positioning accuracy of LS-AEKF is 73.8%–74.1% higher than that of LS-VEKF among the two proposed algorithms in terms of three or four BSs metrics.

In vehicular communications, safety-critical information exchange has stringent reliability and latency requirements. However, lack of accurate instantaneous channel state information due to high mobility poses a great challenge to meet these requirements and the situation gets more complicated when packet retransmission is considered. Based on only large-scale fading channel information, we perform spectrum and power allocation to maximize the sum ergodic capacity of vehicular-to-infrastructure (V2I) links while guaranteeing reliability and latency requirements of vehicular-to-vehicular (V2V) links. First, for each possible V2I-V2V spectrum reusing pair, we analyze the queueing system for the V2V link and derive the packet loss probability and average packet sojourn time under fixed power allocation. Then, an optimal power allocation is derived for each possible pair. Afterwards, we optimize the spectrum reusing pattern by addressing a polynomial time solvable bipartite matching problem. Simulation results confirm the accuracy of the proposed queueing analysis. Moreover, the proposed resource allocation strategy can improve the ergodic capacity of the V2I links under the precondition of guaranteeing the V2V links' requirements on reliability and latency.

Autonomous driving and intelligent transportation have posed stringent requirements on different performance metrics of vehicle-to-everything (V2X) communications depending on the wireless service being carried. Such coexisting heterogeneous performance requirements bring about increasing challenge in managing modern vehicular networks. In this article, we categorize wireless vehicular traffic into two kinds, namely, status update service and data communication service, where in safety-related applications the former is concerned with age of information (AoI) while the latter cares about latency and reliability in general. Afterwards, we uncover the relationship among AoI, latency, and reliability, and propose a novel graphical packet delivery model that is powerful for analyzing AoI performance. To unify the AoI, latency, and reliability management for different services, we further develop an intelligent resource allocation framework validated by a quantitative case study. Finally, we explore key challenges and opportunities to address the coexistence of status update and data communication services in intelligent vehicular networks.

This paper investigates a new analog beamforming architecture for massive multiple-input multiple-output (MIMO) systems, where each of the multiple transmit antennas is switched to be on or off to form a beam according to the channel state information at transmitters. This on-off analog beamforming (OABF) scheme has the advantages in terms of both hardware complexities and algorithmic complexities. OABF can completely remove the high-cost, power-consuming and bulky analog phase-shifters that are extensively employed by traditional analog beamforming schemes; it only requires the deployment of low-cost analog switches that are easy to implement. Moreover, we show that the beams formed by such simple antenna on-off switch operations can achieve rather good performances with low-complexity beamforming algorithms. Specifically, we first propose two optimal signal-to-noise ratio maximization algorithms to determine the on-off state of each switch under the per-antenna power constraint and the total power constraint, respectively. After that, we theoretically prove that OABF can achieve the full diversity gain and the full array gain with complexities up to a polynomial order. Numerical results are consistent with our theoretical analysis. We believe that the simple structure of OABF makes massive MIMO much easier to implement in real systems.