The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle’s perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.

Networking and Communications in Autonomous Driving: A Survey

Nowadays, many disruptive Internet-of-Things (IoT) applications emerge, such as augmented/virtual reality online games, autonomous driving, and smart everything, which are massive in number, data intensive, computation intensive, and delay sensitive. Due to the mismatch between the fifth generation (5G) and the requirements of such massive IoT-enabled applications, there is a need for technological advancements and evolutions for wireless communications and networking toward the sixth-generation (6G) networks. 6G is expected to deliver extended 5G capabilities at a very high level, such as Tbps data rate, sub-ms latency, cm-level localization, and so on, which will play a significant role in supporting massive IoT devices to operate seamlessly with highly diverse service requirements. Motivated by the aforementioned facts, in this article, we present a comprehensive survey on 6G-enabled massive IoT. First, we present the drivers and requirements by summarizing the emerging IoT-enabled applications and the corresponding requirements, along with the limitations of 5G. Second, visions of 6G are provided in terms of core technical requirements, use cases, and trends. Third, a new network architecture provided by 6G to enable massive IoT is introduced, i.e., space–air–ground–underwater/sea networks enhanced by edge computing. Fourth, some breakthrough technologies, such as machine learning and blockchain, in 6G are introduced, where the motivations, applications, and open issues of these technologies for massive IoT are summarized. Finally, a use case of fully autonomous driving is presented to show 6G supports massive IoT.

Enabling Massive IoT Toward 6G: A Comprehensive Survey

Unmanned aerial vehicles (UAVs) have stroke great interested both by the academic community and the industrial community due to their diverse military applications and civilian applications. Furthermore, UAVs are also envisioned to be part of future airspace traffic. The application functions delivery relies on information exchange among UAVs as well as between UAVs and ground stations (GSs), which further closely depends on aeronautical channels. However, there is a paucity of comprehensive surveys on aeronautical channel modeling in line with the specific aeronautical characteristics and scenarios. To fill this gap, this paper focuses on reviewing the air-to-ground (A2G), ground-to-ground (G2G), and air-to-air (A2A) channel measurements and modeling for UAV communications and aeronautical communications under various scenarios. We also provide the design guideline for managing the link budget of UAV communications taking account of link losses and channel fading effects. Moreover, we also analyze the receive/transmit diversity gain and spatial multiplexing gain achieved by multiple-antenna-aided UAV communications. Finally, we discuss the remaining challenge and open issues for the future development of UAV communication channel modeling.

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A Comprehensive Survey on UAV Communication Channel Modeling

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

Multi-UAV-Enabled Load-Balance Mobile-Edge Computing for IoT Networks

Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

Reliable Computation Offloading for Edge-Computing-Enabled Software-Defined IoV

Given the escalating population across the globe, it has become paramount to construct smart cities, aiming for improving the management of urban flows relying on efficient information and communication technologies (ICT). Vehicular sensing networks (VSNs) play a critical role in maintaining the efficient operation of smart cities. Naturally, there are numerous challenges to be solved before the wide-spread introduction of VSNs, including the conception of an accurate topological analysis method and a beneficial cooperation mechanism during the process of city-wide information sharing. Hence, in this article, we construct a VSN-aided smart city model and appraise a range of intelligent applications in terms of both public services and urban flow management. Then, the information source selection algorithm of a complex network and a reinforcement learning based city information sharing mechanism are considered, complemented by a range of open challenges.

/pdf/vehicular-sensing-networks-in-a-smart-city-principles-3egbz5hsop.pdf

Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications

To achieve the goal of “Industrial 4.0,” cellular network with wide coverage has gradually become an intensely important carrier for industrial Internet of Things (IIoT). The fifth generation cellular network is expected to be a unifying network that may connect billions of IIoT devices for the sake of supporting advanced IIoT business. In order to realize wide and seamless information coverage, heterogeneous network architecture becomes a beneficial method, which can also improve the near-ceiling network capacity. In order to guarantee the quality of service (QoS) as well as the fairness of different IIoT devices with limited network resources, the network association in IIoT should be performed in a more intelligent manner. In this article, we propose a distributed $Q$ -learning aided power allocation algorithm for two-layer heterogeneous IIoT networks. Moreover, we discuss the spirit of designing reward functions, followed by four delicately defined reward functions considering both the QoS of femtocell IoT user equipments and macrocell IoT user equipments and their fairness. Also, both fixed and dynamic learning rates and different kinds of multiagent cooperation modes are investigated. Finally, simulation results show the effectiveness and superiority of our proposed $Q$ -learning based power allocation algorithm.

Distributed Q-Learning Aided Heterogeneous Network Association for Energy-Efficient IIoT

The combined MIMO with adaptive modulation and coding (AMC) technology can provide high spectral efficiency and link robustness. While most existing adaptive algorithms focus on physical layer, cross-layer analysis on the queuing behavior of MIMO-AMC systems is necessary, but remains open. In this paper, under the conditions of unsaturated traffic and finite-length buffer, we investigate the queuing characters of two representative categories of MIMO systems, namely the BLAST system and the space-time block coding (STBC) system. We model the service processes of both STBC and BLAST coupled with AMC, which is the most challenging part of the queuing analysis. We observe a new tradeoff between diversity and multiplexing in terms of link layer packet loss rate and queuing delay, based on which we propose a cross-layer design of diversity-multiplexing switching scheme to optimize the QoS of the MIMO-AMC systems.

http://network.ee.tsinghua.edu.cn/papers/ShengZhou-ICC.pdf

Queuing Analysis on MIMO Systems with Adaptive Modulation and Coding

Previous work has shown that the capacity region of the Gaussian MIMO broadcast channels is achieved by dirty paper coding (DPC). However, due to high computation complexity of DPC and infeasibility of perfect channel state information (CSI) at the transmitter in many applications, this paper focuses on a reduced complexity transmission scheme named orthonormal random beamforming (ORBF) [16], which only requires partial CSI feedback at the transmitter. Different from the previous work, we analyze the performance of ORBF with moderate number of users and total transmit power constraint. The analysis results show that ORBF scheme is efficient under low SNR. Then we propose a multi-beam selection (MBS) scheme, which selects only the best subset of all the beams to maximize the sum-rate capacity under low SNR. The simulation results show that the proposed MBS scheme achieves great performance improvement when the SNR is low and the number of users is not very large.

Random Beamforming with Multi-beam Selection for MIMO Broadcast Channels

In this paper, we propose a distributed detection system for hierarchical information acquisition based on autonomous underwater vehicle (AUV) and underwater fixed sensor networks. Different from the previous information collection systems, where the AUV traverses each node to obtain information, we propose a layered network architecture in this work, which is composed of an underwater fixed sensor networks layer and an AUV information acquisition layer. Such information acquisition system does not need to modify the original underlying fixed sensor networks, resulting from its flexible deployability. Additionally, because of the power sensitivity of sensor nodes in underwater fixed sensor networks, an improved algorithm based on classical low energy adaptive clustering hierarchy (Leach) algorithm is proposed in this work. Simulation results validate that the proposed algorithm can effectively improve the life cycle of sensor networks. At the same time, for the AUV information acquisition layer, we propose an angle optimization path planning algorithm based on the ant colony algorithm, which effectively takes the angle and path length as joint optimization objects. Experiments show that introducing the angle optimization jointly not only helps to optimize the AUV rotation angle, but also contributes to improving the convergence of the algorithm.

Kai Zhang

Papers

Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications

Distributed Q-Learning Aided Heterogeneous Network Association for Energy-Efficient IIoT

Queuing Analysis on MIMO Systems with Adaptive Modulation and Coding

Random Beamforming with Multi-beam Selection for MIMO Broadcast Channels

Distributed Hierarchical Information Acquisition Systems Based on AUV Enabled Sensor Networks