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The accuracy of artificial intelligence (AI) models is crucial for connected and autonomous vehicles (CAVs). However, in reality, model training under less frequent weather faces the problem of insufficient sampling. Also, in the real world, weather, sunlight, etc., can only change with the speed of the real-time clock, so the traditional sampling process is very slow. Moreover, currently, collective learning, which can make up the limited experience and computing power of a single vehicle, is always introduced to cases where the data from participants have the same structure, wasting massive heterogeneous data from vehicles of different brands. Therefore, in this paper, we propose a quantum collective learning and many-to-many matching game-based scheme in the metaverse for CAVs. The environment is simulated in the metaverse, which has its own time clock system, thereby expanding sample size and speeding up the sampling process. And we quantify the quality of intelligence in collective learning from the perspective of feature diversity. It is the cornerstone of collective learning between heterogeneous vehicles, facilitating maximum utilization of data with different structures. Then, we formulate the distributed vehicles selection problem as a many-to-many matching game and use Gale–Shapely algorithm to solve it. Also, we formulate the spectrum resource allocation problem as a discrete Markov decision process (MDP) and adopt a quantum-inspired reinforcement learning (QRL) algorithm to find the optimal policy to achieve the high revenue of the system. In simulations, the performance of the proposed scheme is compared with existing methods. [ABSTRACT FROM AUTHOR]

The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality. [ABSTRACT FROM AUTHOR]

The vigorous developments of the Internet of Things make it possible to extend its computing and storage capabilities to computing tasks in the aerial system with the collaboration of cloud and edge, especially for artificial intelligence (AI) tasks based on deep learning (DL). Collecting a large amount of image/video data, unmanned aerial vehicles (UAVs) can only hand over intelligent analysis tasks to the back-end mobile edge computing (MEC) server due to their limited storage and computing capabilities. How to efficiently transmit the most correlated information for the AI model is a challenging topic. Inspired by task-oriented communication in recent years, we propose a new aerial image transmission paradigm for the scene classification task. A lightweight model is developed on the front-end UAV for semantic block transmission with the perception of images and channel states. To achieve the tradeoff between transmission latency and classification accuracy, deep reinforcement learning (DRL) is applied to explore the semantic blocks which have the greatest contribution to the back-end classifier under various channel states. Experimental results show that the proposed method can significantly improve classification accuracy by more than 4% under the same conditions, compared to other semantic saliency learning methods. [ABSTRACT FROM AUTHOR]

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined. [ABSTRACT FROM AUTHOR]

Energy-saving techniques are vital for the battery-powered sensor devices (SDs), which affect their lifetime. In this paper, we propose an air-and-ground cooperative wireless sensor network (AGWSN), wherein several UAVs are deployed as aerial access points (AAPs) to assist the terrestrial access point (TAP) for data collecting. The positions of the AAPs can be modified to approach the cell-edge SDs, therefore reducing the energy of the SDs expended in uploading data. To fully exploit the potential of the AGWSN, we formulate a joint AAP position optimization, channel allocation, and power control problem to minimize the total power consumption of all SDs subject to their decoding threshold. To solve the formulated problem, we first analyze the optimal user pairing rule in each cell and based on the rule propose a maximum-weighted-independent-set inspired algorithm for the AAP position optimization. Then, we remodel the channel allocation problem as an interference minimization problem and devise a K-CUT based algorithm to solve it. We further propose a low-complex iterative algorithm to obtain the optimal transmission power for each SD. The performance of the proposed algorithms is evaluated via theoretical analysis and numerical simulation. Simulation results indicate that if the intracell and intercell interference are not well coordinated, the superiorities of the AGWSN cannot be developed, and its performance is even worse than the traditional terrestrial network (TTN). Cooperated with our algorithms, the AGWSN significantly outperforms the TTN in terms of total power consumption and probability of successful decoding. [ABSTRACT FROM AUTHOR]

This paper investigates a joint resource allocation for ultra-reliable and low-latency radio access networks (URLLRANs) with edge computing. Compared with conventional networks, URLLRANs have more restrictive latency and reliability requirements, and always feature short packet communications. It is a challenging work to provide edge computing services in URLLRANs, since the processing and transmission delay as well as packet loss during computation and communications should all be taken into considerations. Along these lines, to specify the trade-off between latency and reliability, this paper defines computation rates and transmission rates for short packets. Different from the existing work, the proposal takes effective information as well as energy consumption as performance metrics based on the definition. The packet request rates, computation latency, service rates, communication power, blocklength, and transmission information amounts are jointly optimized to reduce energy consumption and meanwhile generate more effective information for both the computation system and the communication system. To solve the NP-hard problem, the locally optimal solution and global optimal solution are both derived. Simulation results validate the performance advantage of the proposal and also indicate that the locally optimal solution can greatly reduce the computation complexity with only a small performance loss when compared with the global optimal solution. [ABSTRACT FROM AUTHOR]

We are seeing a growth in the number of connected vehicles in Vehicular Ad-hoc Networks (VANETs) to achieve the goal of Intelligent Transportation System (ITS). This is leading to a connected vehicular network scenario with vehicles continuously broadcasting data to other vehicles on the road and the roadside network infrastructure. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. Existing works provide solutions addressing specific anomalies in the network only. However, since there can be a multitude of anomalies possible in the network, there is a need for better anomaly detection frameworks that can address this unprecedented scenario. In this paper, we propose an anomaly detection framework for VANETs based on deep neural networks (DNNs) using a sequence reconstruction and thresholding algorithm. In this framework, the DNN architectures are deployed on the roadside units (RSUs) which receive the broadcast vehicular data and run anomaly detection tasks to classify a particular message sequence as anomalous or genuine. Multiple DNN architectures are implemented in this experiment and their performance is compared using key evaluation metrics. Performance comparison of the proposed framework is also drawn against the prior work in this area. Our best performing deep learning-based scheme detects anomalous sequences with an accuracy of 98%, a great improvement over the set benchmark. [ABSTRACT FROM AUTHOR]

Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques. [ABSTRACT FROM AUTHOR]

Driven by the extensively emerging applications requiring big data processing, a series of heterogeneous network architectures have been proposed to meet user experience requirements. Among them, the concept of fog computing facilitates the effective integration and utilization of ubiquitous computing resources. In fog computing scenarios, willingness and service billing issues become significant to computing resource sharing and transactions. In this article, the recently developed blockchain technology characterized by successfully enabling consensus in an untrustworthy environment is introduced. Based on the blockchain technology, we propose a new architecture for resource sharing and transactions in fog computing networks, named Blockchain-Enabled Resource Sharing and Transactions in Fog Computing (B-ReST). The physical architecture, functional architecture, and workflow in B-ReST are defined. We also discuss the key technologies in B-ReST such as the smart contracts, the consensus mechanism and the requester and provider matching (RPM). The wireless characteristics of fog computing and blockchain technology are closely combined to make full and efficient use of ubiquitous computing resources. To prove the feasibility of the proposed architecture, the RPM problem is solved by a deep reinforcement learning (DRL) based method. Simulation results show the advantages of B-ReST to realize resource sharing and transactions, and the performance of B-ReST based on the DRL method has been enhanced. Challenges and future research directions are summarized as well. [ABSTRACT FROM AUTHOR]

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications. Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy. By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data. First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location. Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server. We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs). In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales. Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes. [ABSTRACT FROM AUTHOR]

The next generation wireless network is expected to connect billions of nodes, which brings up the bottleneck on the communication speed for distributed data fusion. To overcome this challenge, computation over multiple access channel (CoMAC) was recently developed to compute the desired functions with a summation structure (e.g., mean, norm, etc.) by using the superposition property of wireless channels. This work aims to maximize the achievable function rate of reliable CoMAC in wireless networks. More specifically, considering channel fading and transceiver design, we derive the achievable function rate adopting the quantization and the nested lattice coding, which is determined by the number of nodes, the maximum value of messages and the quantization error threshold. Based on the derived result, the transceiver design is optimized to maximize the achievable function rate of the network. We first study a single cluster network without inter-cluster interference (ICI). Then, a multi-cluster network is further analyzed in which the clusters work in the same channel with ICI. In order to avoid the global channel state information (CSI) aggregation during the optimization, a low-complexity signaling procedure irrelevant with the number of nodes is proposed utilizing the channel reciprocity and the defined effective CSI. [ABSTRACT FROM AUTHOR]

Based on recent advances in MEC and knowledge transfer in artificial intelligence, we propose a novel framework named ISVN, in which the intelligence of different MEC servers can be shared to improve performance. Specifically, we present the main techniques in the ISVN framework, including aggregation and representation for context features, relationship mining and reasoning, and knowledge transfer among MEC servers. The results of object detection experiments with the proposed ISVN framework are presented. By taking advantage of MEC and knowledge transfer, the processing speed and accuracy of object detection can be significantly improved in different scenarios of vehicular networks. [ABSTRACT FROM AUTHOR]

The accuracy of the ML model is essential for the further development of AI-enabled CAVs. With the increasing complexity of on-board sensor systems, the large amount of raw data available for learning can however cause big communication burdens and data security issues. To alleviate the communication cost yet improve the accuracy of machine learning with preserved data privacy is an important issue to address in CAVs. In this article, we survey the existing literature toward efficient and secured learning in a dynamic wireless environment. In particular, a BCL framework for AI-enabled CAVs is presented. The framework enables distributed CAVs to train ML models locally and upload to blockchain network to overall utilize the "collective intelligence" of CAVs while avoiding large amounts of data transmission. Blockchain is then applied to protect the distributed learned models. We evaluate the performance of the presented framework by simulations and discuss a range of open research issues that need to be addressed in the future. [ABSTRACT FROM AUTHOR]

Realizing the ultra-low latency and high-accuracy solutions for rear-end collision is still challenging, especially under the condition in which many uncertainties exist. This paper proposes an artificial intelligence-based warning algorithm for rear-end collision avoidance. Three key issues are addressed by applying the neural network approach, including noises in positioning, inaccurate risk assessment, and enhanced comfort level of passengers. First, to filter the noises in positioning, wireless vehicular communications are leveraged; accurate relative lane positioning can be achieved to justify when two vehicles are in the same lane. Second, an online neural network model is developed to assess the risk of collisions in real time while driving. The algorithm can converge fast to a globally optimal solution and adapt to different traffic environments. Third, to maximize the comfort of passengers during the braking process, a graded warning strategy is developed at the prerequisite of guaranteed safety. With the above schemes sewed in to one framework, our proposal can achieve rear-end warning with reduced missing alarm rate, accurate risk assessment and enhanced comfort to passengers. The extensive simulations validate the effectiveness and accuracy of our proposal in terms of relative lane positioning, risk assessment, and collision avoidance. [ABSTRACT FROM AUTHOR]

In vehicular communication systems, due to limited computation power of vehicles, low-cost sampling technologies, such as compressed video sensing (CVS), have been proposed. However, after one-time coarse compressive sampling, it is difficult to obtain accurate temporal correlation between video frames. To address this issue, this paper proposes a correlation analysis model in the measurement domain by combining CVS and convolutional neural network (CNN), which is termed as “CVS–CNN.” Specifically, to analyze the temporal correlation of video frames in the measurement domain, we use CNN as a substitute for the pseudo-inverse transform of the measurement matrix and establish the correlation between the measurements of the block to be estimated and those of the neighboring non-overlapping blocks. The network parameters are trained to minimize the loss between the predicted and true measurements, and are assigned to the non-overlapping image blocks. The various experimental results demonstrate that the proposed CVS–CNN method significantly outperforms similar methods of analyzing the video frame correlation in accuracy, process speed, and robustness. This result indicates that the proposed method can be used in many potential applications, such as self-driving systems and in-car warning systems. [ABSTRACT FROM AUTHOR]

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS. [ABSTRACT FROM AUTHOR]

To accommodate the dramatically increasing traffic demand, some of the intermediate nodes within HetNets should be endowed with storage and computation capabilities for facilitating pervasive computation service and massive content delivery. ICN and MEC are recognized as two promising techniques, with which content can be cached and computed in close proximity to mobile devices. These advanced techniques enable Het- Nets to accommodate and support more mobile users, but also bring challenges of architecture deployment and resource deployment. Along these lines, the fist part of this article offers a novel joint architecture of communications, caching, and computing for HetNets based on wireless network virtualization with software defined networking, where ICN and MEC support each other to promote network performance and guarantee different types of business demands. Based on the integrated framework, we further present two different self-enforcing mechanisms (i.e., a caching-assisted MEC approach and a computing-assisted ICN approach) to achieve mutual promotion between caching and computing. Against this background, the second part of this article offers virtual multi-resource deployment for both uplink and downlink scenarios to evaluate a three-way trade-off among communication, caching, and computing revenues. The results demonstrate our hypothesis on the utilization of the mutual promotion between ICN and MEC to improve the overall performance of future wireless systems. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue including traffic features from wireless communication records, urban rail transit's wireless communication system and signal transmission diversity.

The explosively growing demands for mobile traffic services bring both challenges and opportunities to wireless networks. Wireless network virtualization is proposed as the main evolution path toward the forthcoming fifth generation (5G) cellular networks. In this paper, we propose a software defined and virtualized (SDV) wireless network architecture for enabling multi-flow transmission with multiple infrastructure providers (InPs) and multiple mobile virtual network operators (MVNOs). In order to ensure the heterogeneity, we formulate the virtual resource allocation problem with diverse QoS requirements as a social welfare maximization problem with distance-related transaction cost. Due to hidden information of InPs and MVNOs for the auctioneer, we introduce a shadow price for ensuring desirable economic properties and total welfare for the system. Simulations are conducted with different system configurations to show the effectiveness and the energy efficiency performance of the proposed SDV wireless network framework and iterative double auction mechanism. [ABSTRACT FROM PUBLISHER]

Although some excellent works have been done on networking, caching and computing, these three important areas have traditionally been addressed separately in the literature. In this article, we propose a novel framework that jointly considers networking, caching and computing techniques in order to improve end-to-end system performance. This integrated framework can enable dynamic orchestration of networking, caching and computing resources to meet the requirements of different applications. We define and develop the key components of this framework: the data plane, the control plane, and the management plane. The data plane consists of the devices that are responsible for networking, caching and computing operations. The control plane has a logically centralized controller to guide these operations. The management plane enables not only traditional applications, such as traffic engineering, but also new applications, such as content distribution and big data analytics. Simulation results are presented to show the effectiveness of the proposed framework. In addition, we discuss a number of challenges of implementing the proposed framework of software defined networking, caching and computing. [ABSTRACT FROM AUTHOR]

In existing urban rail transit systems, the train-ground communication system for different subsystems is deployed independently. Investing and constructing the communication infrastructures repeatedly not only wastes substantial social resources, but it also is difficult to maintain all these infrastructures. In this paper, we propose an integrated train-ground communication system based on wireless network virtualization for urban rail transit systems. In order to improve the communication-based train control (CBTC) subsystem performance during handoff, we propose a novel handoff scheme to support handoff between virtual networks. The application-layer quality-of-service (QoS) parameters of the CBTC, passenger information system, and closed circuit television subsystems are used as the performance measures in the handoff design. We then formulate the QoS optimization problem in the proposed integrated train-ground communication system as an approximate dynamic programming (ADP) problem. The extensive simulation results show that the proposed integrated train-ground communication system QoS can be improved substantially with our ADP-based optimization model. [ABSTRACT FROM PUBLISHER]

Communication-based train control (CBTC) systems use wireless local area networks for information transmission between trains and wayside equipment. Since inevitable packet delay and drop are introduced in train–wayside communications, information uncertainties in trains' states will lead to unplanned traction/braking demands, as well as waste in electrical energy. Moreover, with the introduction of regenerative braking technology, power grids in CBTC systems are evolving to smart grids, and cost-aware power management should be employed to reduce the total financial cost of consumed electrical energy. In this paper, a cognitive control method for CBTC systems with smart grids is presented to enhance both train operation performance and cost efficiency. We formulate a cognitive control system model for CBTC systems. The information gap in cognitive control is calculated to analyze how the train–wayside communications affect the operation of trains. The Q-learning algorithm is used in the proposed cognitive control method, and a joint objective function composed of the information gap and the total financial cost is applied to generate optimal policy. The medium-access control layer retry-limit adaption and traction strategy selection are adopted as cognitive actions. Extensive simulation results show that the cost efficiency and train operation performance of CBTC systems are substantially improved using our proposed cognitive control method. [ABSTRACT FROM PUBLISHER]

The article discusses challenges of proposing a novel framework which jointly considers networking, caching, and computing techniques in a systematic way to support energy-efficient information retrieval and computing services in green wireless networks. It also highlights advances in networking, caching and computing for development of green wireless networks.

Designing a train control system over wireless train-wayside communications is a challenging task due to the packet delay and loss caused by unreliable wireless communications. In this paper, we study the performance optimization issues in communication-based train control (CBTC) systems with both packet delay and loss introduced by random transmission errors and frequent handoffs in train-wayside communications. A networked control system (NCS) model is formulated for multitrain CBTC systems with packet delay and loss. Then, we propose a novel train control scheme to enhance the quality of service (QoS) of CBTC systems. In the proposed scheme, medium-access control layer retry limit adaption is used to minimize the energy consumption, and guidance trajectory update is used to mitigate the trip time tracking errors. Extensive simulation results show that the QoS of CBTC systems can be substantially improved in the proposed scheme. The energy consumption and trip time error can be reduced in both static and time-varying wireless environments. [ABSTRACT FROM PUBLISHER]

Communication-based train control (CBTC) is being rapidly adopted in urban rail transit systems, as it can significantly enhance railway network efficiency, safety, and capacity. Since CBTC systems are mostly deployed in underground tunnels and trains move at high speeds, building a train–ground wireless communication system for CBTC is a challenging task. Modeling the tunnel channels is very important in designing the wireless networks and evaluating the performance of CBTC systems. Most existing works on channel modeling do not consider the unique characteristics of CBTC systems, such as high mobility speed, deterministic moving direction, and accurate train-location information. In this paper, we develop a finite-state Markov channel (FSMC) model for tunnel channels in CBTC systems. The proposed FSMC model is based on real field CBTC channel measurements obtained from a business-operating subway line. Unlike most existing channel models, which are not related to specific locations, the proposed FSMC channel model takes train locations into account to have a more accurate channel model. The distance between the transmitter and the receiver is divided into intervals and an FSMC model is applied in each interval. The accuracy of the proposed FSMC model is illustrated by the simulation results generated from the model and the real field measurement results. [ABSTRACT FROM PUBLISHER]

A communication-based train control (CBTC) system is an automated train control system that uses bidirectional train–ground communications to ensure the safe operation of rail vehicles. CBTC systems have stringent requirements for communication availability and latency. Due to unreliable wireless communications and frequent handoffs, existing CBTC systems can severely affect train control performance, train operation efficiency, and the utility of railways. In this paper, we use recent advances in coordinated multipoint transmission and reception (CoMP) to enhance the train control performance of CBTC systems. With CoMP, a train can communicate with a cluster of base stations (BSs) simultaneously, which is different from the current CBTC systems, where a train can only communicate with a single BS at any given time. In addition, unlike the existing works on CoMP, in this paper, the linear quadratic cost for the train control performance in CBTC systems is considered the performance measure. We jointly consider the BS cluster selection and handoff decision issues in CBTC systems. Moreover, in order to mitigate the impacts of communication latency on train control performance, we propose an optimal guidance trajectory calculation scheme in the train control procedure that takes full consideration of the tracking error caused by handoff latency. Simulation results show that train control performance can be substantially improved in our proposed CBTC system with CoMP. [ABSTRACT FROM PUBLISHER]

Communication-based train control (CBTC) systems use wireless local area networks (WLANs) to transmit train status and control commands. Since WLANs are not originally designed for applications with high mobility, random transmission delays and packet drops are inevitable, which could result in unnecessary traction, brakes or even emergency brakes of trains, loss of line capacity, and passenger satisfaction. In this paper, we study the packet drops introduced by random transmission errors and handovers in CBTC systems, analyze the impact of random packet drops on the stability and performances of CBTC systems, and propose two novel schemes to improve the performances of CBTC systems. Unlike the existing works that only consider a single train and study the communication issues and train control issues separately, we model the system to control a group of trains as a networked control system (NCS) with packet drops in transmissions. Extensive field test and simulation results are presented. We show that our proposed schemes can provide less energy consumption, better riding comfortability, and higher line capacity compared with the existing scheme. [ABSTRACT FROM PUBLISHER]

A dynamic power allocation scheme is proposed to maximise the energy efficiency (EE) for multi‐antenna transmissions when both the imperfect channel estimation and feedback are considered. The lower bounded ergodic EE is given by the temporal correlation analysis from the Kalman estimation and the differential codebook‐based feedback of the Gauss–Markov channel, based on which both the powers of data and training are adaptively optimised. Simulations show that the proposed dynamic power allocation scheme outperforms the traditional power allocation schemes. [ABSTRACT FROM AUTHOR]