We are delighted to introduce the fifth part of the Special Issue on intelligent autonomous transportation systems with 6G, which aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported intelligent autonomous transport systems. Forty-two papers were selected for publication in this issue. All the papers were rigorously evaluated according to the standard reviewing process of IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccurate time-of-arrival (ToA) or range estimation. Machine leaning (ML) techniques emerged as potential solutions to mitigate ToA-related errors. However, existing ML-based solutions either employ a set of features representing channel measurements only to a limited extent, or account for only device-specific proprietary methods of ToA estimation. In this paper, we propose a convolutional neural network (CNN) to estimate and mitigate the errors of a variety of ToA estimation methods utilizing channel impulse responses (CIRs). Based on real-world measurements from two independent campaigns, the proposed method yields significant improvements in ranging accuracy (up to 37%) of conventional ToA estimators, often eliminating the need of optimizing the underlying conventional methods.

Digital twins (DTs), defined as the virtual representation of a real-world entity or system, act as a mirror to provide a way to simulate, predict physical behaviors, and possibly control the real-world entity where applicable. Originating in the industry, advances in computing capacity and recent progress in artificial intelligence (AI)-based analytics make DTs attractive to a broader set of use cases including mobile networks.

6G communication networks are expected to become an integral part of the infrastructure needed for developing a smart society in the future. Addressing the challenges on the road towards realizing 6G network requirements in terms of quality of service, user experience, and security, is therefore of utmost importance. The digital twin (DT) technology can potentially improve the efficiency, reliability, and security of 6G networks. Digital twins for mobile networks (DTMNs) are seen as a key factor in harnessing the full benefits of 6G. Using digital twins can help address several problems, including network optimization, fault diagnosis, and fault management. Furthermore, DTMNs can characterize the physical entities in a 6G network and their relationships to each other, build their virtual models, and use simulation, learning, and reasoning capabilities to make predictions and support informed decision-making,

We are delighted to introduce the third part of the Special Section on Intelligent Autonomous Transportation Systems with 6G, which aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Twenty articles were selected for publication in this issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

We are delighted to introduce the fourth part of the Special Issue on Intelligent Autonomous Transportation Systems ith 6G, hich aims to provide the scientific community ith a comprehensive overvie of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Forty-three papers ere selected for publication in this issue. All the papers ere rigorously evaluated according to the standard revieing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. e ill introduce these articles and highlight their main contributions in the folloing.

Recently, we have experienced an incredible surge of interest in connected and autonomous vehicles and related enabling technologies, which are expected to revolutionize future Intelligent Transportation Systems (ITS). This surging demand and popularity of ITS with the Internet of Vehicles technology has led to a tremendous rise in the number of connected vehicles. Driven by this massive number of connected vehicles, and the stringent requirements of autonomous vehicles and data-intensive applications such as ultralow latency, high reliability, and high security, intelligent transportation systems are rapidly moving to the 6G networks. The 6G-supported ITS is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles.

In the near future, the 6G-supported Intelligent Transportation System (ITS) is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport System. Twenty articles were selected for publication in the second part of the issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered factors pertaining to originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

Driven by the Internet-of-Things (IoT)-enabled massively data-intensive applications, such as virtual-augmented-reality-based gaming, ultramassive machine-type communications, holographic rendering and high-precision communications, multiway teleconferencing, etc., there is a need for technological advancements and evolutions for wireless communications beyond the fifth-generation (5G) networks. The wireless data traffic is estimated to reach 4394 EB by 2030 (Source: ITU), and 5G will be unable to provide support to most of these advanced applications. Here, 6G is expected to extend the 5G capabilities to very high levels where millions of connected devices and applications could operate seamlessly with high data rates and low latency.

Collaborative and autonomous communications among devices are expected to transform the digital world in the coming years. The device-to-device (D2D) sidelink communication has a great impetus to enable industrial Internet-of-things (IoT). However, the rapid growth of such networks and dynamic quality of service demands of user equipments require robust and efficient resource management. This paper addresses the issue of mode selection in D2D sidelink communications for industrial IoT applications. Specifically, we consider a finite channel blocklength transmission in order to cater for delay-sensitive industrial IoT applications. To improve the performance of the network, we employ a graph-theoretic approach for efficient mode selection. The simulation results of the proposed approach are compared with conventional heuristic approaches. The results demonstrate the feasibility of the proposed approach against the conventional approaches especially in terms of complexity and throughput.

Digital substations (DS) became a large evolutionary advancement for electrical substations with introduction of IEC 61850 communication protocol due to minimized digital and control infrastructure involved. However, maintenance cost and simplicity can further be improved if wired communication between the control room and switchyard is complemented or substituted with a wireless solution. Wireless connectivity can bring benefits to both brown- and greenfield installations, to avoid cumbersome and expensive cable trenching or be used as a redundancy solution. Ultra-reliable low-latency communication (URLLC) technology developed in 5G networks promises support in this field. This paper presents performance results for 4G and 5G wireless systems enabling communication between switch yard and control room in digital substations. Evaluations of a 4G LTE Rel. 13 system was performed by measurements in lab environment and the performance of a 5G new radio (NR) network at the substation coexisting with surrounding macro network is done with system level simulations. Results of the tests and evaluations demonstrate the limitations of 4G LTE Rel. 13 connectivity, as well as capabilities to fulfill the communication needs in substation case using 5G NR system.

Mobile edge or fog computing is a network architecture that brings the functionality of conventional centralized cloud to the edge nodes, which are in close proximity of the end devices in an Internet of Things (IoT) network. Fog networks have many advantages over traditional cloud networks, such as increased bandwidth utilization, enhanced security and privacy, better energy efficiency, improved performance, and support for mobility. The most critical requirement of a fog architecture is to minimize the end to end latency in IoT networks, particularly in scenarios where large number of end devices (IoT nodes) and distributed computing fog nodes (cloudlets) are present. In this paper, we formulated an optimization problem for joint cloudlet selection and latency minimization in a fog network, subjected to maximum work load and latency constraints. The problem can be epitomized as many-to-one matching game in which IoT nodes and cloudlets rank each other in order to minimize the latency. The proposed game belongs to a class of matching games with externalities. We propose an algorithm to solve this game which gives distributed and self-organizing solution. Extensive simulations have been carried out to validate the proposed algorithm.

Due to the upsurge of context-aware and proximity aware applications, device-to-device (D2D) enabled mobile cloud (MC) emerges as next step towards future 5G system. There are many applications for such MC based architecture but mobile data offloading is one of the most prominent one especially for ultra dense wireless networks. The proposed system exploits the short range links to establish a cluster based network between the nearby devices, adapts according to environment and uses various cooperation strategies to obtain efficient utilization of resources. We proposed a novel architecture of MC in which the total coverage area of a eNB is divided into several logical regions (clusters). Furthermore, UEs in the cluster are classified into Primary Cluster Head (PCH), Secondary Cluster Head (SCH) and Standard UEs (UEs). Each cluster is managed by selected PCH and SCH. An algorithm is proposed for the selection of PCH and SCH which is based on signal-to-interference-plus-noise (SINR) and residual energy of UEs. Finally each PCH and SCH distributes data in their respective regions by efficiently utilizing D2D links. Simulation results demonstrate that the proposed D2D-enabled MC based approach yields significantly better gains in terms of data rate and energy efficiency as compared to the classical cellular approach.

Traditional packet scheduling is mainly designed for increasing spectral efficiency (SE) but not for the energy efficiency (EE). Self-organized network (SON) has prospective for self- configuring, self-optimizing self-healing and minimizes the energy consumption in the network. We consider self-optimizing and self-healing property of SON and investigate a novel energy efficient scheduling algorithm for LTEA. We first compare the state the of art scheduling in view of energy efficiency, then explain the tradeoff between EE and SE. System level simulation (SLS) analysis shows that the investigated SON approach achieves notable energy gain over traditional scheduling algorithm.