The path towards ultra-reliable low-latency communications via HARQ

Ultra-reliable Low-latency Communications (URLLC) is potentially one of the most disruptive communication paradigms offered by the next generation of wireless networks, 5G. This is easily demonstrated by the diverse set of applications it enables, such as autonomous driving; remote surgery; wireless networked control systems; mission-critical machine type communication; and many more. Basically, URLLC consists of the almost 100% guarantee of message delivery within a very short time interval. Furthermore, the pressure from climate change coupled with the massive growth of cellular networks expected to occur in the near future means that URLLC must also be energy efficient. On its own, achieving low-latency with high reliability is already a stringent requirement, but when that is coupled with the need for resource efficiency, it becomes even more challenging. That is the motivation behind this thesis: to study URLLC in the context of resource efficiency. Thus, a study of the counterintuitive use of retransmissions, more specifically Hybrid Automatic Repeat Request (HARQ), in the scenario of URLLC is proposed and carried out. HARQ is very attractive in terms of resource efficiency, and that is the motivation behind using it even when stringent time constraints are imposed. Four contributions are made by the present work. Firstly, a mathematical problem is presented and solved for optimizing the number of allowed retransmission rounds considering HARQ in URLLC, considering both energy efficiency as well as electromagnetic irradiation. This representation relies on a few assumptions in order to be realizable in practical scenarios. Namely, these assumptions are regarding the possibility of early error detection for sending the feedback signals and on not having to consider medium access control introduced delays. Secondly, we consider one important aspect of wireless systems, which is that they can be greatly optimized if they are designed with a specific application in mind. Based on this, a study of the use of HARQ specifically tuned for Networked Control Systems is presented, taking into account the particular characteristics of these applications. Results here show that fine-tuning for the specific characteristics of these applications yields better results when compared to using the results from the previous contribution, which are more application-agnostic. These improved results are possible thanks to the exploitation of application-specific characteristics, more specifically the use of a packetized predictive control strategy jointly designed with the communication protocol. Next, the concept of HARQ for URLLC is extended to a larger scale in an effort to relax the aforementioned assumptions. This is studied within the framework of self-organizing networks and leverages machine learning algorithms in order to overcome those strict assumptions from the first contribution. This is demonstrated by developing a digital twin simulation of the city of Glasgow and generating a large dataset of users in the cellular network, which is a third contribution of this thesis. Then, machine learning (more specifically long short-term convolutional neural networks) is applied for predicting message failures. Lastly, a protocol to exploit such predictions in combination with HARQ to deliver downlink URLLC is applied, resulting in a fourth contribution. In summary, this thesis presents a latency aware HARQ technique which is shown to be very efficient. We show that it uses up as much as 18 times less energy than a frequency diversity strategy and that it can emit more than 10 times less energy electromagnetic field radiation when compared to the same strategy. We also propose joint design techniques, where communication and control parameters are tweaked at the same time, enabling wireless control systems with a three-fold reduction in required bandwidth to achieve URLLC requirements. Lastly, we present a digital twin of the city of Glasgow which enables us to create a prediction algorithm for predicting channel quality with very high accuracy—root mean square error on the order of 10−2. This ties into the rest of the contributions as it can be used to enable early feedback detection, which in turn can be used to make sure the latency aware protocol can be employed

Task-oriented prediction and communication co-design for haptic communications

Prediction has recently been considered as a promising approach to meet low-latency and high-reliability requirements in long-distance haptic communications. However, most of the existing methods did not take features of tasks and the relationship between prediction and communication into account. In this paper, we propose a task-oriented prediction and communication co-design framework, where the reliability of the system depends on prediction errors and packet losses in communications. The goal is to minimize the required radio resources subject to the low-latency and high-reliability requirements of various tasks. Specifically, we consider the just noticeable difference (JND) as a performance metric for the haptic communication system. We collect experiment data from a real-world teleoperation testbed and use time-series generative adversarial networks (TimeGAN) to generate a large amount of synthetic data. This allows us to obtain the relationship between the JND threshold, prediction horizon, and the overall reliability including communication reliability and prediction reliability. We take 5G New Radio as an example to demonstrate the proposed framework and optimize bandwidth allocation and data rates of devices. Our numerical and experimental results show that the proposed framework can reduce wireless resource consumption up to 77.80% compared with a task-agnostic benchmark

Energy sustainable paradigms and methods for future mobile networks: A survey

In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure

Engineering and technology applications of control co-design: a survey

Control-inspired design, as the name suggests, involves drawing inspiration from control theory to design other engineering systems. Engineers may use the principles of feedback control to design systems that can adapt and self-correct in response to changing conditions. This technique is known as Control Co-design (CCD), and it focuses on the redesign of dynamics and subsystem interactions. CCD offers several benefits, such as improved performance, reduced design time and cost, and increased reliability, and has been applied to a variety of areas. In this paper, we present a review of 197 articles related to CCD and highlight the main topics of its applications, such as renewable energy, vehicular and aircraft control systems and communication systems in control. We delimit the applications of CCD in the field of engineering, providing an introductory understanding of this topic and presenting the main works developed in this field in recent years, as well as discussing the tendencies and benefits of CCD. The paper offers an in-depth conceptualisation of CCD. A theoretical example is provided to illustrate CCD’s application in a Hybrid Wind-Wave Platform (HWWP), detailing the interaction between aerodynamic and hydrodynamic design domains and their control challenges, along with discussions on simultaneous and nested CCD formulations

Wireless Resource Management in Industrial Internet of Things

Wireless communications are highly demanded in Industrial Internet of Things (IIoT) to realize the vision of future flexible, scalable and customized manufacturing. Despite the academia research and on-going standardization efforts, there are still many challenges for IIoT, including the ultra-high reliability and low latency requirements, spectral shortage, and limited energy supply. To tackle the above challenges, we will focus on wireless resource management in IIoT in this thesis by designing novel framework, analyzing performance and optimizing wireless resources. We first propose a bandwidth reservation scheme for Tactile Internet in the local area network of IIoT. Specifically, we minimize the reserved bandwidth taking into account the classification errors while ensuring the latency and reliability requirements. We then extend to the more challenging long distance communications for IIoT, which can support the global skill-set delivery network. We propose to predict the future system state and send to the receiver in advance, and thus the delay experienced by the user is reduced. The bandwidth usage is analysed and minimized to ensure delay and reliability requirements. Finally, we address the issue of energy supply in IIoT, where Radio frequency energy harvesting (RFEH) is used to charge unattended IIoT low-power devices remotely and continuously. To motivate the third-party chargers, a contract theory-based framework is proposed, where the optimal contract is derived to maximize the social welfare

Task-oriented joint design of communication and computing for Internet of Skills

Nowadays, the internet is taking a revolutionary step forward, which is known as Internet of Skills. The Internet of Skills is a concept that refers to a network of sensors, actuators, and machines that enable knowledge, skills, and expertise delivery between people and machines, regardless of their geographical locations. This concept allows an immersive remote operation and access to expertise through virtual and augmented reality, haptic communications, robotics, and other cutting-edge technologies with various applications, including remote surgery and diagnosis in healthcare, remote laboratory and training in education, remote driving in transportation, and advanced manufacturing in Industry 4.0. In this thesis, we investigate three fundamental communication requirements of Internet of Skills applications, namely ultra-low latency, ultra-high reliability, and wireless resource utilization efficiency. Although 5G communications provide cutting-edge solutions for achieving ultra-low latency and ultra-high reliability with good resource utilization efficiency, meeting these requirements is difficult, particularly in long-distance communications where the distance between source and destination is more than 300 km, considering delays and reliability issues in networking components as well as physical limits of the speed of light. Furthermore, resource utilization efficiency must be improved further to accommodate the rapidly increasing number of mobile devices. Therefore, new design techniques that take into account both communication and computing systems with the task-oriented approach are urgently needed to satisfy conflicting latency and reliability requirements while improving resource utilization efficiency. First, we design and implement a 5G-based teleoperation prototype for Internet of Skills applications. We presented two emerging Internet of Skills use cases in healthcare and education. We conducted extensive experiments evaluating local and long-distance communication latency and reliability to gain insights into the current capabilities and limitations. From our local experiments in laboratory environment where both operator and robot in the same room, we observed that communication latency is around 15 ms with a 99.9% packet reception rate (communication reliability). However, communication latency increases up to 2 seconds in long-distance scenarios (between the UK and China), while it is around 50-300 ms within the UK experiments. In addition, our observations revealed that communication reliability and overall system performance do not exhibit a direct correlation. Instead, the number of consecutive packet drops emerged as the decisive factor influencing the overall system performance and user quality of experience. In light of these findings, we proposed a two-way timeout approach. We discarded stale packets to mitigate waiting times effectively and, in turn, reduce the latency. Nevertheless, we observed that the proposed approach reduced latency at the expense of reliability, thus verifying the challenge of the conflicting latency and reliability requirements. Next, we propose a task-oriented prediction and communication co-design framework to meet conflicting latency and reliability requirements. The proposed framework demonstrates the task-oriented joint design of communication and computing systems, where we considered packet losses in communications and prediction errors in prediction algorithms to derive the upper bound for overall system reliability. We revealed the tradeoff between overall system reliability and resource utilization efficiency, where we consider 5G NR as an example communication system. The proposed framework is evaluated with real-data samples and generated synthetic data samples. From the results, the proposed framework achieves better latency and reliability tradeoff with a 77.80% resource utilization efficiency improvement compared to a task-agnostic benchmark. In addition, we demonstrate that deploying a predictor at the receiver side achieves better overall reliability compared to a system that predictor at the transmitter. Finally, we propose an intelligent mode-switching framework to address the resource utilization challenge. We jointly design the communication, user intention recognition, and modeswitching systems to reduce communication load subject to joint task completion probability. We reveal the tradeoff between task prediction accuracy and task observation length, showing that higher prediction accuracy can be achieved when the task observation length increases. The proposed framework achieves more than 90% task prediction accuracy with 60% observation length. We train a DRL agent with real-world data from our teleoperation prototype for modeswitching between teleoperation and autonomous modes. Our results show that the proposed framework achieves up to 50% communication load reduction with similar task completion probability compared to conventional teleoperation