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Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S$^{\text{4}}$) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S$^{\text{4}}$ in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks., Comment: This paper has been accepted for publication by IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessible

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., Comment: Accepted for publication in the IEEE Transactions on Vehicular Technology

Life on earth depends on healthy oceans, which supply a large percentage of the planet's oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet-of-underwater-things (IoUTs), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision-making. The sensors in conventional IoUTs are battery-powered, which limits their lifetime, and constitutes environmental hazards when they die. In this paper, we propose a sustainable scheme to improve the throughput and lifetime of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximising throughput and harvested power as a Markov Decision Process (MDP), and develop a model-free reinforcement learning (RL) algorithm as a solution. The model's reward function incentivises the AUV to find optimal trajectories that maximise throughput and power transfer to the underwater nodes while minimising energy consumption. To the best of our knowledge, this is the first attempt at using RL to ensure sustainable underwater networks via SWIPT. The scheme is implemented in an open 3D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.

Deep Neural Networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging Non-Volatile Memories (NVMs), with their better scalability, non-volatility and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This paper proposes CRAFT, i.e., Criticality-Aware Fault-Tolerance Enhancement Techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of non-critical bits when more errors (due to stuck-at faults) are present in the critical bits., Comment: to be published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

The growing demand for optimal and low-power energy consumption paradigms for IOT devices has garnered significant attention due to their cost-effectiveness, simplicity, and intelligibility. In this article, an AI hardware energy-efficient framework to achieve optimal energy savings in heterogeneous computing through appropriate power consumption management is proposed. The deep reinforcement learning framework is employed, utilising the Actor-Critic architecture to provide a simple and precise method for power saving. The results of the study demonstrate the proposed approach's suitability for different hardware configurations, achieving notable energy consumption control while adhering to strict performance requirements. The evaluation of the proposed power-saving framework shows that it is more stable, and has achieved more than 34.6% efficiency improvement, outperforming other methods by more than 16%.

Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this paper investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks., Comment: This paper has been accepted for publication by IEEE Transactions on Vehicular Technology. Copyright may be transferred without notice, after which this version may no longer be accessible

Mirror symmetric twisted trilayer graphene (tTLG) is composed of even parity twisted bilayer graphene (tBLG)-like bands and odd parity Dirac-like bands. Here, we study the mirror-symmetric and mirror-asymmetric Hofstadter-Moir\'{e} (HM) fractal bands of tTLG. A novel quantum parity Hall state is identified in mirror-symmetric tTLG at experimentally accessible charge densities. This mirror symmetry-protected topological phase exhibits simultaneous quantized Hall and longitudinal resistances. The effects of the displacement field on the HM fractal bands of tTLG and topological phase transitions are also studied. The application of an electric displacement field results in an emergent weakly dispersive band at the charge neutrality point for a range of twist angles. This zero-energy state resides in the middle layer. It is isolated from the HM spectrum by an energy gap that scales proportional to the applied displacement field, making it a prime candidate to host correlated topological states.

The rise of intelligent reflecting surfaces (IRSs) is opening the door for unprecedented capabilities in visible light communication (VLC) systems. By controlling light propagation in indoor environments, it is possible to manipulate the channel conditions to achieve specific key performance indicators. In this paper, we investigate the role that IRSs can play in boosting the secrecy capacity of non-orthogonal multiple access (NOMA) VLC systems. More specifically, we propose an IRS-based physical layer security (PLS) mechanism that mitigates the information leakage risk inherent in NOMA. Our results demonstrate that the achieved secrecy capacity can be enhanced by up to 105% for a number of 80 IRS elements. To the best of our knowledge, this is the first paper that examines the PLS of NOMA-based IRS-assisted VLC systems.

In a space-air-ground integrated network (SAGIN), managing resources for the growing number of highly-dynamic and heterogeneous radios is a challenging task. Symbiotic communication (SC) is a novel paradigm, which leverages the analogy of the natural ecosystem in biology to create a radio ecosystem in wireless networks that achieves cooperative service exchange and resource sharing, i.e., service/resource trading, among numerous radios. As a result, the potential of symbiotic communication can be exploited to enhance resource management in SAGIN. Despite the fact that different radio resource bottlenecks can complement each other via symbiotic relationships, unreliable information sharing among heterogeneous radios and multi-dimensional resources managing under diverse service requests impose critical challenges on trusted trading and intelligent decision-making. In this article, we propose a secure and smart symbiotic SAGIN (S^4) framework by using blockchain for ensuring trusted trading among heterogeneous radios and machine learning (ML) for guiding complex service/resource trading. A case study demonstrates that our proposed S^4 framework provides better service with rational resource management when compared with existing schemes. Finally, we discuss several potential research directions for future symbiotic SAGIN., Comment: 8 pages, 6 figures, submitted to IEEE Network

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with 100m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELOare in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches R2 values of 85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given., Comment: Submitted to Image and Vision Computing, 44 pages, 7 figures

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR., Comment: This magazine article has been accepted for publication by IEEE Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessible