Sensor location awareness is a critical issue in internet of things applications. For more accurate location estimation, the two issues should be considered extensively: 1) how to sufficiently utilize the connection information between multiple nodes and 2) how to select a suitable solution from multiple solutions obtained by the Euclidean distance loss. In this paper, a DV-Hop localization based on the distance estimation using multi-node (DEMN) and the hop loss in WSNs is proposed to address the two issues. In DEMN, when multiple anchor nodes can detect an unknown node, the distance expectation between the unknown node and an anchor node is calculated using the cross domain information and is considered as the expected distance between them, which narrows the search space. When minimizing the traditional Euclidean distance loss, multiple solutions may exist. To select a suitable solution, the hop loss is proposed, which minimizes the difference between the real and its predicted hops. Finally, the Euclidean distance loss calculated by the DEMN and the hop loss are embedded into the multi-objective optimization algorithm. The experimental results show that the proposed method gains 86.11% location accuracy in the randomly distributed network, which is 6.05% better than the DEM-DV-Hop, while DEMN and the hop loss can contribute 2.46% and 3.41%, respectively.

Terahertz (THz) communications are envisioned as one of the promising technologies to enable ultra-broadband 6G systems. One fundamental challenge when moving to new spectrum is to understand the science of radio propagation and propose an accurate and effective channel prediction method for predicting the signal coverage. In this paper, we first conduct extensive VNA-based wideband radio propagation measurements at 220 GHz in indoor hallway and lobby environments and at 280 GHz in an indoor laboratory environment. Omnidirectional and best directional path loss are analyzed and modeled by empirical single-band and multi-band path loss models. Besides, propagation statistics such as Rician K-factor (KF) and root mean square (RMS) delay spread (DS) are modeled by Weibull distribution and lognormal distribution, respectively, and two-slope model is proposed to analyze the relationship of the KF, RMS DS and distance in various scenarios. Numerical results demonstrate that large-scale close-in model in this paper is simpler and more physically-based compared to floating-intercept model. In particular, path loss prediction method based on environment features is proposed, which can predict path loss directly by utilizing random forest method, and the propagation environment are defined and extracted by scatterer features and related features of Tx and Rx. The performance of the proposed model is better than that of empirical path loss models. The measured results not only enrich the datasets of indoor THz channel propagation, also can guide communication systems, network planning and deployment for 6G and beyond.

Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for largescale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular, publicly-available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution, and quality of labels, and (3) the composition of the database records, and then identify potential issues and ways to address them in using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.

In this paper, we investigate the performance of a Hybrid Quantum Neural Network (HQNN) and a comparable classical Convolution Neural Network (CNN) for detection and classification problem using a radar. Specifically, we take a fairly complex radar time-series model derived from electromagnetic theory, namely the Martin-Mulgrew model, that is used to simulate radar returns of objects with rotating blades, such as drones. We find that when that signal-to-noise ratio (SNR) is high, CNN outperforms the HQNN for detection and classification. However, in the low SNR regime (which is of greatest interest in practice) the performance of HQNN is found to be superior to that of the CNN of a similar architecture.

This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)- enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO base station. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received signal, beam-pattern gains to improve the sensing SINR of reflected echo signals and maximizing the evidence lower bound minus loss function, which in turn minimizes the losses of the ISAC process, and maximizes achievable rate for efficient power allocation. A novel AI-driven framework is presented to tackle the formulated NP-hard problem by decomposing it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the findings achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINR improvement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively.

Radio over Fiber (RoF) is pivotal for extending reliable 5G connectivity to enhanced remote area communications (ERAC) use cases, that can be used for transporting analog signals from the central office to a simplified remote base station, composed only of an optical detector and radio-frequency front-end. However, the RoF link introduces undesired nonlinear effects that can severely degrade overall system performance and prohibitively increase the out-of-band emissions. We investigate and propose the use of reinforcement learning (RL) algorithms based digital predistortion (DPD) called as RLDPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. We experimentally compare the proposed RLDPD with the conventional methods including generalized memory polynomial (GMP), canonical piecewise linearization (CPWL) and deep learning based convolutional neural networks (CNN). The experiment evaluation involves multiband 5G new radio (NR) flexible-waveform signals at 3 GHz and 10 GHz carrier signal transmitted over a 10 km single mode fiber length. The performance is compared in terms of error vector magnitude (EVM), adjacent channel leakage ratio (ACLR) and computation complexities. The RLDPD achieves a EVM of 2.85% for 5G NR waveform, surpassing GMP's 4.8%, CPWL's 3.5%, CNN's 3.08% and 11.25% without linearization, while also reducing ACPR by 19 dBc when compared to absence of linearization.

We address the problem of real-time remote tracking of a partially observable Markov source in an energy harvesting system with an unreliable communication channel. We consider both sampling and transmission costs. Different from most prior studies that assume the source is fully observable, the sampling cost renders the source partially observable. The goal is to jointly optimize sampling and transmission policies for two semantic-aware metrics: i) a general distortion measure and ii) the age of incorrect information (AoII). We formulate a stochastic control problem. To solve the problem for each metric, we cast a partially observable Markov decision process (POMDP), which is transformed into a belief MDP. Then, for both AoII under the perfect channel setup and distortion, we express the belief as a function of the age of information (AoI). This expression enables us to effectively truncate the corresponding belief space and formulate a finite-state MDP problem, which is solved using the relative value iteration algorithm. For the AoII metric in the general setup, a deep reinforcement learning policy is proposed to solve the belief MDP problem. Simulation results show the effectiveness of the derived policies and, in particular, reveal a non-monotonic switching-type structure of the real-time optimal policy with respect to AoI.

This paper presents a metal-only reflectarray based on a 3D unit cell with dual-band capability. The 3D unit cell is a square waveguide whose vertical walls include resonator elements with independent frequency performance. Different resonator geometries are analyzed to obtain a reflected phase variation in the target frequency band and to be feasible for 3-D manufacturing. C-shaped, triangle-shaped, and circle-shaped resonators are selected to obtain the required phase shift in reflection. Two reflectarray (RA) prototypes are designed, including pairs of these resonators where the C-shaped resonator controls the low-frequency band, and the circle and triangle resonators do so for the high-frequency band. The main reflected beam directions for each frequency band are different to show the independent phase tuning of the resonators. The prototypes are manufactured using stereolithography (SLA) with a subsequent silver coating. Measured results show realized gains of 21 dBi in the 18 GHz band and 24 dBi in the 26.5 GHz band, with a high radiation efficiency and good agreement with the simulated results.

With rapid urbanization, cities face immense pressures on infrastructure and resources. Uncoordinated management of transportation, energy, water and waste infrastructure leads to inefficiencies, delays and unsustainability. This paper proposes a novel IoT-enabled framework to address these challenges through holistic data-driven management of city infrastructure. While prior works have explored IoT point solutions for specific domains, our integrated framework delivers a comprehensive architecture for citywide infrastructure visibility. The edge computing-based distributed design enables scalable real-time analytics across thousands of heterogeneous assets spread city-wide. Through consolidated storage and analytics, interdependencies between various infrastructure systems can be uncovered to optimize overall city operations. The standardsbased implementation fosters seamless integration of diverse infrastructure technologies. Our unified data management layer provides a single platform for visual intelligence on city-wide infrastructure health to support data-driven planning. We demonstrate the efficacy of the proposed framework through a case study focused on transportation infrastructure management. The results showcase significant enhancements in operational efficiency, sustainability and cost savings across transport assets when managed under the IoT-enabled framework versus traditional siloed approaches. This paper provides city leaders and technologists an implementable blueprint to harness the power of IoT and analytics for transitioning to smarter, sustainable and resident-friendly infrastructure.

Future 6G networks will be enabled by full softwarization of network functions & operations and in-network intelligence for self-management and orchestration. However, the intelligent management of a softwarized network will require massive data mining, analytics, and processing. That is why it is fundamental to find additional resources like quantum technologies to help achieve 6G key performance indicators. Quantum properties provide quantum computers to run a quantum algorithm with lesser queries. Quantum Machine Learning (QML) studies machine learning techniques on quantum computers. In this work, we use a QML algorithm to solve the controller placement problem for a multi-controller Software Defined Network (SDN). The network delay depends on where the controller is located, thus, it is critical to choose controllers at positions leading to minimize latency between the controllers and their associated switches. We consider an SDN architecture which is in its early stage of installation where the network nodes are deployed but connections will be established after obtaining controller locations, which results in the reduction of the overall controller to switch delay. By using different types of datasets, i.e., uniformly distributed and Gaussian distributed points, the experimental results show that the QML algorithm accelerates the SDN clustering methods (which are used to resolve the control placement problem) compared to those of the classical machine learning algorithm (like K-means) with comparable latency.

Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

We propose a communication-efficient collaborative inference framework in the domain of edge inference, focusing on the efficient use of vision transformer (ViTs) models. The partitioning strategy of conventional collaborative inference fails to reduce communication cost because of the inherent architecture of ViTs maintaining consistent layer dimensions across the entire transformer encoder. Therefore, instead of employing the partitioning strategy, our framework utilizes a lightweight ViT model on the edge device, with the server deploying a complicated ViT model. To enhance communication efficiency and achieve the classification accuracy of the server model, we propose two strategies: 1) attention-aware patch selection and 2) entropy-aware image transmission. Attention-aware patch selection leverages the attention scores generated by the edge device’s transformer encoder to identify and select the image patches critical for classification. This strategy enables the edge device to transmit only the essential patches to the server, significantly improving communication efficiency. Entropy-aware image transmission uses min-entropy as a metric to accurately determine whether to depend on the lightweight model on the edge device or to request the inference from the server model. In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task. Our experiments demonstrate that the proposed collaborative inference framework can reduce communication overhead by 68 % with only a minimal loss in accuracy compared to the server model.

Elliptic curve scalar multiplication (ECSM) is a fundamental element of pre-quantum public key cryptography, which is the predominant choice for public key cryptography. ECSM implementations on deeply-embedded architectures and Internet-of-nano-Things have been vulnerable to both permanent and transient errors, as well as fault attacks. Consequently, error detection is crucial. In this work, we present a novel algorithm-level error detection scheme on Montgomery ladder often used for a number of elliptic curves featuring highly efficient point arithmetic, known as Montgomery curves. Our error detection simulations achieve high error coverage on loop abort and scalar bit flipping fault model utilizing binary tree data structure. Assuming n is the size of the private key, the overhead of our error detection scheme is O(n). Finally, we conduct a benchmark of our suggested error detection scheme on both ARMv8 and FPGA platforms to illustrate the implementation and resource utilization. Deployed on Cortex-A72 processors, our proposed error detection scheme maintains a clock cycle overhead of less than 3%. Additionally, integrating our error detection approach into FPGAs, including AMD/Xilinx Zynq Ultrascale+ and Kintex Ultrascale+, results in comparable throughput and less than 1% increase in area compared to the original hardware implementation. We note that we envision using the proposed architectures in the post-quantum cryptography (PQC) based on elliptic curves.

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, IIoTBroker, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, IIoTDeployer, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results.

This work emerges from the intensifying need to understand and address security issues in rapidly advancing technologies such as 5G and beyond, including open radio access network (O-RAN). The current paper provides an in-depth examination of the security aspects of the E2 interface within the O-RAN context. The research underscores the diverse roles that the E2 interface assumes in enabling communication between the RAN Intelligent Controller (RIC) and the E2 node. It critically examines the various vulnerabilities and potential security threats of this interface. This work subsequently reviews the security mechanisms and methodologies proposed by the O-RAN Alliance to secure the E2 interface. This work aims to highlight the crucial role that the E2 interface undertakes in the network's overall communication and the indispensable security questions, given the stakes of these networks. The findings from this work could serve as a valuable addition to existing resources and provide insightful perspectives for future research in this field. The paper concludes with a discussion of potential directions for future work on the security of the E2 interface.

In recent years, RIS has made significant progress in engineering application research and industrialization, as well as in academic research. However, the engineering application research field of RIS still faces several challenges. This article analyzes and discusses the two deployment modes of RIS-Assisted wireless networks, namely Network Controlled Mode and Standalone mode. It also presents three typical collaboration scenarios of RIS networks, including multi-RIS collaboration, multiuser access, and multi-cell coordination, which reflect the differences between the two deployment modes of RIS. The article proposes collaborative regulation mechanisms for RIS and analyzes their applications in the two network deployment modes in-depth. Furthermore, the article establishes simulation models of three scenarios and provides rich numerical simulation results. An actual field test environment is also built, where a specially designed and processed RIS prototype was used for preliminary field test and verification. Finally, this article puts forward future trends and challenges.

Today modern web site accelerated by scripts, but the foundation, web page its self is still a static structure. Document Object Model (DOM) represents the structure of web page. Here we show a new approach: It is possible to put timetree and DOM together to shape a new structure named Time Object Model. TOM represents not only a static page but also a dynamic stream. We believe the best way for using TOM is to embed it into a HTML page in real time without changing the existence, it is the only way works now.

In this paper, comprehensive double-directional channel measurements at 300 GHz in various usage scenarios in corridor environments, such as Access, Device-to-device (D2D), and Backhaul over 40 different receiver (Rx) positions using an in-house developed channel sounder, are presented. The measurement results are analyzed and validated by ray tracing (RT) simulation. The quasi-optical propagation properties at 300 GHz make an accurate estimation of relatively simple propagation in a corridor environment possible by using ray optics theory. However, even though non-trivial quadruple-bounce specular reflection paths can be identified in both scenarios, propagation phenomena other than reflection exist irrespective of the Rx positions. Thus, to model the propagation mechanism appropriately, a quasi-deterministic (QD) channel model comprising deterministic and random components is also proposed. The results generated using the proposed model are found to agree well with our prior observations and measurement results. Finally, the paper concludes by characterizing and comparing the channel for all the investigated scenarios in terms of path loss (PL) and large-scale parameters (LSP). On analyzing the measurement results using synthesized power spectra, proposed QD model, and evaluated PL and LSP it is observed that the Access and D2D scenarios share almost similar propagation mechanisms. Furthermore, in the Access and Backhaul scenario the LoS is observed to be affected by the unresolvable ceiling-reflected components. This study, across three different scenarios, can aid the design of next-generation communication systems operating in the THz spectrum.

Enabling artificial intelligence native end-to-end systems in ultra-wideband sub-terahertz spectrum faces several challenges. The particularly complex channel variations and nonlinear behavior of analog components of the transceivers are major obstacles to the over-the-air adaptation of these systems. In this paper, we investigate an edge-based bidirectional long-shortterm memory neural network capable of predicting the channel gain variations in Non-Line-of-Sight conditions. We aim to enable end-to-end autoencoders with a predictive model for scheduling the training phase when the power is above the receiver sensitivity and there are no large fading variations. Otherwise, the training of the end-to-end system will likely fail. With only 16 BiLSTM cells our model is capable of inferring the channel gain variations with a worst-case root mean squared error lower than 0.0547 (i.e., 1.1% compared to the normalized channel gain range). Also, with lower computational complexity, our model decreased the propagation of the error compared to traditional recurrent neural networks and deep-learning-based forecasting models.

In the context of 6G architecture development, the concept of a softwarized (orchestration) continuum is a key pillar. Nevertheless, achieving complete softwarization of network functionalities, tasks, and operations presents inherent challenges, leading to critical trade-offs and limitations. This article explores a novel approach to address these issues by integrating quantum technologies and the Physical Layer Service Integration (PLSI) paradigm. Specifically, we propose the formulation and analysis of network synchronization as a quantum PLSI problem. Our study evaluates synchronization time offset in both conventional Precision Time Protocol (PTP) and quantum-based approaches within the network. We investigate the impact of various network conditions on the precision of PTP synchronization, ranging from nanoseconds under ideal circumstances to microseconds when utilizing virtual network devices. Further, we perform a simulation to generate frequency-entangled photon pairs to access nonlocal temporal correlations and calculate the time offsets. Our findings reveal that entanglement-based PLSI for network synchronization achieves precision at the picosecond level. These results emphasises the high precision achievable by interpreting the network synchronisation problem in the perspective of PLSI and not as a service of the softwarized continuum.

Ultra-reliable and low latency communications (URLLC) will be the backbone of the upcoming sixth-generation (6G) systems and will facilitate mission-critical scenarios. A design accounting for stringent reliability and latency requirements for URLLC systems poses a challenge for both industry and academia. Recently, unmanned aerial vehicles (UAV) have emerged as a potential candidate to support communications in futuristic wireless systems due to providing favourable channel gains thanks to Lineof-Sight (LoS) communications. However, usage of UAV in cellular infrastructure increases interference in aerial and terrestrial user equipment (UE) limiting the performance gain of UAV-assisted cellular systems. To resolve these issues, we propose low-complexity algorithms for intercell interference coordination (ICIC) using cognitive radio when single and multi-UAVs are deployed in a cellular environment to facilitate URLLC services. Moreover, we model BS-to-UAV (B2U) interference in downlink communication, whereas in uplink we model UAV-to-BS (U2B), UAV-to-UAV (U2U), and UE-to-UAV (UE2U) interference under perfect/imperfect channel state information (CSI). Results demonstrate that the proposed perfect ICIC accounts for fairness among UAV especially in downlink communications compared to conventional ICIC algorithms. Furthermore, in general, the proposed UAV-sensing assisted ICIC and perfect ICIC algorithms yield better performance when compared to conventional ICIC for both uplink and downlink for the single and multi-UAV frameworks. INDEX TERMS URLLC, multi-UAV, cognitive radio, intercell interference coordination (ICIC).

The value of colorless, directionless, and contentionless (CDC-)ROADM (reconfigurable optical add/drop multiplexer) nodes is strongly contested in the optical networking community. In this work, we compare known ROADM node designs incorporating different switching elements and account for their total nodal switching state support (in consideration of both channel routing and add/drop). This allows us to quantify the impact of directional/contentional accessibility constraints to add/drop transceivers. By considering the network node entity as a permutation network among its ingress/egress ports for all wavelength channels, which covers both through routing and add/drop assignments, we tabulate the node’s switching capacity, or total allowable connection states, per different ROADM architecture, hardware constraints, and finite number of add/drop transceivers. We further introduce the impact of idle wavelength channels on fiber links, as well as bidirectional routing assignments. Our switching capacity enumerations demonstrate that CDC-ROADM outperforms other designs, but parallel contentional aggregation hardware (partially contentional) and directional transceivers (permanently assigned to port directions) offer competitive performance under certain scenarios (at lower and higher number of deployed transceivers, or a combination of both). These findings suggest that design alternatives to the “difficult to implement” CDC-ROADM exist, with nearly equivalent switching capacity, and additional system considerations must be taken into account for ROADM design selection such as hardware availability, cost, impact of traffic churn, and disaster recovery with over-provisioned add/drop transceivers.

The extensive applications of wires and cables in industries are a result of the rapid advancements in electronic device technology. Despite the established use of wireless technology, safety concerns have sustained the reliance on cables. Detecting soft faults is a challenging task due to the fact that they create reflections that are not easily distinguishable. Time and frequency domain reflectometry are commonly used for fault detection. Here, we propose a stable and effective method for estimating transmission line (TL) impedance using frequency domain reflectometry with neural network models. This method not only identifies the location of soft faults but also provides an impedance profile across the TLs. The performance of the proposed method is verified using simulated and experimental data. Moreover, we address the limitations of the Born approximations, which become ineffective for lossy TLs. The proposed method takes into account the loss information of the TLs, enabling accurate estimations of both the real and imaginary parts of the complex impedance values.

Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, usercentric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this form of network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. In this paper, we present a BiLSTM-based multivariate path loss forecasting algorithm. Thanks to the combination of dual prediction by the BiLSTM and diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks for time-series forecasting. In the evaluated scenario, from 2 to 10 steps ahead, we reduce the propagation of the error by a factor of 18 compared to previous research on path loss forecasting by an LSTM time-series-based model. In contrast to parallel transformer solutions, the complexity cost of our algorithm is also significantly lower.