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Controller Area Network (CAN) is an ISO-standardized protocol that is extensively used as the onboard real-time network in vehicles. Evaluating and mitigating responsetime jitter experienced by CAN messages is paramount, particularly in real-time applications where timing predictability is crucial. This paper presents a jitter analysis framework for CAN messages. Within this framework, the worst-case responsetime jitter of CAN messages is calculated through pre-runtime analysis. Additionally, a post-runtime analysis of message traces helps ascertain the maximum response-time jitter these messages encounter at runtime. The framework performs a comparative evaluation of the pre-runtime and runtime response-time jitter and then back-propagates insights and guidelines to the systems' designers to mitigate the jitter. To validate this approach, we implement the framework using two prominent industrial tools: Rubus-ICE and CANalyzer. The effectiveness of the framework is further substantiated by modeling and analyzing an industrial use case provided by Volvo Construction Equipment., Conference paper; Export Date: 17 April 2024; Cited By: 0; Correspondence Address: S. Mubeen; Mälardalen University, Västerås, Sweden; email: saad.mubeen@mdu.se; Conference name: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024; Conference date: 1 February 2024 through 2 February 2024; Conference code: 198277

Over the last two decades, vehicles have undergone a significant shift, transforming into highly software-intensive systems. Projections indicate that even entry-level vehicles will soon integrate hundreds of millions of lines of code and incorporate numerous electronic control units. To navigate the complexity of these software-intensive systems, there has been a notable shift towards adopting model-driven engineering and specialised modelling languages. Among these languages, the Rubus Component Model has played a crucial role for over 25 years, supporting the development and timing analysis of distributed resource-constrained embedded systems. The enduring success of the Rubus Component Model lies in its responsiveness to end-users' demands and its ability to adapt to technological advancements. Notably, the proliferation of network interface controllers, including controller area network controllers, supporting diverse message-receiving policies like polling and interrupt, represents a significant advancement. However, the implications of these policies on end-to-end delays in distributed systems necessitate explicit modelling and dedicated timing analysis tools.This paper introduces an evolved Rubus Component Model, tailored for model-driven development and timing analysis in distributed embedded systems that utilise network interface controllers with diverse message-receiving policies. Drawing inspiration from a real-world example, the paper introduces new elements and properties in the Rubus Component Model designed to support these policies and facilitate timing analysis. The practical application of these enhancements is demonstrated, and insights are extended to other contemporary modelling languages in the vehicular domain. Beyond bolstering expressiveness, this evolution ensures the timing predictability of distributed embedded systems, aligning seamlessly with the Rubus Component Model's core focus., Conference paper; Export Date: 17 April 2024; Cited By: 0; Correspondence Address: A. Bucaioni; Mälardalen University, Västerås, Sweden; email: alessio.bucaioni@mdu.se; Conference name: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024; Conference date: 1 February 2024 through 2 February 2024; Conference code: 198277

16th International Conference on Information Security and Cryptology, ISCTURKEY 2023 -- 18 October 2023 through 19 October 2023 -- 195312, Involutive Lightweight Block Cipher (IVLBC) is a recently proposed SPN-type block cipher. The intended platform for this cipher is the Internet of Things (IoT), and its performance is promising for both software and hardware. Preliminary differential and linear cryptanalysis are conducted via Mixed-Integer Linear Programming (MILP), an automated tool in the design paper. In this paper, we employ the MILP approach to identify the best linear and differential characteristics of IVLBC. For differential cryptanalysis, we discover a characteristic with a probability of 2-46 over 7 rounds of IVLBC, while in linear cryptanalysis, we identify a characteristic with a bias of 2-24, again for 7 rounds of the cipher. These results provide the best single-key differential and linear distinguishers of this cipher in the literature. © 2023 IEEE.

16th International Conference on Information Security and Cryptology, ISCTURKEY 2023 -- 18 October 2023 through 19 October 2023 -- 195312, Involutive Lightweight Block Cipher (IVLBC) is a recently proposed SPN-type block cipher. The intended platform for this cipher is the Internet of Things (IoT), and its performance is promising for both software and hardware. Preliminary differential and linear cryptanalysis are conducted via Mixed-Integer Linear Programming (MILP), an automated tool in the design paper. In this paper, we employ the MILP approach to identify the best linear and differential characteristics of IVLBC. For differential cryptanalysis, we discover a characteristic with a probability of 2-46 over 7 rounds of IVLBC, while in linear cryptanalysis, we identify a characteristic with a bias of 2-24, again for 7 rounds of the cipher. These results provide the best single-key differential and linear distinguishers of this cipher in the literature. © 2023 IEEE.

Constacyclic codes contain cyclic codes as a subclass and have nice algebraic structures. Constacyclic codes have theoretical importance, as they are connected to a number of areas of mathematics and outperform cyclic codes in several aspects. Negacyclic codes are a subclass of constacyclic codes and are distance-optimal in many cases. However, compared with the extensive study of cyclic codes, negacyclic codes are much less studied. In this paper, several families of ternary negacyclic codes and their duals are constructed and analysed. These families of negacyclic codes and their duals contain distance-optimal codes and have very good parameters in general. The duals of three families of ternary negacyclic codes presented in this paper are distance-optimal. IEEE

Constacyclic codes over finite fields are a family of linear codes and contain cyclic codes as a subclass. Constacyclic codes are related to many areas of mathematics and outperform cyclic codes in several aspects. Hence, constacyclic codes are of theoretical importance. On the other hand, constacyclic codes are important in practice, as they have rich algebraic structures and may have efficient decoding algorithms. In this paper, two classes of constacyclic codes are constructed using a general construction of constacyclic codes with cyclic codes. The first class of constacyclic codes is motivated by the punctured Dilix cyclic codes and the second class is motivated by the punctured generalised Reed-Muller codes. The two classes of constacyclic codes contain optimal linear codes. The parameters of the two classes of constacyclic codes are analysed and some open problems are presented in this paper. IEEE

Scientists without specific software programming skills are increasingly required to express their problems in terms of software to exploit the computational power of heterogeneous parallel hardware. Producing software for this hardware is very cumbersome for the experienced programmer; for the novice, it is just impracticable. We aim to grant scientists across disciplines access to heterogeneous hardware via a model-driven holistic approach. Via proper software modelling, we suppress the need to write resource-specific software functions and complex offloading and communication code; we will do this by devising a comprehensive modelling language that implicitly underpins multiple execution semantics (sequential, data-/task-parallel). Current code generators neglect model semantics while compilers expect in input too detailed software descriptions; we will devise an innovative semantics-aware model compiler with automatic parallelization. Overall, we aim at giving researchers and practitioners better tools to focus more on the problem to solve than learning how to master complex techniques and languages to describe it in terms of software. Moreover, the learning curve and technological hindrances for beginners approaching hybrid software will be pushed down dramatically., Conference paper; Export Date: 17 April 2024; Cited By: 0; Correspondence Address: F. Ciccozzi; Mälardalen University, School of Innovation, Design, and Engineering, Västerås, Sweden; email: federico.ciccozzi@mdu.se; Conference name: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024; Conference date: 1 February 2024 through 2 February 2024; Conference code: 198277

In this paper, we present a numerical simulation scenario to estimate the signal strength of a tumor in a microwave detection scenario containing a magnetic near-field applicator and a quarter-wave dipole as transmitter and receiver, respectively. Two different receiver orientations are tested on two different breast phantoms representing high-adipose and low-adipose bulk breast tissue. The tumor signal strength is estimated by subtracting the received signals acquired with and without a tumor being present. The results indicate that in the horizontally aligned receiver case, the presence of a tumor can be estimated from the receiver positions exhibiting the highest difference signals. The strongest difference signals however do not occur at the frequency of operation but below. The other cases are not conclusive, but a stronger tumor signal was received with the horizontally aligned receiver than with the vertically aligned one throughout., Conference code: 199134

Systolic array has emerged as a prominent archi-tecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essen-tial for deploying DNNs across diverse applications. However, when used in safety-critical applications, reliability assessment is mandatory to guarantee the correct behavior of DNN accelerators. While fault injection stands out as a well-established practical and robust method for reliability assessment, it is still a very time-consuming process. This paper addresses the time efficiency issue by introducing a novel hierarchical software-based hardware-aware fault injection strategy tailored for systolic array-based DNN accelerators. The uniform Recurrent Equations system is used for software modeling of the systolic-array core of the DNN accelerators. The approach demonstrates a reduction of the fault injection time up to 3 × compared to the state-of-the-art hybrid (software/hardware) hardware-aware fault injection frameworks and more than 2000 × compared to RT-level fault injection frameworks - without compromising accuracy. Additionally, we propose and evaluate a new reliability metric through experimental assessment. The performance of the framework is studied on state-of-the-art DNN benchmarks.

5th International Conference on Problems of Cybernetics and Informatics, PCI 2023 -- 28 August 2023 through 30 August 2023 -- 195003, This study considers a non-linear Cramér-Lundberg model of the risk theory and investigates the adjustment coefficient when the claims have gamma distribution. The ruin probability of this non-linear risk model is considered when the premium function is square root of time. Thus, in this study, the adjustment coefficient is explored by numerical methods and proposed an approximate formula for practical calculation of adjustment coefficient. Moreover, an implementation of the obtained approximate formula, which investigates ruin probability, is included as an example at the end of the paper. © 2023 IEEE.

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-Term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. © 2002-2012 IEEE.

In electrical distribution networks an adequate management is key for supporting the deployment of renewable generation sources and microgrids while extracting their maximum potential. Among the existing optimization approaches, stochastic and probabilistic methods are experiencing a growth in their use. However, one of the problems when applying these approaches is the complexity of creating and evaluating the quality of the required stochastic forecasts compared to deterministic forecasts. To mitigate this difficulty, this paper proposes a probabilistic forecasting framework that integrates model creation, their evaluation, and the selection of the best model for predicting. Additionally, two novel methods are proposed for creating scenario sets, and a new metric is defined for evaluating and selecting which model to use. The proposed framework is applied in a case study over a dataset of ten secondary distribution substations from a real distribution network located in Manzanilla (Spain), showing the effect of the selection criteria over the forecasting quality. —This article was motivated by the challenge of probabilistic forecasting inclusion in automatic management systems applied to power distribution networks and microgrids. Modern stochastic management optimization methods are fed with probabilistic forecasts, which offer richer information than classic deterministic forecasting. Therefore, the management systems should be able to automatically train a certain number of forecasting models (e.g., machine learning models), evaluate and compare them, and apply the best ones for obtaining the forecasts to feed the management optimization system. Considering the variety of models, techniques, probabilistic forecast types, and evaluation metrics, it can be unclear how to perform this process. For these reasons, this article proposes a probabilistic forecasting framework that integrates methods for the construction of diverse types of predictions (quantiles, inter

Levenberg-Marquardt (LM) algorithm is a powerful approach to optimize the parameters of a neural network (NN). Given a training dataset, the algorithm synthesizes the best path toward the optimum. This paper demonstrates the use of LM optimization algorithm when there are more than one dataset and on/off type switching of NN parameters is allowed. For each dataset a pre-selected set of parameters are allowed for modification and the proposed scheme reformulates the Jacobian under the switching mechanism. The results show that a NN can store information available in different datasets by a simple modification to the original LM algorithm, which is the novelty introduced in this study. The results are verified on a regression problem. IEEE

Marmara University, 10th International Conference on Electrical and Electronics Engineering, ICEEE 2023 -- 8 May 2023 through 10 May 2023 -- 194296, This paper provides a reliability, availability and Mean Time Between Failure (MTBF) analysis for a sample DC/DC converter and Discrete I/O electronic circuit stages. These analyses were performed using the Failure Tree Analyses (FTA) approach. The reason for choosing these structures is that they are frequently used in today's electronic design. In this study, the effect of the importance of reliability on the whole system, starting from a component in electronic circuit design, has been demonstrated. © 2023 IEEE.

2023 IEEE Photonics Conference, IPC 2023 -- 12 November 2023 through 16 November 2023 -- 195842, In this paper, we introduce the design approach of integrated photonic devices by employing reinforcement learning known as attractor selection (AttSel). Here, we combined 3D FDTD with AttSel algorithm, which is based on artificial neural networks, to achieve ultra-compact and highly efficient wavelength demultiplexers with low crosstalk such as. The presented devices consist of SOI materials, which are compatible with complementary MOS technology. Consequently, the reinforcement learning is successfully applied to design smaller and superior integrated photonic devices. © 2023 IEEE.

9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 -- 10 December 2023 through 13 December 2023 -- 196947, A grid of orthogonal beams with zero column coherence can be easily constructed to cover all prospective user equipments (UEs) in the far-field region of a multiple-antenna base station (BS). However, when the BS is equipped with an extremely large aperture array, the Fraunhofer distance is huge, causing the UEs to be located in the radiative near-field region. This calls for designing a grid of beams based on a near-field dictionary. In the previous work, a polar-domain grid design was proposed to maintain control over the column coherence. A limitation of this approach is identified in this paper, and we propose an enhanced methodology for the design of a polar-domain dictionary specifically tailored for the near-field of an extremely large aperture uniform planar array. Through simulation results, it is demonstrated that the proposed dictionary, employing a non-uniform distance sampling approach, achieves lower column coherence than the benchmark and significantly improves the local-ization of UEs compared to uniform distance sampling. © 2023 IEEE., Stiftelsen för Strategisk Forskning, SSF

For the energy-efficient deployment of cell-free massive MIMO functionality in a practical wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is essential. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture. We compare two different functional split options and different resource orchestration mechanisms. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: i) ;#x201C;radio-only;#x201D; where radio resources are optimized independently from the cloud and ii) ;#x201C;local cloud coordination;#x201D; where orchestration is only allowed among a local cluster of radio units. We develop several algorithms to solve the end-to-end power minimization and sum spectral efficiency maximization problems. The numerical results demonstrate that end-to-end resource allocation with fully virtualized fronthaul and cloud resources provides a substantial additional power saving than the other resource orchestration schemes. IEEE

Monitoring is a key feature to enhance systems with the capability to anticipate, detect, predict, and mitigate failures, while providing Quality of Service (QoS) monitoring and Service Level Agreements (SLAs) guarantee. Monitoring frameworks can serve these purposes by deploying probes according to many possible patterns that have different features, for instance in terms of efficiency and privacy. So far, these probe deployment patterns have not been systematically defined, analyzed and assessed. Thus, engineers who design and configure their monitoring systems have to take decisions only based on partial knowledge and personal experience. This paper addresses this knowledge gap, by presenting a systematic analysis of 11 probe deployment patterns, their known uses, and implementations. We assess these patterns qualitatively, and quantitatively using both VMs and containers. Results show the targets have negligible resource consumption (e.g., less than 1% CPU usage), while the probe holder consumption is mainly significant in relation to memory consumption, reaching up to 10 GiB in our experiments. Our findings suggest that reusing probes and holders among users can generally enhance efficiency and scalability when direct access to the monitored target is not an option. We generate a set of best practices that can assist engineers in configuring their monitoring systems. Finally, we showcase the application of certain patterns through three practical usage scenarios, which feature diverse technologies and requirements.

Data enrichment facilitates the creation of rich, expressive, and high-quality datasets, enabling valuable analytics and enhanced decision-making. The accurate geolocation of residential addresses and travel routes is crucial for determining the most appropriate locations of critical social infrastructure, such as educational and medical centres. This paper introduces an interactive semantic enrichment approach that enhances urban data by integrating high-quality geospatial information. The approach is supported by a modular and extensible data enrichment framework, which leverages existing geolocation services, enabling seamless data integration. Human-in-the-loop revision is employed to enhance the quality of geocoding results. A real-world pilot study conducted in Sofia, Bulgaria, was used to validate this approach, demonstrating its promising potential in addressing pressing issues in parametric urban planning.

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-Term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. © 2002-2012 IEEE.

5th International Conference on Problems of Cybernetics and Informatics, PCI 2023 -- 28 August 2023 through 30 August 2023 -- 195003, This study considers a non-linear Cramér-Lundberg model of the risk theory and investigates the adjustment coefficient when the claims have gamma distribution. The ruin probability of this non-linear risk model is considered when the premium function is square root of time. Thus, in this study, the adjustment coefficient is explored by numerical methods and proposed an approximate formula for practical calculation of adjustment coefficient. Moreover, an implementation of the obtained approximate formula, which investigates ruin probability, is included as an example at the end of the paper. © 2023 IEEE.

Levenberg-Marquardt (LM) algorithm is a powerful approach to optimize the parameters of a neural network (NN). Given a training dataset, the algorithm synthesizes the best path toward the optimum. This paper demonstrates the use of LM optimization algorithm when there are more than one dataset and on/off type switching of NN parameters is allowed. For each dataset a pre-selected set of parameters are allowed for modification and the proposed scheme reformulates the Jacobian under the switching mechanism. The results show that a NN can store information available in different datasets by a simple modification to the original LM algorithm, which is the novelty introduced in this study. The results are verified on a regression problem. IEEE

Marmara University, 10th International Conference on Electrical and Electronics Engineering, ICEEE 2023 -- 8 May 2023 through 10 May 2023 -- 194296, This paper provides a reliability, availability and Mean Time Between Failure (MTBF) analysis for a sample DC/DC converter and Discrete I/O electronic circuit stages. These analyses were performed using the Failure Tree Analyses (FTA) approach. The reason for choosing these structures is that they are frequently used in today's electronic design. In this study, the effect of the importance of reliability on the whole system, starting from a component in electronic circuit design, has been demonstrated. © 2023 IEEE.

2023 IEEE Photonics Conference, IPC 2023 -- 12 November 2023 through 16 November 2023 -- 195842, In this paper, we introduce the design approach of integrated photonic devices by employing reinforcement learning known as attractor selection (AttSel). Here, we combined 3D FDTD with AttSel algorithm, which is based on artificial neural networks, to achieve ultra-compact and highly efficient wavelength demultiplexers with low crosstalk such as. The presented devices consist of SOI materials, which are compatible with complementary MOS technology. Consequently, the reinforcement learning is successfully applied to design smaller and superior integrated photonic devices. © 2023 IEEE.

38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023 -- 11 September 2023 through 15 September 2023 -- 194295, Designing and optimizing deep models require managing large datasets and conducting carefully designed controlled experiments that depend on large sets of hyper-parameters and problem dependent software/data configurations. These experiments are executed by training the model under observation with varying configurations. Since executing a typical training run can take days even on proven acceleration fabrics such as Graphics Processing Units (GPU), properly managing training data, avoiding human error in configuration preparations and securing the repeatability of the experiments are of utmost importance. In this paper, we present two open source software tools that aim to achieve these goals, namely, a Dataset Manager (DatumAid) tool and a Training Automation Manager (OrchesTrain) tool. DatumAid is a software tool that integrates with Computer Vision Annotation Tool (CVAT) to facilitate the management of annotated datasets. By adding additional functionality, DatumAid allows users to filter labeled data, manipulate datasets, and export datasets for training purposes. The tool adopts a simple code structure while providing flexibility to users through configuration files. OrchesTrain aims to automate model training process by facilitating rapid preparation and training of models in the desired style for the intended tasks. Users can seamlessly integrate their models prepared in the PyTorch library into the system and leverage the full capabilities of OrchesTrain. It enables the simultaneous or separate usage of Wandb, MLflow, and TensorBoard loggers. To ensure reproducibility of the conducted experiments, all configurations and codes are saved to the selected logger in an appropriate structure within a YAML file along with the serialized model files. Both software tools are publicly available on GitHub. © 2023 IEEE.

9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 -- 10 December 2023 through 13 December 2023 -- 196947, A grid of orthogonal beams with zero column coherence can be easily constructed to cover all prospective user equipments (UEs) in the far-field region of a multiple-antenna base station (BS). However, when the BS is equipped with an extremely large aperture array, the Fraunhofer distance is huge, causing the UEs to be located in the radiative near-field region. This calls for designing a grid of beams based on a near-field dictionary. In the previous work, a polar-domain grid design was proposed to maintain control over the column coherence. A limitation of this approach is identified in this paper, and we propose an enhanced methodology for the design of a polar-domain dictionary specifically tailored for the near-field of an extremely large aperture uniform planar array. Through simulation results, it is demonstrated that the proposed dictionary, employing a non-uniform distance sampling approach, achieves lower column coherence than the benchmark and significantly improves the local-ization of UEs compared to uniform distance sampling. © 2023 IEEE., Stiftelsen för Strategisk Forskning, SSF

For the energy-efficient deployment of cell-free massive MIMO functionality in a practical wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is essential. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture. We compare two different functional split options and different resource orchestration mechanisms. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: i) ;#x201C;radio-only;#x201D; where radio resources are optimized independently from the cloud and ii) ;#x201C;local cloud coordination;#x201D; where orchestration is only allowed among a local cluster of radio units. We develop several algorithms to solve the end-to-end power minimization and sum spectral efficiency maximization problems. The numerical results demonstrate that end-to-end resource allocation with fully virtualized fronthaul and cloud resources provides a substantial additional power saving than the other resource orchestration schemes. IEEE

In this paper, we devise a deep SARSA reinforcement learning (DSRL) user scheduling algorithm for a base station (BS) that uses a high-altitude platform station (HAPS) as a backup to serve multiple users in a wireless cellular network. Considering a realistic scenario, we assume that only the outdated channel state information (CSI) of the terrestrial base station (TBS) is available in our defined user scheduling problem. We model this user scheduling problem using a Markov decision process (MDP) framework, aiming to maximize the sum-rate while minimizing the number of active antennas at the HAPS. Our performance analysis shows that the sum-rate obtained with our proposed DSRL algorithm is close to the optimal sum-rate achieved with an exhaustive search method. We also develop a heuristic optimization method to solve the user scheduling problem at the BS. We show that for a scenario where perfect CSI is not available, our proposed DSRL algorithm outperforms the heuristic optimization method.

As the cloud moves from monolithic infrastructure to a self-isolated cloud native microservice environment, automation is becoming an important aspect for the management of the application life cycle. In this context, there are many tools available that can monitor these applications and raise alarms. However, automated orchestration is still in its early stages, and the available solutions are not capable of monitoring the whole system from application to hardware levels and performing automated operations within the system. Moreover, IP Multimedia Subsystem (IMS), which is the core part of the Telecom industry, has switched to a microservice environment. These IMS services are critical and need to be proactively monitored to provide automated orchestration operations. In this paper, we address the aforementioned problem by proposing a new scheme for monitoring the metrics from different sources and proactively and automatically performing orchestration using machine learning while improving the scalability of the cloud native Virtual IMS. Experiments carried out with a real cloud-native IMS running in a kubernetes cluster explore the relevance, efficiency and scalability of the proposed scheme.

The GaN/SiC cascode device featuring a low-voltage (LV) enhancement-mode (E-mode) GaN HEMT and a highvoltage (HV) SiC JFET has the unique capability of delivering thermally stable threshold voltage, avalanche capability, zero reverse recovery charge (Qrr), and fast switching speed simultaneously. However, the lack of avalanche capability in the LV GaN HEMT presents a challenging issue, namely the negative gate overstress of the HV SiC JFET during the switching process, which raises reliability concerns. A clamping unit based on a Si Zener diode can provide gate protection for JFET but inevitably increases the switching loss significantly due to the reverserecovery process of the Si Zener diode. This paper presents a new gate protection design based on a Si bi-directional Zener diode that can clamp the gate voltage of JFET without introducing the reverse-recovery process. Therefore, the gate of JFET can be protected while the switching performance of the cascode device is not compromised, as verified by the experiment results IEEE

As a fundamental component of autonomous driving systems, motion planning has garnered significant attention from both academia and industry. This paper focuses on efficient and spatial-temporal optimal trajectory optimization in unstructured environments using compact convex approximations of vehicle shapes. Conventional approaches typically model the task as an optimal control problem by discretizing the motion process in state configuration space. However, this often results in a tradeoff between optimality and efficiency since generating high-quality motion trajectories often requires high-precision discretization of the dynamic process, which imposes a substantial computational burden. To address this issue, we leverage the differential flatness property of car-like robots to simplify the trajectory representation and analytically formulate the spatial-temporal joint optimization problem with flat outputs in a compact manner, while ensuring the feasibility of nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a collision-free driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community. IEEE

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We thirdly provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the pr

Performing accurate sensing in diverse environments is a challenging issue in wireless sensing technologies. Existing solutions usually require collecting a large number of samples to train a classifier for every environment, or further assume similar sample distribution between different environments such that a model trained in one environment can be transferred to another. In this paper, we propose RF-Siamese, an RFID-based gesture sensing approach that achieves comparable accuracy to existing solutions but requires only a few samples in each eivironment. RF-Siamese leverages Siamese networks to distinguish different gestures with only a small number of samples and is enhanced by several novel designs to achieve high accuracy in diverse environments. First, the network structure and parameters (e.g., loss function and distance metric) are carefully designed to be suitable for RFID gesture recognition. Second, a permutation-based dataset generation strategy is proposed to make full use of the collected samples to enhance the recognition accuracy. Third, a template matching method is proposed to extend the Siamese network to classify multiple gestures. Extensive experiments on commercial RFID devices demonstrate that RF-Siamese achieves a high accuracy of 0.93 with only one sample of each gesture when recognizing 18 different gestures, while state-of-the-art approaches based on transfer learning and meta learning achieve an accuracy of only 0.59 and 0.70, respectively. IEEE

This paper investigates an intelligent reflecting surfaces (IRS) aided anti-jamming communication strategy in the integrated terrestrial-satellite network (ITSN), where the IRS is exploited to mitigate jamming interference and enhance the integrated system communication performance. In such a network, the terrestrial network and satellite network are co-existing with a spectrum-sharing scheme in the presence of a multi-antenna jammer. We aim at maximizing the weighted sum rate (WSR) of all users by jointly optimizing the terrestrial beamformers and IRS phase shifts while considering the signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users. Different from the non-convex optimization techniques utilized in the IRS-related problem, a novel optimization-driven deep reinforcement learning (DRL) algorithm is proposed, which leverages both the robustness of model-free learning approaches and the efficiency of model-based optimization methods. In the optimization module of the proposed algorithm, we analyze the smart jammer under the unknown jamming model and derive a lower bound of the anti-jamming uncertainty, such that the IRS-aided anti-jamming problem can be solved by alteration method with second-order cone programming (SOCP) algorithm and semidefinite relaxation (SDR) technique. Simulation results demonstrate that the IRS can enhance the anti-jamming performance efficiently, and the proposed optimization-driven DRL algorithm can improve both the learning rate and the system performance compared with existing solutions.

Packet loss inferring can enable a transceiver to distinguish between channel impairment and collision for transmission failures, and thus can improve the network performance by exclusively performing rate adaptation or adjusting the medium access parameter. Machine learning methods from literature have shown great potential in producing models that can detect the loss causes over various network trace, however haven't considered accurate data-driven loss inferring on resource-constrained devices that cannot accommodate deep models. In this paper, we propose a novel packet loss inferring framework that can train lightweight models to distinguish between channel losses and collisions by learning the data trace from both simulation and real devices. Specifically, we first train a sophisticated teacher model based on extensive simulation datasets, whose knowledge is then transferred to a small student model that can be deployed on tiny device. The simulation-reality distillation is conducted via personalized trace from each client correspondingly, whose performance bound is analytically guaranteed. We have implemented our method on real testbed and show that the network access performance can be significantly improved, especially for sudden network variations. IEEE

This paper proposes a new closed-form design of wideband infinite impulse response digital integrators using numerical integration rules that constructed via trigonometric interpolation. The proposed method can be used to design integrators satisfying prescribed frequency-domain specifications. Design examples with various specifications are given. Simulation comparisons with existing methods are presented to demonstrate the effectiveness of the new design method. The proposed method is experimentally verified on two real applications, Rogowski current transducer and strapdown inertial navigation system. The results show that our method can efficiently improve the integration bandwidth under low sampling rates compared to existing ones, and hence is attractive for resource-constrained applications that require wideband integration.

Multiple-object tracking (MOT) is a crucial component in autonomous driving systems. However, inaccurate object detection is always the bottleneck for MOT. Most detectors are not designed to take the temporal information across consecutive frames into consideration. To take advantage of such information, we design a novel data representation, the spatio-temporal (ST) map, which collects a batch of detection results spatio-temporally, and we train a novel network, ST-TrackNet, to assign predicted track IDs to each positive detection across a sequence. With our ST map detection fed into the tracker, the correlation of objects between adjacent frames becomes prominent, which improves the performance of the tracker in the data association step. Moreover, the long-term trajectory in a sequence also helps to refine the detection results. We train and evaluate our network on the KITTI dataset, a CARLA simulation dataset, and a dataset recorded in a factory environment. Our approach generally achieves superior performance over the state-of-the-art. Note to Practitioners—We investigate the MOT problem in this paper. A spatio-temporal pipeline is proposed to provide a solution to this problem. Object detection results produced by off-the-shelf object detectors are used to form the proposed ST maps. In low signal-to-noise ratio (SNR) situations, our proposed framework can achieve more accurate and robust tracking results with more false-positives. Due to the simplicity and modular design of our framework, it can be applied directly after the detection stage to achieve the online tracking task. The proposed method is evaluated on several datasets, and the experimental results demonstrate its effectiveness. Our method can also be used for other autonomous driving applications, such as path planning and trajectory prediction. IEEE

Ride-hailing service (RHS) has become an important transportation mode in our daily life. Although many works have been proposed to improve RHS from different aspects, only few works focus on the selections of pick-up locations, where rider and driver meet and start a trip. In this paper, we present MPLRec, a data-driven pick-up location recommendation system that exploits riders' specific mobility demands, e.g. destination, and historical experiences to meet riders' travel requirements. MPLRec generates potential pick-up locations over the road network and characterizes them with rich features that describe a location from the riders' perspective. We also build spatio-temporal indexes to organize potential pick-up locations and historical data for facilitating online recommending. When processing an online recommendation request, MPLRec derives candidate pick-up locations and investigates them with materialized features, which are computed from historical order and trajectory data while considering rider's mobility demands. Based on these features, a novel scoring function is used to derive the best pick-up location for each request. Moreover, we implement an RHS simulator to evaluate MPLRec using large-scale practical ride-hailing datasets. Extensive experiments and simulations demonstrate the effectiveness and efficiency of MPLRec, which can complete each request within 0.5 s and largely reduce the ride-hailing costs when compared to baseline methods. IEEE

Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by >30% over fixed-line and by >50% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet.

Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by >30% over fixed-line and by >50% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet.

Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by >30% over fixed-line and by >50% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet.

During snowfall, the utility of the road infrastructure is critical. Roads must be effectively cleared to ensure access to important locations and services. In this paper, we present an end-to-end framework for snow removal vehicle routing based on road priority. We offer an artificial intelligence-based image-based approach for estimating snow depth and traffic volume on roads. For segments monitored by CCTV cameras, we exploit images and supervised learning models to perform this task. For unmonitored roads, we use the Graph Convolutional Network architecture to predict parameters in a semi-supervised manner. Following that, we assign priority weights to all graph edges as a function of image-based attributes and road categories. We test the method using a real-world example, simulating snow removal within a study area in Montreal, Quebec, Canada. As input for the framework, we collect CCTV image data and combine it with a 2D map. As a result, more efficient snow removal operation can be achieved by optimizing the trajectories of trucks based on the computer vision module outputs.

This paper focuses on enhancing the resilience of Power Park Modules (PPMs), connected to the grid via power electronic units, under asymmetrical voltage sag conditions. We particularly address three interconnected constraints crucial for Low-Voltage Ride-Through (LVRT) capability of PPMs: grid voltage support, overcurrent protection, and the DC-link voltage quality. Existing literature often overlooks the holistic consideration of these factors, prompting this study to introduce and compare three flexible LVRT strategies compliant with recent German grid codes. The proposed strategies use distinct approaches to reinforce the LVRT capability of the PPMs. Strategy A employs static gain factors to control positiveand negative-sequence reactive current (PSRC and NSRC) and the negative sequence of the active current (NSAC). Strategy B aims to enhance the DC-link voltage quality by eliminating real power oscillations through the flexible choice of NSAC and gain factors controlling PSRC and NSRC. Strategy C enhances DC-bus voltage quality by eliminating NSAC and employing flexible equal gain factors for PSRC and NSRC, mitigating both real and imaginary power oscillations. Testing on an inverter-based PPM, involving 400 kW PV module and 200 A, storage system, reveals the superior performance of strategy C. Additional tests evaluate Strategy C’s performance with var/voltage regulation equipment (On-Load-Tap-Changing transformer), demonstrating stable and promising performance. Through the introduced approaches, the authors believe that this research work is useful for developing future grid codes and improving the safety and reliability of grid-connected power systems.

Although many proposals have been developed for the sixth-generation (6G) technology, realizing 6G is fraught with numerous fundamental interdisciplinary, multidisciplinary, and transdisciplinary challenges. To mitigate some of these challenges, goal-oriented semantic communication (SemCom) has emerged as a promising 6G technology enabler. This enabler employs only semantically-relevant information for successful task execution while minimizing power usage, bandwidth consumption, and transmission delay. On the other hand, 6G is essential for realizing major goal-oriented SemCom use cases such as autonomous transportation. These paradigms of 6G for goal-oriented SemCom and goal-oriented SemCom for 6G call for a tighter integration of 6G and goal-oriented SemCom. To facilitate this purpose, this survey paper exposes the fundamental challenges of 6G; details the notion of goal-oriented SemCom and its stateof- the-art research landscape; presents state-of-the-art trends, use cases, and frameworks of goal-oriented SemCom; exposes the fundamental and major challenges of goal-oriented SemCom; and offers promising future research directions for goal-oriented SemCom. Consequently, this survey article stimulates numerous lines of research on goal-oriented SemCom theories, algorithms, and realization.

There is an increasing demand of massive machine-type communication (mMTC) to provide scalable access for a large number of devices, which has prompted extensive investigation on grant-free massive random access (RA) in 5G and beyond wireless networks. Although many efficient signal processing algorithms have been developed, the limited radio resource for pilot transmission in grant-free massive RA systems makes accurate user activity detection and channel estimation challenging, which thereby compromises the communication reliability. In this paper, we adopt retransmission as a means to improve the quality of service (QoS) for grant-free massive RA. Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm. Then, we analyze the performance of the proposed receiver, including the user activity detection, channel estimation, and data error, by resorting to the state evolution of the correlated AMP algorithm and the random matrix theory (RMT). Our analysis admits a tight closed-form approximation for frame error rate (FER) evaluation. Simulation results corroborate our theoretical analysis and demonstrate the effectiveness of the proposed receiver for grant-free massive RA with retransmission, compared with a conventional design that disregards the critical user activity correlation. IEEE

Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially for the complex channels not modeled as the well-known typical ones. Recently, neural networks have been used in mutual information estimation and optimization. They are particularly considered as efficient tools for learning channel capacity. In this paper, we propose a cooperative framework to simultaneously estimate channel capacity and design the optimal codebook. First, we will leverage MIM-based GAN, a novel form of generative adversarial network (GAN) using message importance measure (MIM) as the information distance, into mutual information estimation, and develop a novel method, named MIM-based mutual information estimator (MMIE). Then, we design a generalized cooperative framework for channel capacity learning, in which a generator is regarded as an encoder producing the channel input, while a discriminator is the mutual information estimator that assesses the performance of the generator. Through the adversarial training, the generator automatically learns the optimal codebook and the discriminator estimates the channel capacity. Numerical experiments will demonstrate that compared with several conventional estimators, the MMIE achieves state-of-the-art performance in terms of accuracy and stability. IEEE

Over-the-Air aggregation (OTA) is a promising technology for Internet-of-Things (IoT) applications, but it can be vulnerable to burst interference from co-channel non-cooperative IoT communications. In this paper, we propose a robust OTA framework for IoT-OTA applications that mitigates burst sparse interference using analog product code. We first propose a hierarchical likelihood model and a Markov-based hierarchical prior model to capture the structural properties in the received coded observations and the statistics of the burst-sparse interference, respectively. Then, we design a low-complexity Bayesian decoder using online turbo variational Bayesian inference technique, which ensures good performance without prior knowledge of the model parameters. In addition, we propose an optimization-based design for the analog product code encoding matrix that minimizes the end-to-end decoding mean squared error. Extensive simulations demonstrate the effectiveness of the proposed solution on interference mitigation, outperforming various state-of-the-art baseline schemes. © 1991-2012 IEEE.

This paper studies the optimal transmission scheduling for remote state estimation over multi-hop networks. A smart sensor observes a dynamic system, and sends its local state estimate to a remote estimator (RE). To save energy, multi-hop networks are deployed to relay data packets from the smart sensor to the RE. The smart sensor needs to decide the hop number communicating with the RE by adjusting its transmission power. To minimize the estimation error and the energy consumption, the transmission scheduling is formulated as a modified Markov decision process (MDP) by incorporating historical actions into the state. A sufficient condition is constructed to guarantee that the MDP has an optimal deterministic and stationary policy. The optimal policy's structure is further obtained to reduce the computation complexity. A deep reinforcement learning (DRL) algorithm, i.e., dueling double Q-network (D3QN), is introduced to obtain a near-optimal policy. Finally, a simulation example is provided to illustrate the developed results. IEEE

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we introduce several electromagnetic characteristics and general distance boundaries in XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further discuss and summarize signal processing schemes for XL-MIMO. It is worth noting that the low-complexity signal processing schemes and deep learning empowered signal processing schemes are reviewed and highlighted to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems. IEEE

Aiming at improving the operation of the bottleneck area of the highway in the environment of a connected and automated vehicle, this paper proposes an integrated lane-level control (ILC) method by combining the variable speed limit control method and lane selection method into a comprehensive framework. A lane-level variable speed limit (LVSL) control method is proposed for the mixed traffic flow based on a deep deterministic policy gradient algorithm. Then, a lane-level short-term traffic prediction (LSTP) model based on hybrid deep learning is built to forecast the traffic state in a next control horizon. Finally, a lane selection method using a dynamic programming algorithm is established for the connected automated vehicle (CAV) to look for the optimal lane-level route by considering the estimated traffic speeds and limit speeds from LSTP and LVSL respectively. Comprehensive simulation-based evaluation experiments were conducted in various scenarios e.g., with different traffic demands, penetration rates of CAVs, length of control horizons, and the number of lanes. The evaluation results reveal that the proposed LSTP model outperforms the state-of-the-art traffic prediction models in terms of accuracy and stability. In addition, the proposed ILC method is capable of improving the traffic operation of the bottleneck efficiently by synthetically taking the advantage of the LVSL method and lane selection method. Compared with the no-control strategies, the ILC method can reduce total travel time by more than 30% in various traffic scenarios.