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Multi-Variate Time Series Prediction of Traffic and Users for Dynamic RRH-BBU Mapping in C-RAN

IEEE Transactions on Mobile Computing 2025年

作者： Wu, Fan Wang, Shanshan Zhou, Jieyu Pan, Haoye Zhou, Conghao Yang, Wang Lyu, Feng Zhang, Yaoxue Central South University School of Computer Science and Engineering Changsha410083 China Rutgers University Department of Computer Science PiscatawayNJ United States University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada BNRist Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Cellular operators face significant challenges in cutting operating expenses while maintaining the quality of service (QoS) for users due to growing network traffic and dynamic user connections. These challenges are addressed by the cloud radio access network (C-RAN) architecture, which includes a centralized pool of baseband units (BBUs) and distributes them from remote radio heads (RRHs). The key to improving C-RAN performance is to dynamically allocate large-scale RRHs to different BBUs in real time. In this paper, we propose a user behavior-aware RRH-BBU mapping framework to improve the performance of large-scale C-RANs by predicting RRH traffic and users in advance. First, we propose a Multivariate RRH time series Prediction Model (MRPM) that captures the spatio-temporal patterns in the data to predict the traffic volume and the number of users of RRHs, which represents key indicators of RRH connection states. Second, we formulate the RRH-BBU mapping as a Markov decision process problem to optimize cost and QoS by considering BBU utilization, BBU energy consumption, RRH migration frequency, and BBU load balancing. Third, we propose a prediction-based RRH-BBU mapping scheme (PB-RBM) to find the optimal RRH-BBU mapping strategy by leveraging the prediction information of MRPM. In the PB-RBM algorithm, we employ an A3C algorithm to learn the mapping policy and group the RRHs based on a defined popularity metric to reduce the state and action space of the reinforcement learning algorithm. Finally, extensive experiments are conducted on a real-world dataset, and our algorithm is compared with several matching algorithms, such as ACKTR, heuristic, etc., to demonstrate its superiority, especially reducing 17.5% in RMSE compared to the best-performing baseline. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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RIS-Assisted Physical Layer Security in Emerging RF and Optical Wireless Communication Systems: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Khoshafa, Majid H. Maraqa, Omar Moualeu, Jules M. Aboagye, Sylvester Ngatched, Telex M.N. Ahmed, Mohamed H. Gadallah, Yasser Di Renzo, Marco The Department of Electrical and Computer Engineering McMaster University HamiltonONL8S 4L8 Canada The School of Electrical and Information Engineering University of the Witwatersrand Johannesburg2000 South Africa The School of Engineering University of Guelph GuelphONN1G 2W1 Canada The School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada The Department of Electronics and Communications Engineering The American University in Cairo New Cairo11835 Egypt Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systémes Gif-sur-Yvette91192 France

Security and latency are crucial aspects in the design of future wireless networks. Physical layer security (PLS) has received a growing interest from the research community in recent years for its ability to safeguard data confidentiality without relying on key distribution or encryption/decryption, and for its latency advantage over bit-level cryptographic techniques. However, the evolution towards the fifth generation (5G) technology and beyond poses new security challenges that must be addressed in order to fulfill the unprecedented performance requirements of future wireless communication networks. Among the potential key-enabling technologies, reconfigurable intelligent surface (RIS) has attracted extensive attention due to its ability to proactively and intelligently reconfigure the wireless propagation environment to combat dynamic wireless channel impairments. Consequently, the RIS technology can be adopted to improve the information-theoretic security of both radio frequency (RF) and optical wireless communications (OWC) systems. It is worth noting that the configuration of RIS in RF communications is different from the one in optical systems at many levels (e.g., RIS materials, signal characteristics, and functionalities). This survey paper provides a comprehensive overview of the information-theoretic security of RIS-based RF and optical systems. The article first discusses the fundamental concepts of PLS and RIS technologies, followed by their combination in both RF and OWC systems. Subsequently, some optimization techniques are presented in the context of the underlying system model, followed by an assessment of the impact of RIS-assisted PLS through a comprehensive performance analysis. Given that the computational complexity of future communication systems that adopt RIS-assisted PLS is likely to increase rapidly as the number of interactions between the users and infrastructure grows, machine learning (ML) is seen as a promising approach to address

关键词： 5G mobile communication systems

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Ornstein-Uhlenbeck process and generalizations: Particle dynamics under comb constraints and stochastic resetting

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Physical Review E 2023年第5期107卷 054129-054129页

作者： Pece Trajanovski Petar Jolakoski Kiril Zelenkovski Alexander Iomin Ljupco Kocarev Trifce Sandev Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 1000 Skopje Macedonia Department of Physics Technion Haifa 32000 Israel Max Planck Institute for the Physics of Complex Systems D-01187 Dresden Germany Faculty of Computer Science and Engineering Ss. Cyril and Methodius University PO Box 393 1000 Skopje Macedonia Institute of Physics & Astronomy University of Potsdam D-14776 Potsdam-Golm Germany Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 1000 Skopje Macedonia

The Ornstein-Uhlenbeck process is interpreted as Brownian motion in a harmonic potential. This Gaussian Markov process has a bounded variance and admits a stationary probability distribution, in contrast to the standard Brownian motion. It also tends to a drift towards its mean function, and such a process is called mean reverting. Two examples of the generalized Ornstein-Uhlenbeck process are considered. In the first one, we study the Ornstein-Uhlenbeck process on a comb model, as an example of the harmonically bounded random motion in the topologically constrained geometry. The main dynamical characteristics (as the first and the second moments) and the probability density function are studied in the framework of both the Langevin stochastic equation and the Fokker-Planck equation. The second example is devoted to the study of the effects of stochastic resetting on the Ornstein-Uhlenbeck process, including stochastic resetting in the comb geometry. Here the nonequilibrium stationary state is the main question in task, where the two divergent forces, namely, the resetting and the drift towards the mean, lead to compelling results in the cases of both the Ornstein-Uhlenbeck process with resetting and its generalization on the two-dimensional comb structure.

关键词： Brownian motion Diffusion Fluctuations & noise Noise Random walks Stochastic processes

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Employing Total Ionizing Dose Effect in PN-Junction Photodetectors Implemented in Standard CMOS Technology for Dosimetry Application

Employing Total Ionizing Dose Effect in PN-Junction Photodet...

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2021 IEEE Nuclear science Symposium and Medical Imaging Conference, NSS/MIC 2021

作者： Khalili, Razieh Pil-Ali, Abdollah Adnani, Sahar Karami, Mohammad Azim University of Guilan Faculty of Sciences Rasht Iran Department of Electrical and Computer Engineering Centre for Bioengineering and Biotechnology University of Waterloo 200 University Ave W WaterlooONN2L3G1 Canada School of Electrical Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781665421133

Radiation effects on electronic devices are usually considered as a significant source of degradation to the point where they can cause device failure. The effect of total ionizing dose, which is a permanent effect, affects the performance of electronic devices by changing their current or voltage characteristics. In photodetectors specifically, increasing the effect of total ionizing dose leads to higher dark currents. Although this negative radiation effect is avoided in designing, there are applications that potentially benefit from it. Radiation dosimetry is one of many applications in which the effect of radiation on devices is used for monitoring purposes. In this work, we present the feasibility of utilizing the total ionizing dose effect for radiation dosimetry. A PN-junction based photodetector implemented in 180 nm standard complementary-metal-oxide-semiconductor technology is simulated in Silvaco TCAD, and its dark current under different gamma-ray radiation up to 1 Mrad is studied. Oxide-trapped charges and interface states formed by the total ionizing dose effect induce more dark current, which is tabulated for different gamma-ray radiation doses up to 1 Mrad. A transimpedance amplifier - a picoammeter circuit - is integrated with the photodetector to monitor and measure different levels of radiation to demonstrate the conceptual design of the proposed dosimeter. © 2021 IEEE.

关键词： Metals

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MICAL: Mutual Information-Based CNN-Aided Learned Factor Graphs for Seizure Detection from EEG Signals

arXiv

引用

arXiv 2022年

作者： Salafian, Bahareh Ben-Knaan, Eyal Fishel Shlezinger, Nir De Ribaupierre, Sandrine Farsad, Nariman The Department of Computer Science Ryerson University TorontoONM5B 2K3 Canada The School of Electrical and Computer Engineering Ben-Gurion University of the Negev Be'er Sheva84105 Israel Department of Clinical Neurological Sciences The School of Biomedical Engineering University of Western Ontario ONN6A 5B9 Canada

We develop a hybrid model-based data-driven seizure detection algorithm called Mutual Information-based CNN-Aided Learned factor graphs (MICAL) for detection of eclectic seizures from EEG signals. Our proposed method contains three main components: a neural mutual information (MI) estimator, 1D convolutional neural network (CNN), and factor graph inference. Since during seizure the electrical activity in one or more regions in the brain becomes correlated, we use neural MI estimators to measure inter-channel statistical dependence. We also design a 1D CNN to extract additional features from raw EEG signals. Since the soft estimates obtained as the combined features from the neural MI estimator and the CNN do not capture the temporal correlation between different EEG blocks, we use them not as estimates of the seizure state, but to compute the function nodes of a factor graph. The resulting factor graphs allows structured inference which exploits the temporal correlation for further improving the detection performance. On public CHB-MIT database,We conduct three evaluation approaches using the public CHB-MIT database, including 6-fold leave-four-patients-out cross-validation, all patient training;and per patient training. Our evaluations systematically demonstrate the impact of each element in MICAL through a complete ablation study and measuring six performance metrics. It is shown that the proposed method obtains state-of-the-art performance specifically in 6-fold leave-four-patients-out cross-validation and all patient training, demonstrating a superior generalizability. © 2022, CC BY.

关键词： Convolutional neural networks

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Feasibility of deep learning to predict tinnitus patient outcomes

引用

Intelligence-Based Medicine 2024年 9卷 100141-100141页

作者： Adcock, Katherine S. Byczynski, Gabriel Meade, Emma Leong, Sook Ling Gault, Richard Lim, Hubert Vanneste, Sven Trinity Institute of Neuroscience Trinity College Dublin Ireland Neuromod Devices Limited Dublin D08 R2YP Ireland School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Department of Biomedical Engineering University of Minnesota Minneapolis 55455 MN United States

Advances in machine and deep learning techniques provide a novel approach in understanding complex patterns within large datasets, leading to an implementation of personalized medicine approaches to support clinical decision making. Results from recent clinical trials (TENT-A1 and TENT-A2 studies;***: NCT02669069 and NCT03530306) support that a novel bimodal neuromodulation approach could be a breakthrough treatment for patients with tinnitus, which adversely affects 10–15 % of the population. Given the heterogeneity of symptoms, it is important to identify whether treatment has an optimal effect on specific subgroups of tinnitus patients. The current study is a first look at the feasibility of using deep learning modelling on patient reported data to predict treatment outcomes in individuals with tinnitus, and highlights what features are most beneficial for clinical decision making. © 2024 The Authors

关键词： Deep learning Personalized medicine Tinnitus

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Determination of the spectral dispersion for the heart muscle – a Kramers-Kronig approach

Determination of the spectral dispersion for the heart muscl...

引用

International Conference on Laser Optics

作者： Luís M. Oliveira Maria R. Pinheiro Hélder P. Oliveira Maria I. Carvalho Valery V. Tuchin Physics Department Rua Dr. António Bernardino de Almeida 431 Polytechnic of Porto School of Engineering Porto Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Rua Dr. Roberto Frias Porto Portugal Department of Computer Science Rua do Campo Alegre 1021 Faculty of Science Porto University Porto Portugal Faculty of Engineering Rua Dr. Roberto Frias Porto University Porto Portugal Institute of Physics and Science Medical Center Saratov State University 83 Astrakhanskaya str. Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning Tomsk State University 36 Lenin ave. Tomsk Russia RC “Biotechnology of the Russian Academy of Sciences” 33 Leninsky prospect build 2 A.N. Bach Institute of Biophotonics Moscow Russia

ISBN: (数字)9798350390674

ISBN: (纸本)9798350390681

The refractive index of the pigs heart was measured at wavelengths between 255 and 850 nm to calculate the dispersion. The total transmittance and total reflectance spectra of the pig heart were measured between 200 and 1000 nm to calculate the spectral absorption coefficient. Using Kramers-Kronig relations, the dispersion of the heart was matched to experimental refractive index values.

关键词： Heart Reflectivity Absorption Wavelength measurement Refractive index Measurement by laser beam Muscles

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zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning

arXiv

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arXiv 2023年

作者： Wang, Zhipeng Dong, Nanqing Sun, Jiahao Knottenbelt, William Guo, Yike The Department of Computing Imperial College London LondonSW7 2AZ United Kingdom The Shanghai Artificial Intelligence Laboratory Shanghai200232 China FLock.io LondonWC2H 9JQ United Kingdom The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom

Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution in big data scenarios. Traditional FL relies on the trust assumption of the central aggregator, which forms cohorts of clients honestly. However, a malicious aggregator, in reality, could abandon and replace the client’s training models, or insert fake clients, to manipulate the final training results. In this work, we introduce zkFL, which leverages zero-knowledge proofs to tackle the issue of a malicious aggregator during the training model aggregation process. To guarantee the correct aggregation results, the aggregator provides a proof per round, demonstrating to the clients that the aggregator executes the intended behavior faithfully. To further reduce the verification cost of clients, we use blockchain to handle the proof in a zero-knowledge way, where miners (i.e., the participants validating and maintaining the blockchain data) can verify the proof without knowing the clients’ local and aggregated models. The theoretical analysis and empirical results show that zkFL achieves better security and privacy than traditional FL, without modifying the underlying FL network structure or heavily compromising the training speed. © 2023, CC BY.

关键词： Blockchain

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IRS-Enhanced UAV Communication Networks: Securing Data with Hybrid Genetic and Gradient Descent Algorithms

IRS-Enhanced UAV Communication Networks: Securing Data with ...

引用

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Chkirbene, Zina Gouissem, Ala Hamila, Ridha Devrim, Unal Al-Dweik, Arafat Kuru, Kaya Qatar University Qatar College of Computing and Information Technology University of Doha for Science and Technology Qatar College of Engineering Qatar University Kindi Center for Computing Research Qatar Khalifa University 4 Ku 6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates School of Engineering University of Central Lancashire PrestonPR1 2HE United Kingdom

ISBN: (纸本)9798350368369

In the rapidly advancing field of wireless communication, Unmanned Aerial Vehicles (UAVs) have become indispensable due to their extensive coverage capabilities and ability to access remote locations. Whether deployed as mobile base stations (BSs) or relays, UAVs significantly enhance network throughput and reliability. Alongside UAVs, Intelligent Reflecting Surfaces (IRS) have emerged as a cost-effective solution for improving communication quality through passive modulation arrays. Despite these advancements, the potential misuse of UAVs poses serious security risks, particularly in the form of communication eavesdropping. To address these challenges, this paper introduces a novel communication framework that integrates a UAV equipped with an adaptive IRS. The primary aim is to boost communication secrecy between BSs and multiple users, even in the presence of several UAV eavesdroppers. This objective is formulated as an optimization problem focused on maximizing the secrecy rate while considering UAV mobility constraints. To solve this non-convex problem, we propose a hybrid strategy that combines Genetic Algorithms and Gradient Descent techniques. This innovative approach efficiently determines suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs, thereby enhancing the security of the communication network. This method not only addresses the complexity of the optimization but also provides a practical pathway to secure communications in environments with high eavesdropping risks. © 2025 IEEE.

关键词： Drones

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Morpho-Mechanical Analysis of Porcine Growth Plate Tissue Mechanics Under Torsional Shear

SSRN

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SSRN 2024年

作者： Hucke, Lucie Quelhas Teixeira, Graciosa Seitz, Andreas Martin Gámez, Antonio Huß, Armin Hammer, Niels Wittek, Andreas Niestrawska, Justyna A. Personalized Biomedical Engineering Laboratory Department 2: Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany Department of Mechanical Engineering and Industrial Design School of Engineering Universidad de Cádiz Cádiz Spain Division of Macroscopic and Clinical Anatomy Gottfried Schatz Research Center Medical University of Graz Graz Austria Institute of Orthopedic Research and Biomechanics Ulm University Medical Centre Ulm Germany Department of Orthopaedic and Trauma Surgery University of Leipzig Leipzig Germany Fraunhofer IWU Dresden Germany

This study aimed to investigate the microstructural and mechanical properties of growth plate tissue under torsional loading, focusing on variations across individuals, growth plate types, and anatomical locations.A total of 398 samples from three distinct growth plates in five porcine subjects were subjected to cyclic torsion tests followed by ultimate failure testing. Additionally, histological analyses were performed using Movat's pentachrome staining to assess tissue *** testing revealed significant differences in shear moduli across growth plate types;notably, the proximal femur exhibited a higher primary shear modulus compared to both the distal femur and proximal tibia. Correlation analyses showed a negative relationship between hypertrophic zone thickness and primary shear modulus (rho = -0.49 at 0.5 °/s, p © 2024, The Authors. All rights reserved.

关键词： Shear strain

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