Published paper on Conditional handover for 5G networks with obstacles

Check this newly published article "Identifying Vital Nodes by Local g-Core on Symmetric Networks" at https://brnw.ch/21wWt9x Authors: Letian Wang The H-index is a widely recognized centrality measure for nodes in symmetric networks, defined as the maximum number of neighbors with degrees equal to or greater than the node’s own degree. However, this metric underestimates the structural influence of “weak nodes”—low-degree nodes connected to high-degree hubs—that often serve as critical connectors in network topology. To address this limitation, we propose the 𝐻𝛼-index, which generalizes the H-index by considering the maximum number of neighbors with degrees at least 𝛼 times the node’s degree, where 𝛼≥1. Based on this refinement, we introduce two novel centrality measures: the g-core and the local g-core, which were derived from iterative applications of the 𝐻𝛼-index to a node’s neighbors... 山东大学 #mdpisymmetry #complexnetworks

💡Article spotlight: Distributed Beamforming Using Decentralized Time Synchronization in a Six-Element Array This paper discusses a method for highly synchronized timing of signals emanating from a set of non-colocated antennas to produce a coherent output that can be power combined or beam steered. The algorithm works by correcting and optimizing both circuit timing errors as well as frequency drift and gain variations. The authors demonstrate the scheme on a 6-element distributed antenna array transmitting at 1 GHz and produce a near-ideal coherent beam more than 16 meters away through an all-wireless optimization loop working at 2.1 GHz. This paper is from our September 2025 issue. 📎 Read the full article here: https://lnkd.in/gsmGvgGX  🌐 Topics covered in this article: Consensus averaging, distributed antenna arrays, distributed beamforming, distributed collaborative beamforming, distributed phased arrays, synchronization, wireless networks 📝 Authors: Naim Shandi, Jason Merlo, Jeffrey Nanzer

We're happy to share the acceptance of our two recent papers: 1. Data-Driven Context-Aware Traffic Prediction and Modeling for Tactile Internet (accepted at European Wireless 2025): In this work, we propose a Markov-based modeling approach for traffic patterns in the Tactile Internet, complemented by a data-driven traffic predictor. This research was carried out in collaboration with Prof. Domenico Prattichizzo and Selin Nur Ö. from SIRSLab, University of Siena, Italy, as part of the TOAST project. (LInk: https://lnkd.in/eB2YZ3ph) 2. Optimistic Constrained Online Convex Optimization for Resource Reservation in Tactile Internet (accepted at IEEE GLOBECOM 2025). Here, we integrate our proposed traffic predictor into the Optimistic Constrained Online Convex Optimization framework for resource scheduling in the Tactile Internet. Our results demonstrate a better performance compared to non-optimistic methods. (Link: https://lnkd.in/ed5QhQXQ) I would like to sincerely thank my supervisor, Prof. Salah Eddine EL AYOUBI, and my co-supervisor, Dr. Vineeth S Varma, for their invaluable guidance and continuous support throughout these works.

Happy to share our recent accepted papers (led by Ruide and 郭斌全), on game theory in Open RAN and scheduling federated learning over satellite networks, in IEEE GLOBECOM 2025 and IEEE TrustCom 2025 respectively! 𝐀 𝐔𝐬𝐞𝐫-𝐭𝐨-𝐔𝐬𝐞𝐫 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐑𝐞𝐬𝐞𝐥𝐥𝐢𝐧𝐠 𝐆𝐚𝐦𝐞 𝐢𝐧 𝐎𝐩𝐞𝐧 𝐑𝐀𝐍 𝐰𝐢𝐭𝐡 𝐁𝐮𝐟𝐟𝐞𝐫 𝐑𝐨𝐥𝐥𝐨𝐯𝐞𝐫 Ruide Cao, Marie Siew, David Yau IEEE Globecom 2025. In this paper, we propose a user-to-user resource reselling model for Open RAN that improves efficiency by reducing data loss and spectrum wastage. Our model models the carryover of unmet demand across time slots, along with how users’ internal buffer states relate to any PRBs purchased. https://lnkd.in/gqQKyqcY 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐋𝐚𝐫𝐠𝐞-𝐒𝐜𝐚𝐥𝐞 𝐋𝐄𝐎 𝐒𝐚𝐭𝐞𝐥𝐥𝐢𝐭𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Binquan Guo, Junteng Cao, Marie Siew, Binbin Chen, Tony Q. S. Quek, Zhu Han IEEE TrustCom 2025 https://lnkd.in/gcDwHZ5p We propose a discrete temporal graph–based on-demand scheduling framework to accelerate Federated Learning in large-scale Low-Earth-Orbit (LEO) satellite systems, addressing the prolonged training times caused by limited bandwidth and dynamic network topologies.

I am proud to share that my latest research paper, “Machine Learning-Based Proactive Fault Monitoring and Prediction in GPON Networks,” has been published in Modern Applied Science by the Canadian Center of Science and Education. This work introduces a proactive fault detection approach for GPON networks using machine learning to predict and classify network faults before service disruption occurs. The study demonstrates high accuracy and real-world effectiveness in improving network reliability. You can read the full paper here: https://lnkd.in/gvX3FYbV

The paper titled 'OAM-LiFiNet: Orientation Aware Multi-AP Li-Fi Network with User Mobility" authored by Saswati Paramita, A. Rawat, and Vivek Ashok Bohara has been accepted in  IEEE Future Networks World Forum 2025 (FNWF'25). Abstract—Li-Fi networks provide high indoor throughput but face challenges due to their reliance on line-of-sight (LoS) and limited coverage. User mobility can degrade throughput as movement beyond a single access point (AP) reduces signal quality, while deploying multiple APs introduces interference and signal fluctuations. This paper uses the random waypoint mobility model to analyze how device orientation impacts signal-to-interference-plus-noise ratio (SINR) and throughput in dynamic indoor environments. Results show that orienting user devices toward a Li-Fi AP enhances SINR in high-interference areas, while a non-oriented state improves throughput in low-interference regions by preserving LoS. The proposed strategy dynamically adjusts device orientation based on real-time SINR and channel conditions, balancing interference reduction and LoS preservation. This adaptive approach optimizes network performance under varying mobility and interference scenarios, improving throughput and reliability in Li-Fi networks.

On 14th August 2025, the Physical Sciences Data Infrastructure (PSDI) hosted a webinar titled “Seekest thou the road to, all that’s digital and FAIR – the Ballad of the ELN Journey”, presented by Dr. Samantha Pearman-Kanza,. The session explores the sociotechnical challenges of implementing Electronic Lab Notebooks (ELNs) to achieve FAIR (Findable, Accessible, Interoperable, Reusable) data in research labs. For those who missed the live session or wish to revisit the discussion, the full webinar is available on our YouTube channel: https://lnkd.in/eVyucgtx The slides are available on Zenodo: https://lnkd.in/e-rKQYfP

#Article Relations between Shot Noise, Gain Bandwidth, and Saturation of Instabilities by Yichao Jing, Vladimir N. Litvinenko, Jun Ma and Gang Wang https://lnkd.in/eEBikDyy MDPI Brookhaven National Laboratory Stony Brook University #highbrightness #highintensitybeams #collectiveeffectsandinstabilities #saturationofbeaminstabilities #Abstract There are numerous instabilities present in charged particle beams that undergo exponential growth and reach saturation. In various applications, such as free-electron lasers or micro-bunching light sources, achieving saturation is desirable. Conversely, there are applications where these instabilities are utilized as linear broad-band amplifiers for signals embedded in the charged beam. In the latter scenario, the saturation of an instability induces non-linear distortions in the imprinted signal, thereby limiting the useful range of such amplifiers. Accurate evaluation of these instabilities necessitates a complete and comprehensive modeling approach that includes shot noise within the beam. Unfortunately, such modeling is not always feasible or practical. In this paper, we introduce a methodology utilizing the frequency and bandwidth of the instability as key parameters. Through this, we derive an estimation for the range of linear instability growth. Our derivation is conducted in a model-independent manner, making it applicable to a broad spectrum of instabilities. To validate our approach, we employ established and thoroughly benchmarked simulations with a free electron laser (FEL) code as well as self-consistent 3-dimensional simulation of plasma-cascade instability using code SPACE.

In Macroscopic Fundamental Diagram (MFD)-based traffic management, identifying homogeneous and connected network regions is crucial for reliable monitoring, prediction, and control. But how should urban networks be clustered? Which methods best balance homogeneity, connectivity, and computational efficiency? And how do these choices affect applications such as routing, congestion pricing, and incident detection?   In our latest paper by Yamam Alayasreih, Gabriel Tilg, Florian Dandl, Mehdi Keyvan-Ekbatani (University of Canterbury), and Klaus Bogenberger, published in Transportation Research Record, benchmarks machine learning clustering algorithms (k-means, GMM, DBSCAN, HAC) against state-of-the-art methods such as snake similarity and sensor-bias-corrected community detection.   This study provides practitioners with clear guidance on selecting the most suitable clustering approach for their specific traffic management objectives.   Open access at: https://lnkd.in/djMZg5GD

🎉 New paper in IEEE-TAC! Our paper "𝘈 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳𝘢𝘭 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘧𝘰𝘳 𝘓𝘗𝘝 𝘥𝘢𝘵𝘢-𝘥𝘳𝘪𝘷𝘦𝘯 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯𝘴" has just been published in the IEEE Transactions on Automatic Control 😁! 🔍In this work, we lay the foundations for achieving analysis and controller design of Linear Parameter-Varying (LPV) systems 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 from data! We use the celebrated behavioral approach (introduced by the legend Jan C. Willems) to construct finite horizon data-driven representations for LPV systems, with which we can directly perform analysis or control design. 🤔𝗦𝗼, 𝘄𝗵𝘆 𝗟𝗣𝗩 𝘀𝘆𝘀𝘁𝗲𝗺𝘀? The LPV framework has been serving as a bridge between LTI systems and the nonlinear realm, providing both rich representation capabilities and convex, intuitive tools. 𝗧𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝘁𝗵𝗮𝘁 𝗼𝘂𝗿 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗺𝗮𝘆 𝘀𝗲𝗿𝘃𝗲 𝗮𝘀 𝗮 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝘁𝗲𝗽 𝘁𝗼𝘄𝗮𝗿𝗱 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝗻𝗼𝗻𝗹𝗶𝗻𝗲𝗮𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀! I also want to thank my collaborators Ivan Markovsky, Sofie Haesaert, and Roland Toth! You can check out the paper here: https://lnkd.in/eS7mFz82 🤝Happy to chat if you want details, code, discuss possible collaboration, or explore how this could fit into your work! #ControlEngineering #Research #IEEE #Control #Behavior #DataDrivenControl