- Electrical and Electronic Engineering - Research Publications
Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableReconfigurable optical crosshaul architecture for 6G radio access networksTao, Y ; Ranaweera, C ; Edirisinghe, S ; Lim, C ; Nirmalathas, A ; Wosinska, L ; Song, T (Optica Publishing Group, 2023-12)The radio access network (RAN) architecture is undergoing a significant evolution to support the next-generation mobile networks and their emerging applications. To realize scalable and sustainable deployment and operations, RAN needs to consider the requirements of 6G and beyond wireless technologies such as ultra densification of cells, higher data rates, ubiquitous coverage, and new radio spectrum in the millimeter-wave band. This calls for a careful redesign of every aspect of RAN, including its crosshaul. The crosshaul is an important network segment in future RAN, capable of transporting diverse traffic types with varying stringent requirements within RAN. The crosshaul towards 6G is envisioned to be highly intelligent, reconfigurable, and adaptable to dynamic service requirements and network conditions. To this end, we propose a software defined network (SDN)-enabled reconfigurable optical crosshaul architecture (ROCA) that supports heterogeneous crosshaul transport technologies and dynamic functional splittings. ROCA enables efficient and intelligent control of the crosshaul data plane. The proposed architecture with a set of the next-generation RAN (NG-RAN) transport interfaces is evaluated using network models built on the ns-3 network simulator. Simulation results demonstrate the strengths and weaknesses of different crosshaul interfaces in agreement with the understanding of respective NG-RAN interfaces from the literature, which validates the modeling accuracy. We then demonstrate the reconfigurability of the architecture using a dynamic scenario with different reconfiguration strategies for meeting the user and network demands. The results indicate that ROCA serves as a scalable and flexible foundation for supporting high-capacity delay-stringent RAN that can be used in 6G and beyond wireless technologies.
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ItemNo Preview AvailableWearable Transmitter Coil Design for Inductive Wireless Power Transfer to Implantable Devices.Tai, Y-D ; Widdicombe, B ; Unnithan, RR ; Grayden, DB ; John, SE (IEEE, 2023-07)Wireless endovascular sensors and stimulators are emerging biomedical technologies for applications such as endovascular pressure monitoring, hyperthermia, and neural stimulations. Recently, coil-shaped stents have been proposed for inductive power transfer to endovascular devices using the stent as a receiver. However, less work has been done on the external transmitter components, so the maximum power transferable remains unknown. In this work, we design and evaluate a wearable transmitter coil that allows 50 mW power transfer in simulation.Clinical Relevance-This allows more accurate measurements and precise control of endovascular devices.
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ItemNo Preview AvailableGraph Neural Networks for Power Allocation in Wireless Networks with Full Duplex NodesChen, L ; Zhu, J ; Evans, J (IEEE, 2023)
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ItemNo Preview AvailableOn Distributed Nonconvex Optimisation via Modified ADMMMafakheri, B ; Manton, JH ; Shames, I (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023)
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ItemNo Preview AvailableThe Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise DetectionMendis, L ; Palaniswami, M ; Brownfoot, F ; Keenan, E (IEEE, 2023)Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To improve the performance of fetal compromise detection, deep learning methods have been proposed to automatically interpret FHR recordings. However, existing deep learning methods typically analyse a fixed-length segment of the FHR recording after removing signal gaps, where the influence of this segment selection process has not been comprehensively assessed. In this work, we develop a novel input length invariant deep learning model to determine the effect of FHR segment selection for detecting fetal compromise. Using this model, we perform five times repeated five-fold cross-validation on an open-access database of 552 FHR recordings and assess model performance for FHR segment lengths between 15 and 60 minutes. We show that the performance after removing signal gaps improves with increasing segment length from 15 minutes (AUC = 0.50) to 60 minutes (AUC = 0.74). Additionally, we demonstrate that using FHR segments without removing signal gaps achieves superior performance across signal lengths from 15 minutes (AUC = 0.68) to 60 minutes (AUC = 0.76). These results show that future works should carefully consider FHR segment selection and that removing signal gaps might contribute to the loss of valuable information.
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ItemNo Preview AvailableDemonstration of a Wideband 28 GHz Analog Radio-over-Fiber Optical Fronthaul Transmission Enabling Nonlinearity ToleranceSong, T ; Lim, C ; Nirmalathas, A (IEEE, 2023-01-01)
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ItemNo Preview AvailableEconomic Model Predictive Control of Water Distribution Systems with Solar Energy and BatteriesZheng, X ; Wang, Y ; Weyer, E ; Manzie, C (IEEE, 2023-01-01)
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ItemNo Preview AvailableSecond-Order Properties of Noisy Distributed Gradient DescentQin, L ; Cantoni, M ; Pu, Y (IEEE, 2023-01-01)
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ItemNo Preview AvailableA Group Formation Game for Local Anomaly DetectionYe, Z ; Alpcan, T ; Leckie, C (IEEE, 2023-01-01)
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ItemNo Preview AvailableStability of Nonlinear Systems with Two Time Scales Over a Single Communication ChannelWang, W ; Maass, AI ; Nešić, D ; Tan, Y ; Postoyan, R ; Heemels, WPMH (IEEE, 2023-01-01)