Electrical and Electronic Engineering - Research Publications
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ItemAchieving AI-enabled Robust End-to-End Quality of Experience over Backhaul Radio Access Networks(Institute of Electrical and Electronics Engineers (IEEE), 2022-01-01)Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-linear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machine-learning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the Software-Defined Network (SDN) and Kubernetes technologies. Our results show that deep learning-based modelling achieves E2E QoE with approximately 99.8% accuracy, and our robust joint-optimization technique allocates resources efficiently when compared to existing differential services alternatives.
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ItemAchieving QoS for bursty uRLLC applications over passive optical networks(Optica Publishing Group, 2022-05-01)Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict quality of service (QoS) requirements. The passive optical network (PON) is a popular access network technology, which is envisioned to handle such applications at the access segment of the network. However, the existing standards cannot handle strict QoS constraints for such applications. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing proposals in generic networks with optimal strategies are computationally complex and are, therefore, not suitable for uRLLC applications. This paper presents a novel computationally efficient, far-sighted bandwidth allocation policy design for facilitating bursty uRLLC traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements. To this purpose, first we design a delay-tracking mechanism, which allows us to model the resource allocation problem from a control-theoretic viewpoint as a model predictive control (MPC) problem. MPC helps in making far-sighted decisions regarding resource allocations and captures the time-varying dynamics of the network. We provide computationally efficient polynomial time solutions and show their implementation in the PON framework. Compared to existing approaches, MPC can improve delay violations by 15% and 45% at loads of 0.8 and 0.9, respectively, for delay-constrained applications of 1 ms and 4 ms. Our approach is also robust to varying traffic arrivals.
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ItemSum-Rate Optimization in Flexible Half-Duplex Networks With Transmitter/Receiver Scheduling(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-07-01)In this paper, we focus on the problem of transmitter and receiver scheduling to maximize the achievable sum-rate of a flexible half-duplex network where nodes have the flexibility to either transmit, receive or be silent in a given time slot. We consider a network with multiple transmitters and receivers where each transmitter has specific information it needs to send to a set of receiving nodes. First, we conduct some structural analysis and show that the achievable sum-rate is maximized when each transmitter only transmits to a single receiver at a given time. Next, we consider one instance of the flexible network and by reducing the symmetric multiple receiver network to a single receiver network, we also show that the achievable sum-rate is maximized when either one transmitter or all the transmitters transmit. In fact, there exists a unique received signal-to-noise ratio at which the optimality changes from all-to-one. Finally, we design a novel low-cost algorithm that gives a sub-optimal solution to the achievable sum-rate maximization problem in a flexible half-duplex network. We also provide a comprehensive comparison of the proposed algorithm with respect to existing resource allocation techniques, and observe that our proposed algorithm provides significant sum-rate gains.
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ItemNo Preview AvailableGenetic optimization of mid-infrared filters for a machine learning chemical classifier(Optica Publishing Group, 2022-05-23)Miniaturized mid-infrared spectrometers present opportunities for applications that range from health monitoring to agriculture. One approach combines arrays of spectral filters with infrared photodetectors, called filter-array detector-array (FADA) microspectrometers. A paper recently reported a FADA microspectrometer in tandem with machine learning for chemical identification. In that work, a FADA microspectrometer with 20 filters was assembled and tested. The filters were band-pass, or band-stop designs that evenly spanned the microspectrometer’s operating wavelength range. However, given that a machine learning classifier can be trained on an arbitrary filter basis, it is not apparent that evenly spaced filters are optimal. Here, through simulations with noise, we use a genetic algorithm to optimize six bandpass filters to best identify liquid and gaseous chemicals. We report that the classifiers trained with the optimized filter sets outperform those trained with evenly spaced filter sets and those handpicked to target the absorption bands of the chemicals investigated.
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ItemNo Preview AvailableInvestigation on repetition-coding and space-time-block-coding for indoor optical wireless communications employing beam shaping based on orbital angular momentum modes(Optica Publishing Group, 2022-06-06)In this paper, we propose a novel beam shaping technique based on orbital angular momentum (OAM) modes for indoor optical wireless communications (OWC). Furthermore, we investigate two spatial diversity techniques, namely repetition-coding (RC) and Alamouti-type orthogonal space-time-block-coding (STBC) for indoor OWC employing the new beam shaping technique. The performance of both diversity schemes is systematically analyzed and compared under different beam shaping techniques using different OAM modes with different power ratios of the modes. It is shown that both RC and STBC can improve the system performance and effective coverage and RC outperforms STBC in all the beam shaping techniques regardless of the power ratios of the different modes. In addition, to further understand the performance of RC and STBC schemes against the signal delays induced during OAM mode conversion, the system tolerance of the two schemes to the delay interval is investigated with different OAM mode-based beam shaping techniques. Numerical results show that higher resistance to the delay interval can be achieved in STBC scheme. The advantage is more obvious when employing OAM0 and OAM1 based beam shaping technique.
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ItemNo Preview AvailableDeep-learning-enabled high-performance full-field direct detection with dispersion diversity(Optica Publishing Group, 2022-03-28)Data center interconnects require cost-effective photonic integrated optical transceivers to meet the ever-increasing capacity demands. Compared with a coherent transmission system, a complex-valued double-sideband (CV-DSB) direct detection (DD) system can minimize the cost of the photonic circuit, since it replaces two stable narrow-linewidth lasers with only a low-cost un-cooled laser in the transmitter while maintaining a similar spectral efficiency. In the carrier-assisted DD system, the carrier power accounts for a large proportion of the total optical signal power. Reducing the carrier to signal power ratio (CSPR) can improve the information-bearing signal power and thus the achievable system performance. To date, the minimum required CSPR is ∼7 dB for all the reported CV-DSB DD systems having electrical bandwidths of approximately half of baud rates. In this paper, we propose a deep-learning-enabled DD (DLEDD) scheme to recover the full optical field of the transmitted signal at a low CSPR of 2 dB in experiment. Our proposal is based on a dispersion-diversity receiver with an electrical bandwidth of ∼61.0% baud rate and a high tolerance to laser wavelength drift. A deep convolutional neural network enables accurate signal recovery in the presence of a strong signal-signal beat interference. Compared with the conventional method, the proposed DLEDD scheme can reduce the optimum CSPR by ∼8 dB, leading to a significant signal-to-noise ratio improvement of ∼5.8 dB according to simulation results. We experimentally demonstrate the optical field reconstruction for a 28-GBaud 16-ary quadrature amplitude modulation signal after 80-km single-mode fiber transmission based on the proposed DLEDD scheme with a 2-dB optimum CSPR. The results show that the proposed DLEDD scheme could offer a high-performance solution for cost-sensitive applications such as data center interconnects, metro networks, and mobile fronthaul systems.
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ItemNo Preview AvailableFeature issue introduction: ultra-wideband optical communications.(Optica Publishing Group, 2022-04-11)This Feature Issue covers the important aspects to develop ultra-wideband optical communication systems including optoelectronics, impairment modeling and compensation, optical amplification, superchannel and multi-band transmission and control, and so forth. This Introduction provides a summary of the articles on these topics in this Feature Issue.
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ItemNovel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-06-01)Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, severely affecting upper limb movements. Monitoring the progression of hemiparesis requires manual observation of limb movements at regular intervals, and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparesis in acute stroke. We propose novel measures of similarity and asymmetry in hand activities through bivariate Poincaré analysis between two-hand accelerometer data for quantifying hemiparetic severity. The proposed descriptors characterize the distribution of activity surrogates derived from acceleration of the two hands, on a 2D bivariate Poincaré Plot. Experiments show that while the descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, their normalized difference CSDR and the descriptors Complex Cross-Correlation Measure ( C3M) and Activity Asymmetry Index ( AAI) can distinguish between mild, moderate and severe hemiparesis. These measures are compared with traditional measures of cross-correlation and evaluated against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for hemiparetic severity estimation. This study, undertaken on 40 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length ( 5 minutes) wearable accelerometry data for identifying hemiparesis with greater clinical sensitivity. Results show that the proposed descriptors with a hierarchical classification model outperform state-of-the-art methods with overall accuracy of 0.78 and 0.85 for 4-class and 3-class hemiparesis identification respectively.
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ItemNo Preview AvailableMicrogrids Against Wildfires: Distributed Energy Resources Enhance System Resilience(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-01-01)
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ItemNo Preview AvailableASYMPTOTICS OF EIGENSTRUCTURE OF SAMPLE CORRELATION MATRICES FOR HIGH-DIMENSIONAL SPIKED MODELS(STATISTICA SINICA, 2021-01-01)Sample correlation matrices are widely used, but for high-dimensional data little is known about their spectral properties beyond "null models", which assume the data have independent coordinates. In the class of spiked models, we apply random matrix theory to derive asymptotic first-order and distributional results for both leading eigenvalues and eigenvectors of sample correlation matrices, assuming a high-dimensional regime in which the ratio p/n, of number of variables p to sample size n, converges to a positive constant. While the first-order spectral properties of sample correlation matrices match those of sample covariance matrices, their asymptotic distributions can differ significantly. Indeed, the correlation-based fluctuations of both sample eigenvalues and eigenvectors are often remarkably smaller than those of their sample covariance counterparts.