Sparse Structure Enabled Grid Spectral Mixture Kernel for Temporal Gaussian Process Regression

Yin, Feng; He, Xianghuo; Pan, Lishuo; Chen, Tianshi; Luo, Zhi-Quan

doi:10.23919/icif.2018.8455284

Cited by 9 publications

(9 citation statements)

References 10 publications

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“…In [64], a spectral mixture (SM) kernel was proposed to approximate the spectral density with a Gaussian mixture model arbitrarily well in the frequency domain and transform it back into a universal stationary kernel. In [65], [66], the authors modified the SM kernel to a linear multiple low-rank sub-kernels with a favorable optimization structure, which enables faster and more stable numerical search. In [67], [68], [69], [70], a DNN architecture was combined with the automatic relevance determination (ARD) kernel to approximate any kernel function (including both the stationary and non-stationary ones).…”

Section: B Gaussian Processesmentioning

confidence: 99%

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Yin

Lin

Kong

et al. 2020

IEEE Open J. Signal Process.

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176

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In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used learning models, namely the deep neural network model and the Gaussian process model, and (3) various distributed model hyper-parameter optimization schemes. Then, we demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works, which cover a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation using low-sampling GPS, and spatio-temporal wireless traffic data modeling and prediction. Experimental results show that near centralized data fitting-and prediction performance can be achieved by a set of collaborative mobile users running distributed algorithms. All the surveyed use cases fall under our newly proposed Federated Localization (FedLoc) framework, which targets on collaboratively building accurate location services without sacrificing user privacy, in particular, sensitive information related to their geographical trajectories. Future research directions are also discussed at the end of this paper.

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Section: B Gaussian Processesmentioning

confidence: 99%

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Yin

Lin

Kong

et al. 2020

IEEE Open J. Signal Process.

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176

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“…Additionally, in order to eliminate human interventions, automatic kernel learning/determination has become more and more fashionable in recent years. Representative results include [26]- [28] that proposed to design an universal kernel in the frequency domain and [29] that proposed to search for a space of kernel structures built compositionally by adding and multiplying a small number of elementary kernels. The drawback of these advanced kernels lies in the increased model and training complexity.…”

Section: ) Kernel Function Tailored For Wireless Traffic Prediction:mentioning

confidence: 99%

“…Consequently, when the wireless traffic dataset is large, i.e., N is a large number, the time consumption of each 4 However, for multiple linear kernels, such as the ones proposed in [28], [30], [31], P 0 becomes a difference-of-convex problem and efficient algorithms exist for solving the hyper-parameters.…”

Section: ) Learning Objectivesmentioning

confidence: 99%

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Wireless Traffic Prediction With Scalable Gaussian Process: Framework, Algorithms, and Verification

Yin

et al. 2019

IEEE J. Select. Areas Commun.

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162

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The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions.Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. Experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance. Index TermsC-RANs, Gaussian processes, parallel processing, ADMM, cross-validation, machine learning, wireless traffic solution to reach such ambitious goals is the adoption of cloud radio access networks (C-RANs) [2], which have attracted intense research interests from both academia and industry in recent years [3]. A C-RAN is composed of two parts: the distributed remote radio heads (RRHs) with basic radio functionalities to provide coverage over a large area, and the centralized baseband units (BBUs) pool with parallel BBUs to support joint processing and cooperative network management. The BBUs can perform dynamic resource allocation in accordance with realtime network demands based on the virtualized resources in cloud computing. One major feature for the C-RANs to enable high energy-efficient services is the fast adaptability to non-uniform traffic variations [1]-[4], e.g., the tidal effects. Consequently, wireless traffic prediction techniques stand out as the key enabler to realize such loadaware management and proactive control in C-RANs, e.g., the load-aware RRH on/off operation [4]. However, the adoption of wireless traffic prediction techniques in C-RANs must satisfy the requirements on prediction accuracy, cost-efficiency, implementability, and scalability for large-scale executions. A. Related WorksIn the literature, many statistical ti...

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“…Recently, [9] compared different kernels thoroughly using simulations and proved that the SE kernel works quite well in most cases. Most recent work on kernel design include [10]- [12]. GP based RSS models have been used in different Bayesian filters, which include extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter [5].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Processes for RSS Fingerprints Construction in Indoor Localization

Zhao

Liu

Mihaylova

et al. 2018

2018 21st International Conference on Information Fusion (FUSION)

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Location-based applications attract more and more attention in recent years. Examples of such applications include commercial advertisements, social networking software and patient monitoring. The received signal strength (RSS) based location fingerprinting is one of the most popular solutions for indoor localization. However, there is a big challenge in collecting and maintaining a relatively large RSS fingerprint database. In this work, we propose and compare two algorithms namely, the Gaussian process (GP) and Gaussian process with variogram, to estimate and construct the RSS fingerprints with incomplete data. The fingerprint of unknown reference points is estimated based on measurements at a limited number of surrounding locations. To validate the effectiveness of both algorithms, experiments using Bluetooth-low-energy (BLE) infrastructure have been conducted. The constructed RSS fingerprints are compared to the true measurements, and the result is analyzed. Finally, using the constructed fingerprints, the localization performance of a probabilistic fingerprinting method is evaluated.

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Sparse Structure Enabled Grid Spectral Mixture Kernel for Temporal Gaussian Process Regression

Cited by 9 publications

References 10 publications

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Wireless Traffic Prediction With Scalable Gaussian Process: Framework, Algorithms, and Verification

Gaussian Processes for RSS Fingerprints Construction in Indoor Localization

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