Machine-Learning-Based Throughput Estimation Using Images for mmWave Communications

Okamoto, Hiroyuki; Nishio, Takayuki; Morikura, Masahiro; Yamamoto, Koji; Murayama, Daisuke; Katsuya, Nakahira

doi:10.1109/vtcspring.2017.8108570

Cited by 29 publications

(49 citation statements)

References 14 publications

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“…This paper expands our previous work [23], [24] to quantitatively predict the time series of received power including the future (i.e., several hundred milliseconds ahead) on a mmWave link. In [23], [24], we proposed current throughput or received power estimation schemes from depth camera images. The schemes enable the estimation of the throughput or received power at the time when the image is obtained on a mmWave link even when an access point (AP) and a station (STA) are not communicating at the time.…”

Section: Introductionmentioning

confidence: 53%

“…However, since the implementation of beamforming operations depends on manufacturers and it is black-boxed, effects of the black-boxed beamforming operations can reduce the reproducibility of the experiments. Therefore, we set up the experiment so that the beamforming is not 23 performed even when the blockage occurs. Specifically, the MD was located behind the STA, two pedestrians traveled between the STA and the MD moving along the path shown in Fig.…”

Section: A Setup Of Mmwave Experimentsmentioning

confidence: 99%

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Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

Nishio¹,

Okamoto²,

Nakashima³

et al. 2019

IEEE J. Select. Areas Commun.

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This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks.Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs. We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead. The key idea is to leverage the camera imagery and machine learning (ML). The time-sequential images can involve the spatial geometry and the mobility of obstacles representing the mmWave signal propagation. ML is used to build the prediction model from the dataset of sequential images labeled with the received power in several hundred milliseconds ahead of when each image is obtained. The simulation and experimental evaluations using IEEE 802.11ad devices and a depth camera show that the proposed mechanism employing convolutional LSTM predicted a time series of the received power in up to 500 ms ahead at an inference time of less than 3 ms with a root-mean-square error of 3.5 dB.Parts of this work were presented at the 85th IEEE Vehicular Technology Conference (VTC Spring) and the IEEE Consumer Communications and Networking Conference (CCNC). 2 Index Terms millimeter-wave communications, link quality prediction, proactive prediction, machine learning, supervised learning, depth image Received power (dBm) Grand truth (a) When the camera was at A low . Received power (dBm) Grand truth (b) When the camera was at A high .

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Section: Introductionmentioning

confidence: 53%

Section: A Setup Of Mmwave Experimentsmentioning

confidence: 99%

Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

Nishio¹,

Okamoto²,

Nakashima³

et al. 2019

IEEE J. Select. Areas Commun.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [21], the authors proposed a method using unsupervised machine learning method to cluster low power nodes and decide fog nodes in 5G heterogeneous networks to reduce latency. In [22], the authors proposed a machine learning based method to collect images from surveillance cameras to let devices decide the blockage locations, so that the communication devices can estimate the communication condition with the help of the blockage location information and improve system performance. In [23], the authors used machine learning based method to help solve automatic modulation recognition problem for cognitive radio (CR) systems.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Signal Detection for CoMP Downlink in Ultra-Dense Small Cell Networks

et al. 2020

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In the next generation wireless communication systems, high-speed data rate, reliable link quality, and ubiquitous access are necessary requirement. To meet the next generation communication system requirements, the ultra-dense small cell network (SCN) is proposed as one of the possible candidate solutions. To achieve high data rate and reliable link quality, coordinated multiple point (CoMP) transmission is usually used in ultra-dense SCN to satisfy the performance target. However, to realize CoMP transmissions in ultradense SCN, the feedback load is heavy because of the large amount of small cells and users. In this study, we want to investigate the ultra-dense SCN environment and design an effective method for its downlink (DL) transmission. This method consists of iterative scheme which iteratively solves the received signal with proper step-size value γ. The step-size value γ is very important to the convergence performance of iterative scheme because it affects the convergence speed and the converged error level of the iterative algorithm. Therefore, in this paper, we propose a novel machine learning based method which creates data model from input data. When the model is well established, the optimal step-size can be well estimated and the feedback information can become rough and the bandwidth allocated for feedback can be saved for data transmission. The simulations show that, the proposed method can create good model and achieve better convergence performance. For example, about the distribution of the transmissions with convergence iteration number less than 10 4 level, the proposed method can obtain 30% improvement than the traditional method with fixed step-size γ = 0.01. INDEX TERMS Small cell network (SCN), ultra-dense SCN, coordinated multi-point (CoMP) transmission, feedback reduction, machine learning, neural network.

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“…A test-bed consisting of a Kinect sensor [29] and IEEE 802.11 ad compliant WLAN devices was constructed to estimate the position and velocity of pedestrians, with the aim of avoiding throughput degradation by predicting potential human body blockage incidents. Moreover, Okamoto et al studied throughput estimation at mmWave frequencies using images from an RGB-D camera along with machine learning [30]. An online algorithm, called adaptive regularization of weight vectors, was applied to process the image depth, thus building a relationship between image depth and unexpected throughput degradation.…”

Section: Introductionmentioning

confidence: 99%

An RSS-Based Classification of User Equipment Usage in Indoor Millimeter Wave Wireless Networks Using Machine Learning

et al. 2020

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The use of millimeter wave technologies offer a promising solution for dense small cell networks, despite having to contend with challenging propagation characteristics. In particular, user-induced effects can lead to significant channel variations depending on the user equipment (UE) usage mode which in turn, can impact the quality of service. Estimation of UE operating conditions is therefore critical for optimal radio resource management. We propose a new approach to user activity recognition which makes use of both supervised and unsupervised machine learning. In particular, using information extracted from the received signal strength (RSS), a common metric readily available from many receiver chipsets, we perform a classification of user state (static or mobile relative to an access point) and UE mode of operation (voice call, using an app or in pocket). To develop and then train our classification system, measured RSS data was obtained using a custom 60 GHz measurement system for a range of indoor office scenarios which considered various UE to ceiling mounted access point configurations. In our approach, differentiation between static and mobile states is performed in preprocessing using a k-means algorithm. Small-scale fading features are then estimated from the RSS data and, using different feature scaling mechanisms, various supervised learning approaches are applied to investigate the optimal classification accuracy for the considered use cases in this work. We compare the classification performance of various window sizes and types, and show that a sliding window length of 1s without overlap performs best for time series segmentation at 60 GHz for the activities considered in this study. Among the different supervised learning approaches, the Decision Tree (DT) classifier performs best for both the user static and mobile cases with an accuracy of 100% and 98.0%, respectively. For static cases, user orientation, i.e., line-of-sight (LOS), quasi-LOS, and non-LOS, can also be classified and here the DT classifier also performs best with an accuracy of 98.2%, 97.6% and 100% for the voice call, using an app or in pocket use cases. Additionally, a feature ranking algorithm, called ReliefF, is adopted to determine the small-scale fading features that have the most significant influences on the classification accuracy and three different feature sets, namely Full, Reduced and Constrained sets, are then proposed based on feature ranking results. This allows the proposed techniques to be deployed on wireless platforms with different levels of processing capability.

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Machine-Learning-Based Throughput Estimation Using Images for mmWave Communications

Cited by 29 publications

References 14 publications

Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

Machine Learning-Based Signal Detection for CoMP Downlink in Ultra-Dense Small Cell Networks

An RSS-Based Classification of User Equipment Usage in Indoor Millimeter Wave Wireless Networks Using Machine Learning

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