DeepMTL: Deep Learning Based Multiple Transmitter Localization

Zhan, Caitao; Ghaderibaneh, Mohammad; Sahu, Pranjal; Gupta, Himanshu

doi:10.1109/wowmom51794.2021.00017

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…A few studies have been published that propose a variety of localization techniques for the purpose of spectrum sharing scheme that make use of received signal strength (RSS) or received signal strength indicator (RSSI) [7], [8], [9], [10], [11], [12]. The advantages and disadvantages of these algorithms are summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood-Based Location, Orientation, Transmitted Power, and Antenna Beamwidth Estimator Utilizing the Angle-of-Arrival and Received Power for Spectrum Sharing at Millimeter Wave Bands

et al. 2023

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The fifth-generation (5G) system utilizing millimeter wave is available in Japan, but more bandwidth is demanded by the mobile phone operators to accommodate the demand for high data rate. Allocation of the vacant spectrum is impossible due to the past spectrum allocation to other radio systems. For example, 24.25-27.5 GHz has been allocated for the fixed wireless access (FWA) and license exempt small power data communication system in Japan. Although Japanese government is considering to license a part of the band for 5G under the spectrum sharing scheme, the coexistence design is quite challenging due to unavailable operation data of such systems in a form of database for the coexistence study. For this purpose, this paper introduces a maximum-likelihood estimator (MLE), which utilizes the angle-of-arrival (AoA) and received power of the line-of-sight (LOS)/diffraction paths as well as the map database to simultaneously estimate these unknown parameters via the spectrum sensing. It was applied to the measurement data obtained from a sub-urban environment where a commercial fixed wireless access system with similar operational parameters is under operation. Its performance was evaluated for a different number of receiver (Rx) points. The results showed that the parameter estimation accuracy is proportional to the number of receiver points. The distance and orientation errors of 45 m and 5 deg were achieved at 90th percentile with 40 Rx points. Furthermore, the prediction performance of the effective isotropic radiated power (EIRP) was decent, albeit a slight underestimation of the beamwidth.

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Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood-Based Location, Orientation, Transmitted Power, and Antenna Beamwidth Estimator Utilizing the Angle-of-Arrival and Received Power for Spectrum Sharing at Millimeter Wave Bands

et al. 2023

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“…Deep learning has been proved to be an efficient tool in a wide range of fields due to its outstanding capability to capture the features and learn the mapping of data [23,24]. Recently, several researches [25][26][27] apply deep neural networks (DNN) for end-to-end localization and gain some improvement. A long short-term memory (LSTM) network is used in [25] for small-scale indoor localization.…”

Section: Introductionmentioning

confidence: 99%

“…Lin et al [26] suggest a heatmap regression-based HMTLNet, revealing the active role of ResNet in a fully convolutional network (FCN) for localization. Zhan et al [27] present a convolutional neural network (CNN) to solve localization problems from a view of computer vision. However, there are mainly two challenges of these methods.…”

Section: Introductionmentioning

confidence: 99%

“…(2) The existing deep learning-based localization methods require massive data and dense deployment of sensors for perception of the target area. For example, the density of sensors in DeepMTL method [27] is 2 percent, with the number of sensors reaching to 200 in a 1 km × 1 km-sized area, which is costly in practice…”

Section: Introductionmentioning

confidence: 99%

“…4.2.4.Comparison with Other Algorithms. The proposed method is compared with DeepMTL localization[27] and another network structure, i.e., U-net[36]. DeepMTL primarily converts the RSS value into image data and predicts the positions of multiple transmitters by CNN.…”

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confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Localization with Urban Electromagnetic and Geographic Information

Wang

Huang

et al. 2022

Wireless Communications and Mobile Computing

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There is a growing demand for localization of illegal signal sources, aiming to guarantee the security of urban electromagnetic environment. The performance of traditional localization methods is limited due to the non-line-of-sight (NLOS) propagation and sparse layouts of sensors. In this paper, a deep learning-based localization method is proposed to overcome these issues in urban scenarios. Firstly, a model of electromagnetic wave propagation considered with geographic information is proposed to prepare reliable datasets for intelligent cognition of urban electromagnetic environment. Then, this paper improves an hourglass neural network which consists of downsampling and upsampling layers to learn the propagation features from sensing data. The core modules of VGG and ResNet are, respectively, utilized as feature extractors in downsampling. Moreover, this paper proposes a weighted loss function to expand the attention on position features, in order to improve the performance of localization with sparse layouts of sensors. Representative numerical results are discussed to assess the proposed method. ResNet-based extractor performs more efficiently than VGG-based extractor, and the proposed weighted loss function increases the localization accuracy by more than 50%. Additionally, the established geographic model supports qualitative and quantitative evaluation of the robustness with varied degree of NLOS propagation. Compared with other deep learning-based algorithms, the proposed method presents the more robust and superior performance under severe NLOS propagation and sparse sensing conditions.

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