Doppler spectrum measurements for land mobile satellite systems around 2.2 GHz and 3.8 GHz

Ait-Ighil, M.; Deloues, Thierry; Lemorton, J.; Issac, F.; Rougerie, Sébastien

doi:10.1109/eucap.2016.7481883

Cited by 4 publications

(11 citation statements)

References 2 publications

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“…Last, there is no dazzling effect due to the sun in the SYNTHIA dataset similar as what we may observe in our collected images (see for example Figure 2). The resolution and the size of the images available in the CAMVID datasets are very different from the ones we collected in [1] and [2] and thus, these images cannot be used for the algorithms training. Finally, we decided to train the deep learning algorithms with images extracted from our dataset.…”

Section: The Table 1 Present the Main Competition Results For Images ...mentioning

confidence: 99%

“…To do so, we investigate two available databases: the SYNTHIA database [16] and the CAMVID database [17]. Here, we meet the main issue of the deep learning approach as these datasets are very different from the images collected in [1] and [2]. The SYNTHIA dataset proposes realistic synthetic images, but the contrast is too important (the edges of objects are much too clear) and the color homogeneity of the objects is too high.…”

Section: The Table 1 Present the Main Competition Results For Images ...mentioning

confidence: 99%

“…The selection of the images for the deep learning training is completely random, and around 100 images were selected and classified manually among more than 10 000 images. After the algorithm training, Figure 2 and Figure 3 present the classification results of the deep learning algorithms respectively done on Ka band database [2] and S band database [1]. The green border represents pixels tagged as "vegetation", the red border the pixels tagged as "building", the blue border pixels tagged as "pole", and the whole other pixels are therefore tagged as "sky".…”

Section: The Table 1 Present the Main Competition Results For Images ...mentioning

confidence: 99%

“…It is reliable for frequencies between 1 to 30 GHz, and for several environments (train, highway, sub-urban, wooded …). This model has been built thanks to extensive measurements campaigns at L, S and C band ( [4], [5], [6], [1]) and Ku, Ka band [7], [2]. This model provides reliable results for selected overall environments and frequencies, and can be used for receiver testing and link budget estimation.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], the authors propose to use the fish-eye images to classify the local environment in four classes: Sky, Vegetation, poles or defoliated trees and other man-made structures such as buildings. For each class, the statistics associated to the RF signal (extracted from measurement, [1]) are provided and give a first idea of signal fading associated to each LOS and NLOS situation. In this paper, the concept proposed in [15] is enhanced with two main improvements: first, the images processing is enhanced with the help of deep learning processing available in the literature (section II of the paper).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Land Mobile Propagation Satellite Model based on 360° images

Rougerie

Israel

2021

2021 15th European Conference on Antennas and Propagation (EuCAP)

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This paper presents a new concept of LandMobile Propagation Satellite model. The main idea is to predict the propagation channel based on the analysis of 360° images. To do so, concurrent RF / optic measurements have been done in the past and this paper will present the correlation study between the images and RF signal. Finally, a coupling model RF / images is proposed here, which allows a LMS channel prediction between 1 to 30 GHz.

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Section: The Table 1 Present the Main Competition Results For Images ...mentioning

confidence: 99%

Section: The Table 1 Present the Main Competition Results For Images ...mentioning

confidence: 99%

Section: The Table 1 Present the Main Competition Results For Images ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Land Mobile Propagation Satellite Model based on 360° images

Rougerie

Israel

2021

2021 15th European Conference on Antennas and Propagation (EuCAP)

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Multi-Scene Doppler Power Spectrum Modeling of LEO Satellite Channel Based on Atlas Fingerprint Method

et al. 2021

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The modeling of low earth orbit (LEO) satellite channel depends on its Doppler power spectrum. Due to satellite during transit, diversity and dynamic channel scene, the modeling of Doppler power spectrum has two serious problems: one is that the shape of the Doppler power spectrum will vary with the change of scenes and time, but the use of the existing traditional Doppler power spectrum models is difficult to accurately describe them. The other one is that the amount of measured data used for modeling is too large to handle, and data aliasing is easy to occur between different scenes, which will make it difficult to ensure the accuracy of model parameter fitting. In this paper, a two-side truncated asymmetric Doppler power spectrum model is proposed to universally describe the Doppler power spectrum during satellite transit. In addition, the atlas fingerprint method clustering is adopted to realize the classification of the measured data samples of Doppler power spectrum in multiple scenes, and the data with the strongest representation ability in each scene is selected to fit the model parameters. The simulation results show that the proposed model is in good agreement with the measured data. Therefore, parameter fitting using the proposed method can improve the accuracy of the model, so as to better describe the characteristics of LEO satellite channel fading in the frequency domain.INDEX TERMS LEO satellite channel, Doppler power spectrum model, multiple scenes, atlas fingerprint, parameter fitting.

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Downlink Cell-Free Fixed Wireless Access: Architectures, Physical Realities, and Research Opportunities

Zhang

You

Zheng

et al. 2023

IEEE Wireless Commun.

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Recently a new paradigm of wireless access, termed as cell-free massive multiple-input multiple-output (MIMO), has drawn significant research interest. Its primary distinction from conventional massive MIMO aided cellular networks is the ability to eliminate the detrimental inter-cell interference (ICI), or to convert ICI into extra power for the intended signal via a multi-cell cooperation approach originated from network MIMO. However, the information-theoretical limit of cell-free access is achieved at the expense of large network configuration overhead and high MIMO processing complexity. Because of the dynamic nature of wireless channels, the global channel state information (CSI) invoked for network MIMO quickly becomes outdated, leading to performance degradation. This paper focuses on the cell-free implementation of fixed wireless access (FWA), a complementary solution to fibre-to-the-premise (FTTP) where the latter is prohibitively expensive. In particular, we discuss the centralisation architectures and channel characteristics of cellfree FWA, as well as their joint implications on imperfect CSI performance. Moreover, measurement-based offline simulations show that the long coherence time ('quasi-static') assumption of real-world FWA channels is only valid against a completely motionless background, and thus it should not be used in FWA system design or performance analysis. Finally, we present new research opportunities for cell-free FWA in terms of physical infrastructure, data processing as well as machine learning.

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Doppler spectrum measurements for land mobile satellite systems around 2.2 GHz and 3.8 GHz

Cited by 4 publications

References 2 publications

Land Mobile Propagation Satellite Model based on 360° images

Land Mobile Propagation Satellite Model based on 360° images

Multi-Scene Doppler Power Spectrum Modeling of LEO Satellite Channel Based on Atlas Fingerprint Method

Downlink Cell-Free Fixed Wireless Access: Architectures, Physical Realities, and Research Opportunities

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