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14. Double-hit separation and dE/dx resolution of a time projection chamber with GEM readout

Author

Aoki, Yumi, Attié, David, Behnke, Ties, Bellerive, Alain, Bezshyyko, Oleg, Sankar, Deb Bhattacharya, Bhattacharya, Purba, Bhattacharya, Sudeb, Chang, Yue, Colas, Paul, De Lentdecker, Gilles, Dehmelt, Klaus, Desch, Klaus, Diener, Ralf, Dixit, Madhu, Einhaus, Ulrich, Fedorchuk, Oleksiy, Fleck, Ivor, Fujii, Keisuke, Fusayasu, Takahiro, Ganjour, Serguei, Gros, Philippe, Hayman, Peter, Ikematsu, Katsumasa, Jönsson, Leif, Kaminski, Jochen, Kato, Yukihiro, Kleinwort, Claus, Martin, Peter Kluit, Kobayashi, Makoto, Krämer, Uwe, Lundberg, Björn, Lupberger, Michael, Majumdar, Nayana, Malek, Paul, Matsuda, Takeshi, Mjoernmark, Ulf, Mukhopadhyay, Supratik, Müller, Felix, Münnich, Astrid, Nakajima, Jurina, Narita, Shinya, Negishi, Kentaro, Ogawa, Tomohisa, Oskarsson, Anders, Österman, Lennart, Peterson, Daniel, Qi, Huirong, Riallot, Marc, Rosemann, Christoph, Roth, Stefan, Schade, Peter, Schaefer, Oliver, Settles, Ronald Dean, Shirazi, Amir Noori, Shoji, Aiko, Smirnova, Oxana, Sugiyama, Akira, Takahashi, Tohru, Timmermans, Jan, Titov, Maksym, Tsionou, Dimitra, Vauth, Annika, Watanabe, Takashi, Werthenbach, Ulrich, Wu, Mengqing, Yang, Yifan, Yonamine, Ryo, Yuan, Zhiyang, Yumino, Keita, and Zhang, Fan

Subjects
Physics - Instrumentation and Detectors
Abstract

A time projection chamber (TPC) with micropattern gaseous detector (MPGD) readout is investigated as main tracking device of the International Large Detector (ILD) concept at the planned International Linear Collider (ILC). A prototype TPC equipped with a triple gas electron multiplier (GEM) readout has been built and operated in an electron test beam. The TPC was placed in a 1 T solenoidal field at the DESY II Test Beam Facility, which provides an electron beam up to 6 GeV/c. The performance of the readout modules, in particular the spatial point resolution, is determined and compared to earlier tests. New studies are presented with first results on the separation of close-by tracks and the capability of the system to measure the specific energy loss dE/dx. This is complemented by a simulation study on the optimization of the readout granularity to improve particle identification by dE/dx., Comment: 29 pages, 30 figures, 6 tables. This is the Accepted Manuscript version of an article accepted for publication in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1748-0221/17/11/P11027

Published
2022
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16. The International Linear Collider: Report to Snowmass 2021

Author

Aryshev, Alexander, Behnke, Ties, Berggren, Mikael, Brau, James, Craig, Nathaniel, Freitas, Ayres, Gaede, Frank, Gessner, Spencer, Gori, Stefania, Grojean, Christophe, Heinemeyer, Sven, Jeans, Daniel, Kruger, Katja, List, Benno, List, Jenny, Liu, Zhen, Michizono, Shinichiro, Miller, David W., Moult, Ian, Murayama, Hitoshi, Nakada, Tatsuya, Nanni, Emilio, Nojiri, Mihoko, Padamsee, Hasan, Perelstein, Maxim, Peskin, Michael E., Poeschl, Roman, Posen, Sam, Robson, Aidan, Strube, Jan, Suehara, Taikan, Tian, Junping, Titov, Maxim, Vos, Marcel, White, Andrew, Wilson, Graham, Yokoya, Kaoru, Zarnecki, Aleksander Filip, Adachi, Ichiro, Agashe, Kaustubh, Jovin, Tatjana Agatonovic, Aihara, Hiroaki, Altmannshofer, Wolfgang, Alves, Daniele, Anguiano, Justin, Aoki, Ken-Ichi, Aoki, Masato, Aoki, Toshihiro, Aoki, Yumi, Arai, Yasuo, Araki, Hayato, Asada, Haruka, Asai, Kento, Asai, Shoji, Attie, David, Baer, Howard, Bagger, Jonathan, Bai, Yang, Bailey, Ian, Barrue, Ricardo, Bartoldus, Rainer, Barzi, Emanuela, Basso, Matthew, Bauerdick, Lothar, Baum, Sebastian, Bellerive, Alain, Belomestnykh, Sergey, Antequera, Jorge Berenguer, Beyer, Jakob, Bhat, Pushpalatha, Bilki, Burak, Black, Kevin, Bloom, Kenneth, Bodwin, Geoffrey, Boisvert, Veronique, Boran, Fatma, Boudry, Vincent, Boughezal, Radja, Boveia, Antonio, Bozovic-Jelisavcic, Ivanka, Brient, Jean-Claude, Brodsky, Stanley, Brunetti, Laurent, Buesser, Karsten, Bulyak, Eugene, Burrows, Philip N., Burt, Graeme C., Cai, Yunhai, Cairo, Valentina, Canepa, Anadi, Celiberto, Francesco Giovanni, Cenni, Enrico, Chacko, Zackaria, Chaikovska, Iryna, Checchin, Mattia, Chen, Lisong, Chen, Thomas Y., Cheng, Hsin Chia, Cho, Gi-Chol, Choudhary, Brajesh, Clarke, Jim, Cline, James, Co, Raymond, Cohen, Timothy, Colas, Paul, Damerell, Chris, Das, Arindam, Dasu, Sridhara, Dawson, Sally, de Blas, Jorge, de Lima, Carlos Henrique, Deandrea, Aldo, Dehmelt, Klaus, Delayen, Jean, Demarteau, Marcel, Denisov, Dmitri, Dermisek, Radovan, Dieguez, Angel, Dohmae, Takeshi, Dopke, Jens, Dort, Katharina, Du, Yong, Dudar, Bohdan, Dutta, Bhaskar, Dutta, Juhi, Einhaus, Ulrich, Elsen, Eckhard, Endo, Motoi, Eremeev, Grigory, Eren, Engin, Erler, Jens, Esarey, Eric, Everett, Lisa, Golfe, Angeles Faus, Garcia, Marcos Fernandez, Foster, Brian, Fourches, Nicolas, Fouz, Mary-Cruz, Fujii, Keisuke, Fujimoto, Junpei, Torregrosa, Esteban Fullan, Furukawa, Kazuro, Fusayasu, Takahiro, Fuster, Juan, Ganjour, Serguei, Gao, Yuanning, Gaur, Naveen, Geng, Rongli, Georgi, Howard, Gherghetta, Tony, Goldstein, Joel, Goncalves, Dorival, Gonski, Julia, Gonzalo, Tomas, Goto, Takeyoshi, Goto, Toru, Graf, Norman, Grames, Joseph, Grannis, Paul, Gray, Lindsey, Grohsjean, Alexander, Gu, Jiayin, Guler, Yalcin, Gutierrez, Phillip, Haba, Junji, Haber, Howard, Hallford, John, Hamaguchi, Koichi, Han, Tao, Hara, Kazuhiko, Harada, Daisuke, Hashimoto, Koji, Hashino, Katsuya, Hayashi, Masahito, Heinrich, Gudrun, Hidaka, Keisho, Higuchi, Takeo, Hinode, Fujio, Hioki, Zenro, Hirose, Minoru, Hiroshima, Nagisa, Hisano, Junji, Hollik, Wolfgang, Homiller, Samuel, Hong, Sungwoo, Hook, Anson, Horii, Yasuyuki, Hoshina, Hiroki, Hristova, Ivana, Huitu, Katri, Hyakutake, Yoshifumi, Iijima, Toru, Ikematsu, Katsumasa, Ilderton, Anton, Inami, Kenji, Irles, Adrian, Ishikawa, Akimasa, Ishiwata, Koji, Ito, Hayato, Ivanov, Igor, Iwamoto, Sho, Iwamoto, Toshiyuki, Iwasaki, Masako, Iwashita, Yoshihisa, Jia, Haoyi, Morales, Fabricio Jimenez, Joshi, Prakash, Jung, Sunghoon, Kacarevic, Goran, Kagan, Michael, Kakizaki, Mitsuru, Kalinowski, Jan, Kaminski, Jochen, Kanaya, Kazuyuki, Kanemura, Shinya, Kanno, Hayato, Kano, Yuya, Kashiwagi, Shigeru, Kato, Yukihiro, Kawada, Nanami, Kawada, Shin-ichi, Kawagoe, Kiyotomo, Khoze, Valery, Kichimi, Hiromichi, Kim, Doojin, Kitahara, Teppei, Kitano, Ryuichiro, Klamka, Jan, Komamiya, Sachio, Kong, K. C., Konomi, Taro, Kotera, Katsushige, Kou, Emi, Kravchenko, Ilya, Kubo, Kiyoshi, Kubo, Takayuki, Kumaoka, Takuya, Kumar, Ashish, Kumar, Nilanjana, Kunath, Jonas, Kundu, Saumyen, Kunitomo, Hiroshi, Kurata, Masakazu, Kuriki, Masao, Kusenko, Alexander, Lagouri, Theodota, Lankford, Andrew J., Lastovicka-Medin, Gordana, Diberder, Francois Le, Lee, Claire, Liepe, Matthias, Linacre, Jacob, Liptak, Zachary, Lomte, Shivani, Low, Ian, Ma, Yang, Maalouf, Hani, MacFarlane, David, Madison, Brendon, Madlener, Thomas, Maeda, Tomohito, Malek, Paul, Mandal, Sanjoy, Markiewicz, Thomas, Marshall, John, Martens, Aurelien, Martin, Victoria, Martinello, Martina, Rivero, Celso Martinez, Maru, Nobuhito, Matheson, John, Matsumoto, Shigeki, Matsunaga, Hiroyuki, Matsuo, Yutaka, Mawatari, Kentarou, Mbagwu, Johnpaul, McIntosh, Peter, McKeown, Peter, Meade, Patrick, Mekala, Krzysztof, Merkel, Petra, Mihara, Satoshi, Miralles, Víctor, Lopez, Marcos Miralles, Mishima, Go, Mishima, Satoshi, Mistlberger, Bernhard, Mitov, Alexander, Miyabayashi, Kenkichi, Miyamoto, Akiya, Mohanty, Gagan, Monaco, Laura, Mondragon, Myriam, Montgomery, Hugh E., Moortgat-Pick, Gudrid, Morange, Nicolas, Llacer, María Moreno, Moretti, Stefano, Mori, Toshinori, Morii, Toshiyuki, Moroi, Takeo, Morrissey, David, Nachman, Benjamin, Nagano, Kunihiro, Nakajima, Jurina, Nakamura, Eiji, Narita, Shinya, Nath, Pran, Nelson, Timothy, Newbold, David, Niki, Atsuya, Nishimura, Yasuhiro, Nishiyama, Eisaku, Nomura, Yasunori, Nowak, Kacper, Nozaki, Mitsuaki, de Vera, María Teresa Nunez Pardo, Ochoa, Ines, Ogata, Masahito, Ohashi, Satoru, Ohta, Hikaru, Ohta, Shigemi, Ohuchi, Norihito, Oide, Hideyuki, Okada, Nobuchika, Okada, Yasuhiro, Okawa, Shohei, Okayasu, Yuichi, Okugawa, Yuichi, Okugi, Toshiyuki, Okui, Takemichi, Okuyama, Yoshitaka, Omet, Mathieu, Omori, Tsunehiko, Ono, Hiroaki, Onoe, Tomoki, Ootani, Wataru, Otono, Hidetoshi, Ozawa, Shuhei, Griso, Simone Pagan, Papa, Alessandro, Paparella, Rocco, Park, Eun-Kyung, Perez, Gilad, Perez-Lorenzana, Abdel, Peters, Yvonne, Petriello, Frank, Piedra, Jonatan, Porod, Werner, Potter, Christopher, Price, Alan, Radkhorrami, Yasser, Reina, Laura, Reuter, Juergen, Richard, Francois, Riemann, Sabine, Rimmer, Robert, Rizzo, Thomas, Robens, Tania, Ruber, Roger, Jimeno, Alberto Ruiz, Saeki, Takayuki, Saha, Ipsita, Saito, Tomoyuki, Sakaguchi, Makoto, Sakai, Tadakatsu, Sakaki, Yasuhito, Sakurai, Kodai, Salvatico, Riccardo, Salvatore, Fabrizio, San, Yik Chuen, Sandick, Pearl, Sanuki, Tomoyuki, Sasikumar, Kollassery Swathi, Schaefer, Oliver, Schaefer, Ruth, Schneekloth, Uwe, Schoerner-Sadenius, Thomas, Schroeder, Carl, Schuster, Philip, Schwartzman, Ariel, Schwienhorst, Reinhard, Sefkow, Felix, Seiya, Yoshihiro, Sekiguchi, Motoo, Sekizawa, Kazuyuki, Senyo, Katsumi, Sert, Hale, Sertore, Danielev, Settles, Ronald, Shafi, Qaisar, Shahdara, Tetsuo, Haghi, Barmak Shams Es, Sharma, Ashish, Shelton, Jessie, Shepherd-Themistocleous, Claire, Shibuya, Hiroto, Shidara, Tetsuo, Shimomura, Takashi, Shindou, Tetsuo, Shoji, Yutaro, Shu, Jing, Sievers, Ian, Simon, Frank, Singh, Rajeev, Soreq, Yotam, Stanitzki, Marcel, Stapnes, Steinar, Steinhebel, Amanda, Stupak, John, Su, Shufang, Suekane, Fumihiko, Sugamoto, Akio, Sugawara, Yuji, Sugimoto, Satoru, Sugimoto, Yasuhiro, Sugiyama, Hiroaki, Sumino, Yukinari, Sundrum, Raman, Suzuki, Atsuto, Suzuki, Shin, Swiatlowski, Maximilian, Tait, Tim M. P., Takahashi, Shota, Takahashi, Tohru, Takeshita, Tohru, Takeuchi, Michihisa, Takubo, Yosuke, Tanabe, Tomohiko, Tanedo, Philip, Tanimoto, Morimitsu, Tao, Shuichiro, Tata, Xerxes, Tauchi, Toshiaki, Taylor, Geoffrey, Terada, Takahiro, Terunuma, Nobuhiro, Thaler, Jesse, Thea, Alessandro, Tillinger, Finn, Timmermans, Jan, Tobioka, Kohsaku, Toda, Kouichi, Tokiyasu, Atsushi, Toma, Takashi, Torndal, Julie, Tosun, Mehmet, Tsai, Yu-Dai, Tseng, Shih-Yen, Tsumura, Koji, Tuckler, Douglas, Uchida, Yoshiki, Uchiyama, Yusuke, Ueda, Daiki, Ukegawa, Fumihiko, Umemori, Kensei, Urakawa, Junji, Vallee, Claude, Vega, Roberto, Velasco, Liliana, Verdu-Andres, Silvia, Vernieri, Caterina, Vila, Anna, Alvarez, Ivan Vila, Vossebeld, Joost, Vsrms, Raghava, Vukasinovic, Natasa, Wackeroth, Doreen, Wakida, Moe, Wang, Liantao, Washio, Masakazu, Watanabe, Takashi, Watson, Nigel, Watts, Gordon, Weiglein, Georg, Wells, James D., Wenskat, Marc, Westhoff, Susanne, White, Glen, Williams, Ciaran, Willocq, Stephane, Wing, Matthew, Winter, Alasdair, Winter, Marc, Wu, Yongcheng, Xie, Keping, Xu, Tao, Yakovlev, Vyacheslav, Yamada, Shuei, Yamamoto, Akira, Yamamoto, Hitoshi, Yamamoto, Kei, Yamamoto, Yasuchika, Yamanaka, Masato, Yamashita, Satoru, Yamatani, Masahiro, Yamatsu, Naoki, Yasui, Shigehiro, Yoda, Takuya, Yonamine, Ryo, Yoshihara, Keisuke, Yoshioka, Masakazu, Yoshioka, Tamaki, Yuasa, Fukuko, Yumino, Keita, Zerwas, Dirk, Zheng, Ya-Juan, Zhou, Jia, Zhu, Hua Xing, Zobov, Mikhail, and Zomer, Fabian

Subjects
Physics - Accelerator Physics, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

The International Linear Collider (ILC) is on the table now as a new global energy-frontier accelerator laboratory taking data in the 2030s. The ILC addresses key questions for our current understanding of particle physics. It is based on a proven accelerator technology. Its experiments will challenge the Standard Model of particle physics and will provide a new window to look beyond it. This document brings the story of the ILC up to date, emphasizing its strong physics motivation, its readiness for construction, and the opportunity it presents to the US and the global particle physics community., Comment: 356 pages, Large pdf file (40 MB) submitted to Snowmass 2021; v2 references to Snowmass contributions added, additional authors; v3 references added, some updates, additional authors

Published
2022

26. Federated AirNet: Hybrid Digital-Analog Neural Network Transmission for Federated Learning

Author

Fujihashi, Takuya, Koike-Akino, Toshiaki, and Watanabe, Takashi

Subjects
Computer Science - Multimedia
Abstract

A key issue in federated learning over wireless channels is how to exchange a large number of the model parameters via time-varying channels. Two types of solutions based on digital and analog schemes are used typically. The digital-based solution takes quantization and entropy coding for compression, whereas transmissions via wireless channels may cause catastrophic errors owing to the all-or-nothing behavior in entropy coding. The analog-based solutions such as AirNet and AirComp use analog modulation for the parameter transmissions. However, such an analog scheme often causes significant distortion due to the source signal's large power without compression gain. This paper proposes a novel hybrid digital-analog transmission-Federated AirNet--for the model parameter transmissions in federated learning. The Federated AirNet integrates low-rate digital coding and energy-compact analog modulation. The digital coding offers the baseline of the model parameters and compacts the source signal power. In addition, the residual parameters, which are obtained from the original and encoded model parameters, are analog-modulated to enhance the baseline according to the instantaneous wireless channel quality. We show that the proposed Federated AirNet yields better image classification accuracy compared with the digital-based and analog-based solutions over a wide range of wireless channel signal-to-noise ratios (SNRs).

Published
2022

29. Soft Delivery: Survey on A New Paradigm for Wireless and Mobile Multimedia Streaming

Author

Fujihashi, Takuya, Koike-Akino, Toshiaki, and Watanabe, Takashi

Subjects
Computer Science - Multimedia
Abstract

The increasing demand for video streaming services is the key driver of modern wireless and mobile communications. For robust and high-quality delivery of video content over wireless and mobile networks, the main challenge is sending image and video signals to single and multiple users over unstable and diverse channel environments. To this end, many studies have designed digital-based video delivery schemes, which mainly consist of a sequence of digital-based coding and transmission schemes. Although digital-based schemes perform well when the channel characteristics are known in advance, significant quality degradation, known as cliff and leveling effects, often occurs owing to the fluctuating channel characteristics. To prevent cliff and leveling effects irrespective of the channel characteristics of each user, a new paradigm for wireless and mobile video streaming has been proposed. Soft delivery schemes skip the digital operations of quantization and entropy and channel coding while directly mapping the power-assigned frequency--domain coefficients onto the transmission symbols. This modification is based on the fact that the pixel distortion due to communication noise is proportional to the magnitude of the noise, resulting in graceful quality improvement, wherein quality is improved gradually, according to the wireless channel quality without any cliff and leveling effects. Herein, we present a comprehensive summary of soft delivery schemes.

Published
2021

30. System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability

Author

Nishi, Ryosuke and Watanabe, Takashi

Subjects
Physics - Physics and Society
Abstract

Sag is a road section where a downhill changes into an uphill, and is a highway bottleneck. We consider a system in which all vehicles are connected, and run on a single-lane road with a sag. We propose a simple strategy for removing each traffic jam caused by the sag. Our strategy assigns a vehicle upstream of the jam front to perform the jam-absorption driving (JAD): running toward the predicted goal, and finally removing the jam. We use a microscopic car-following model possessing the traffic instability, an acceleration model against the road gradient of a sag, and an instantaneous fuel consumption model. Our main goal is to elucidate the influence of the system size (the number of vehicles in the system) on our strategy. By increasing the system size from 500 to 10000 vehicles, we have found the following results for the average total travel time per vehicle, and the average total fuel consumption per vehicle. Our strategy can reduce the former with a slightly increasing rate of reduction. Our strategy can reduce the latter with a rate of reduction which decreases and becomes roughly constant. Optimal spatiotemporal scales of JAD for the former and the latter become roughly constant, respectively. Minimizing the former and the latter simultaneously is not possible. Not only vehicular traffic flow, but also the collective dynamics of other self-driven particles (such as ships, swarm robots, and pedestrians) lined up in a single column, and passing through a bottleneck can be modeled using our strategy.

Published
2021
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38. Learning Mobile CNN Feature Extraction Toward Fast Computation of Visual Object Tracking

Author

Murate, Tsubasa, Watanabe, Takashi, and Yamada, Masaki

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning, 68T10, 68T05, 68T07
Abstract

In this paper, we construct a lightweight, high-precision and high-speed object tracking using a trained CNN. Conventional methods with trained CNNs use VGG16 network which requires powerful computational resources. Therefore, there is a problem that it is difficult to apply in low computation resources environments. To solve this problem, we use MobileNetV3, which is a CNN for mobile terminals.Based on Feature Map Selection Tracking, we propose a new architecture that extracts effective features of MobileNet for object tracking. The architecture requires no online learning but only offline learning. In addition, by using features of objects other than tracking target, the features of tracking target are extracted more efficiently. We measure the tracking accuracy with Visual Tracker Benchmark and confirm that the proposed method can perform high-precision and high-speed calculation even in low computation resource environments., Comment: 9 pages, 17 figures

Published
2021

40. CSI2Image: Image Reconstruction from Channel State Information Using Generative Adversarial Networks

Author

Kato, Sorachi, Fukushima, Takeru, Murakami, Tomoki, Abeysekera, Hirantha, Iwasaki, Yusuke, Fujihashi, Takuya, Watanabe, Takashi, and Saruwatari, Shunsuke

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This study aims to find the upper limit of the wireless sensing capability of acquiring physical space information. This is a challenging objective, because at present, wireless sensing studies continue to succeed in acquiring novel phenomena. Thus, although a complete answer cannot be obtained yet, a step is taken towards it here. To achieve this, CSI2Image, a novel channel-state-information (CSI)-to-image conversion method based on generative adversarial networks (GANs), is proposed. The type of physical information acquired using wireless sensing can be estimated by checking wheth\-er the reconstructed image captures the desired physical space information. Three types of learning methods are demonstrated: gen\-er\-a\-tor-only learning, GAN-only learning, and hybrid learning. Evaluating the performance of CSI2Image is difficult, because both the clarity of the image and the presence of the desired physical space information must be evaluated. To solve this problem, a quantitative evaluation methodology using an object detection library is also proposed. CSI2Image was implemented using IEEE 802.11ac compressed CSI, and the evaluation results show that the image was successfully reconstructed. The results demonstrate that gen\-er\-a\-tor-only learning is sufficient for simple wireless sensing problems, but in complex wireless sensing problems, GANs are important for reconstructing generalized images with more accurate physical space information., Comment: 12 pages, 19 figures

Published
2020

41. Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks

Author

Fujihashi, Takuya, Koike-Akino, Toshiaki, Chen, Siheng, and Watanabe, Takashi

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Information Theory, Computer Science - Machine Learning
Abstract

In typical point cloud delivery, a sender uses octree-based digital video compression to send three-dimensional (3D) points and color attributes over band-limited links. However, the digital-based schemes have an issue called the cliff effect, where the 3D reconstruction quality will be a step function in terms of wireless channel quality. To prevent the cliff effect subject to channel quality fluctuation, we have proposed soft point cloud delivery called HoloCast. Although the HoloCast realizes graceful quality improvement according to wireless channel quality, it requires large communication overheads. In this paper, we propose a novel scheme for soft point cloud delivery to simultaneously realize better quality and lower communication overheads. The proposed scheme introduces an end-to-end deep learning framework based on graph neural network (GNN) to reconstruct high-quality point clouds from its distorted observation under wireless fading channels. We demonstrate that the proposed GNN-based scheme can reconstruct clean 3D point cloud with low overheads by removing fading and noise effects., Comment: 5 pages

Published
2020

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