Abstract
We propose Argus, a system to enable millimeter-wave (mmWave) deployers to quickly complete site-surveys without sacrificing the accuracy and effectiveness of thorough network deployment surveys. Argus first models the mmWave reflection profile of an environment, considering dominant reflectors, and then use this model to find locations that maximize the usability of the reflectors. The key component in Argus is an efficient machine learning model that can map the visual data to the mmWave signal reflections of an environment and can accurately predict mmWave signal profile at any unobserved locations. It allows Argus to find the best picocell locations to provide maximum coverage and also lets users self-localize accurately anywhere in the environment. Furthermore, Argus allows mmWave picocells to predict device's orientation accurately and enables object tagging and retrieval for VR/AR applications. Currently, we implement and test Argus on two different buildings consisting of multiple different indoor environments. However, the generalization capability of Argus can easily update the model for unseen environments, and thus, Argus can be deployed to any indoor environment with little or no model fine-tuning.
- IEEE P802.11 - Task Group ay, “Status of Project IEEE 802.11ay,” http://www.ieee802.org/11/Reports/tgay_update.htm, 2020.Google Scholar
- BIBentryALTinterwordspacing3GPP: A Global Initiative, “The Mobile Broadband Standard: Release 17,” 2021. [Online]. Available: http://www.3gpp.org/release-17BIBentrySTDinterwordspacingGoogle Scholar
- Mathew K. Samimi and Theodore S. Rappaport, “3-D Statistical Channel Model for Millimeter-Wave Outdoor Mobile Broadband Communications,” in IEEE International Conference on Communications (ICC), 2015.Google Scholar
- Theodore S. Rappaport and Eshar Ben-Dor and James N. Murdock and Yijun Qiao, “38 GHz and 60 GHz Angle-Dependent Propagation for Cellular and Peer-to-Peer Wireless Communications,” in IEEE International Conference on Communications (ICC), 2012.Google Scholar
- Hao Xu and Vikas Kukshya and Theodore S. Rappaport, “Spatial and Temporal Characteristics of 60-GHz Indoor Channels,” IEEE Journal on Selected Areas in Communications, vol. 20, no. 3, 2002.Google Scholar
- Sanjib Sur and Vignesh Venkateswaran and Xinyu Zhang and Parmesh Ramanathan, “60 GHz Indoor Networking through Flexible Beams: A Link-Level Profiling,” in Proc. of ACM SIGMETRICS, 2015.Google Scholar
- Sanjib Sur and Xinyu Zhang and Parmesh Ramanathan and Ranveer Chandra, “BeamSpy: Enabling Robust 60 GHz Links Under Blockage,” in Proceedings of USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2016.Google Scholar
- Christopher R. Anderson and Theodore S. Rappaport, “In-Building Wideband Partition Loss Measurements at 2.5 and 60 GHz,” IEEE Transactions on Wireless Communications, vol. 3, no. 3, 2004.Google Scholar
- Theodore S. Rappaport and Felix Gutierrez and Eshar Ben-Dor and James N. Murdock and Yijun Qiao and Jonathan I. Tamir, “Broadband Millimeter-Wave Propagation Measurements and Models Using Adaptive-Beam Antennas for Outdoor Urban Cellular Communications,” IEEE Transactions on Antennas and Propagation, vol. 61, no. 4, 2013.Google Scholar
- Peter F. M. Smulders, “Statistical Characterization of 60-GHz Indoor Radio Channels,” IEEE Transactions on Antennas and Propagation, vol. 57, no. 10, 2009.Google Scholar
Cross Ref
- Ahmed M. Al-Samman and Tharek A. Rahman and Marwan H. Azmi and M.N. Hindia, “Large-scale path loss models and time dispersion in an outdoor line-of-sight environment for 5G wireless communications,” AEU - International Journal of Electronics and Communications, vol. 70, no. 11, 2016.Google Scholar
- Virk, Usman Tahir and Haneda, Katsuyuki, “Modeling Human Blockage at 5G Millimeter-Wave Frequencies,” IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, 2020.Google Scholar
Cross Ref
- Moltchanov, Dmitri and Ometov, Aleksandr and Kustarev, Pavel and Evsutin, Oleg and Hosek, Jiri and Koucheryavy, Yevgeni, “Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment,” Sensors, vol. 20, no. 14, 2020.Google Scholar
- Sanjib Sur and Xinyu Zhang, “Scoping Environment for Robust 60 GHz Link Deployment,” in Invited paper at Information Theory and Applications, 2016.Google Scholar
- Wei, Teng and Zhou, Anfu and Zhang, Xinyu, “Facilitating Robust 60 GHz Network Deployment by Sensing Ambient Reflectors,” in Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation, 2017.Google Scholar
Digital Library
- Timothy Dayne Hooks and Hem Regmi and Sanjib Sur, “Poster: VisualMM: Visual Data and Learning Aided 5G Picocell Placement,” in Proceedings of ACM International Workshop on Mobile Computing Systems and Applications (HotMobile), 2021.Google Scholar
- BIBentryALTinterwordspacingCisco, “Site Survey Guidelines for WLAN Deployment,” 2021. [Online]. Available: https://www.cisco.com/c/en/us/support/docs/wireless/5500-series-wireless-controllers/116057-site-survey-guidelines-wlan-00.htmlBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingiBwave Solutions, “iBwave Design: A Single Solution to Streamline the Design of All Your Indoor Wireless Network Projects,” 2021. [Online]. Available: https://www.ibwave.com/ibwave-designBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingSecureEdge Networks, “Software that Makes WiFi More Intelligent,” 2021. [Online]. Available: https://www.securedgenetworks.com/softwareBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingTransition Products Inc., “Wireless Site Surveys,” 2021. [Online]. Available: https://www.tpi1.com/services/wireless-site-surveys/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingExterNetworks Inc., “Wireless Site Survey,” 2021. [Online]. Available: https://www.extnoc.com/wireless-site-surveyBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingVisiWave, “Visualize Your Wireless Network,” 2021. [Online]. Available: https://www.visiwave.com/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingMade by WiFi Inc., “How Much Does A Wireless Site Survey Cost And Is It Worth It For My Business” 2021. [Online]. Available: https://www.madebywifi.com/blog/how-much-does-a-wireless-site-survey-cost-and-is-it-worth-it-for-my-business/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingExterNetworks Inc., “Wireless Site Survey -- Outdoor Survey for upto 100,000 Sq. Ft,” 2021. [Online]. Available: https://www.extnoc.com/store/wireless-outdoor-survey-100000sqft/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingRemcom, “Wireless InSite: 3D Wireless Prediction Software,” 2021. [Online]. Available: https://www.remcom.com/wireless-insite-em-propagation-softwareBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingAltair Engineering, Inc., “Altair Feko Applications,” 2021. [Online]. Available: https://www.altair.com/feko-applications/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingSiradel, “Software for Wireless Network and Smart City Planning,” 2021. [Online]. Available: https://www.siradel.com/solutions/software/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingSonicWall, “WiFi Planner: Elevate Your WiFi User Experience with the Right Design,” 2021. [Online]. Available: https://www.sonicwall.com/products/secure-wireless/wifi-planner/BIBentrySTDinterwordspacingGoogle Scholar
- Sanjib Sur and Xinyu Zhang, “Scoping Environment to Assist 60 GHz Link Deployment,” in Proceedings of ACM International Conference on Mobile Computing and Networking (MobiCom) Poster, 2015.Google Scholar
- Basar, Ertugrul and Di Renzo, Marco and De Rosny, Julien and Debbah, Merouane and Alouini, Mohamed-Slim and Zhang, Rui, “Wireless Communications Through Reconfigurable Intelligent Surfaces,” IEEE Access, vol. 7, 2019.Google Scholar
- BIBentryALTinterwordspacingGsmarena, “Asus Zenfone AR ZS571KL,” 2021. [Online]. Available: https://www.gsmarena.com/asus_zenfone_ar_zs571kl-8502.phpBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingSilicon Radar GmbH, “24 GHz Products,” 2020. [Online]. Available: https://siliconradar.com/products/#24ghz-radar-chipsBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingSanjib Sur, “Software-Hardware Reconfigurable Systems for Mobile Millimeter-Wave Networks,” 2022. [Online]. Available: https://cse.sc.edu/ sur/projects/nsf1910853BIBentrySTDinterwordspacingGoogle Scholar
- M. Soumekh, Synthetic Aperture Radar Signal Processing .hskip 1em plus 0.5em minus 0.4emrelax John Wiley & Sons, Inc., 1999.Google Scholar
- Nozhan Hosseini and Mahfuza Khatun and Changyu Guo and Kairui Du and Ozgur Ozdemir and David Matolak and Ismail Guvenc and Hani Mehrpouyan, “Attenuation of Several Common Building Materials: Millimeter-Wave Frequency Bands 28, 73, and 91 GHz,” in IEEE Antennas and Propagation Magazine, 2021.Google Scholar
- Timothy A. Thomas and Huan Cong Nguyen and George R. MacCartney Jr. and Theodore S. Rappaport, “3D mmWave Channel Model Proposal,” in IEEE Vehicular Technology Conference, 2014.Google Scholar
- Yaniv Azar and George N. Wong and Kevin Wang and Rimma Mayzus and Jocelyn K. Schulz and Hang Zhao and Felix Gutierrez and DuckDong Hwang and Theodore S. Rappaport, “28 GHz Propagation Measurements for Outdoor Cellular Communications using Steerable Beam Antennas in New York City,” in IEEE International Conference on Communications (ICC), 2013.Google Scholar
- BIBentryALTinterwordspacingGoogle, “Tango,” 2014. [Online]. Available: https://www.youtube.com/watch?v=Qe10ExwzCqkBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacing----, “ARCore,” 2021. [Online]. Available: https://developers.google.com/arBIBentrySTDinterwordspacingGoogle Scholar
- Iro Armeni and Ozan Sener and Amir R. Zamir and Helen Jiang and Ioannis Brilakis and Martin Fischer and Silvio Savarese, “3D Semantic Parsing of Large-Scale Indoor Spaces,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2016.Google Scholar
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, 2004.Google Scholar
Digital Library
- Alexander Maltsev and Roman Maslennikov and Artyom Sevastyanov and Alexey Lomayev and Alexey Khoryaev, “Statistical Channel Model for 60 GHz WLAN Systems in Conference Room Environment,” in In Proceedings of the Fourth European Conference on Antennas and Propagation (EuCAP), 2010.Google Scholar
- Chao Dong and Chen Change Loy and Kaiming He and Xiaoou Tang, “Image Super-Resolution Using Deep Convolutional Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, 2016.Google Scholar
- Wenming Yang and Xuechen Zhang and Yapeng Tian and Wei Wang and Jing-Hao Xue and Qingmin Liao, “Deep Learning for Single Image Super-Resolution: A Brief Review,” IEEE Transactions on Multimedia, vol. 21, no. 12, 2019.Google Scholar
- Kevin de Haan and Yair Rivenson and Yichen Wu and Aydogan Ozcan, “Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy,” Proceedings of the IEEE, vol. 108, no. 1, 2020.Google Scholar
- Saiprasad Ravishankar and Jong Chul Ye and Jeffrey A. Fessler, “Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning,” Proceedings of the IEEE, vol. 108, no. 1, 2020.Google Scholar
- Florian Knoll and Kerstin Hammernik and Chi Zhang and Steen Moeller and Thomas Pock and Daniel K. Sodickson, “Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues,” IEEE Signal Processing Magazine, vol. 37, no. 1, 2020.Google Scholar
- Sarabandi, K and Vahidpour, M and Moallem, M and East, J, “Compact Beam Scanning 240 GHz Radar for Navigation and Collision Avoidance,” in Micro-and Nanotechnology Sensors, Systems, and Applications III, vol. 8031, 2011.Google Scholar
Cross Ref
- Mosalanejad, Mohammad and Ocket, Ilja and Soens, Charlotte and Vandenbosch, Guy AE, “Multilayer Compact Grid Antenna Array for 79 GHz Automotive Radar Applications,” IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 9, 2018.Google Scholar
- Rappaport, Theodore S, Wireless Communications: Principles and Practice .hskip 1em plus 0.5em minus 0.4emrelax Prentice Hall, 2002.Google Scholar
- Sanjib Sur and Ioannis Pefkianakis and Xinyu Zhang and Kyu-Han Kim, “Towards Scalable and Ubiquitous Millimeter-Wave Wireless Networks,” in Proc. of ACM International Conference on Mobile Computing and Networking (MobiCom), 2018.Google Scholar
- Song, Rongguo and Wang, Zhe and Zu, Haoran and Chen, Qiang and Mao, Boyang and Wu, Zhi Peng and He, Daping, “Wideband and Low Sidelobe Graphene Antenna Array for 5G Applications,” Sci. Bull, 2020.Google Scholar
- Huang, He and Li, Xiaoping and Liu, Yanming, “5G MIMO Antenna Based on Vector Synthetic Mechanism,” IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 6, 2018.Google Scholar
Cross Ref
- Pandya, Sharnil and Wakchaure, Manoj Ashok and Shankar, Ravi and Annam, Jagadeeswara Rao, “Analysis of NOMA-OFDM 5G Wireless System Using Deep Neural Network,” The Journal of Defense Modeling and Simulation, 2021.Google Scholar
- Li, Tian-Hao and Khandaker, Muhammad RA and Tariq, Faisal and Wong, Kai-Kit and Khan, Risala T, “Learning the wireless V2I channels using deep neural networks,” in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019.Google Scholar
Cross Ref
- Ravi, Nagarathna and Rani, P Vimala and Shalinie, S Mercy, “Secure Deep Neural (SeDeN) Framework for 5G Wireless Networks,” in 2019 10th International Conference on computing, communication and networking technologies (ICCCNT), 2019.Google Scholar
Cross Ref
- Gu, Jiuxiang and Wang, Zhenhua and Kuen, Jason and Ma, Lianyang and Shahroudy, Amir and Shuai, Bing and Liu, Ting and Wang, Xingxing and Wang, Gang and Cai, Jianfei and others, “Recent Advances in Convolutional Neural Networks,” Pattern Recognition, vol. 77, 2018.Google Scholar
- Zhang, Chen-Lin and Luo, Jian-Hao and Wei, Xiu-Shen and Wu, Jianxin, “In Defense of Fully Connected Layers in Visual Representation Transfer,” in Pacific Rim Conference on Multimedia, 2017.Google Scholar
- BIBentryALTinterwordspacingKeras, “Keras Applications,” 2021. [Online]. Available: https://keras.io/api/applications/BIBentrySTDinterwordspacingGoogle Scholar
- Karen Simonyan and Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations, 2015.Google Scholar
- He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.Google Scholar
- Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jon and Wojna, Zbigniew, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.Google Scholar
Cross Ref
- G. Huang and Z. Liu and L. Van Der Maaten and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.Google Scholar
- BIBentryALTinterwordspacingAndrew G. Howard and Menglong Zhu and Bo Chen and Dmitry Kalenichenko and Weijun Wang and Tobias Weyand and Marco Andreetto and Hartwig Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017. [Online]. Available: https://arxiv.org/abs/1704.04861BIBentrySTDinterwordspacingGoogle Scholar
- Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018.Google Scholar
Cross Ref
- J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.Google Scholar
Cross Ref
- Qi, Charles R. and Su, Hao and Mo, Kaichun and Guibas, Leonidas J., “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.Google Scholar
- BIBentryALTinterwordspacingScikit-Learn, “OneHotEncoder,” 2021. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.htmlBIBentrySTDinterwordspacingGoogle Scholar
- Tsai, Chun-Wei and Cho, Hsin-Hung and Shih, Timothy K and Pan, Jeng-Shyang and Rodrigues, Joel JPC, “Metaheuristics for the deployment of 5G,” IEEE Wireless Communications, vol. 22, no. 6, 2015.Google Scholar
- Xiao, Zhu and Liu, Hongjing and Havyarimana, Vincent and Li, Tong and Wang, Dong, “Analytical study on multi-tier 5G heterogeneous small cell networks: Coverage performance and energy efficiency,” Sensors, vol. 16, no. 11, 2016.Google Scholar
- Im, Chaehun and Jung, Sunghoon and Lee, Chungyoung, “A deep autoencoder approach to received signal strength-based localization with unknown channel parameters,” in 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2020.Google Scholar
Cross Ref
- Sathish, L and Bhuvaneswari, Y Satya and Devi, B Satya Sri and Nandan, Durgesh, “Analysis of Received Signal Strength Based on User Position Locating by Using ML Methods,” in International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, 2020.Google Scholar
- e Silva, Pedro Figueiredo and Richter, Philipp and Talvitie, Jukka and Laitinen, Elina and Lohan, Elena Simona, “Challenges and solutions in Received Signal Strength-based seamless positioning,” in Geographical and Fingerprinting Data to Create Systems for Indoor Positioning and Indoor/Outdoor Navigation, 2019.Google Scholar
- Menta, Estifanos Yohannes and Malm, Nicolas and J"antti, Riku and Ruttik, Kalle and Costa, Mário and Lepp"anen, Kari, “On the performance of AoA-based localization in 5G ultra--dense networks,” IEEE Access, vol. 7, 2019.Google Scholar
- Siriwardhana, Yushan and Porambage, Pawani and Liyanage, Madhusanka and Ylianttila, Mika, “A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects,” IEEE Communications Surveys Tutorials, vol. 23, no. 2, 2021.Google Scholar
- Yang, Chang-Fa and Wu, Boau-Cheng and Ko, Chuen-Jyi, “A Ray-Tracing Method for Modeling Indoor Wave Propagation and Penetration,” IEEE transactions on Antennas and Propagation, vol. 46, no. 6, 1998.Google Scholar
- Shihao Ju, Syed Hashim Ali Shah, Muhammad Affan Javed, Jun Li, Girish Palteru, Jyotish Robin, Yunchou Xing, Ojas Kanhere, Theodore S. Rappaport, “Scattering Mechanisms and Modeling for Terahertz Wireless Communications,” in IEEE International Conference on Communications (ICC), 2019.Google Scholar
- Amini, Navid and Sarrafzadeh, Majid and Vahdatpour, Alireza and Xu, Wenyao, “Accelerometer-based on-body sensor localization for health and medical monitoring applications,” Pervasive and mobile computing, vol. 7, no. 6, 2011.Google Scholar
- Hsu, Ching-Hsien and Yu, Chia-Hao, “An accelerometer based approach for indoor localization,” in 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing. hskip 1em plus 0.5em minus 0.4emrelax IEEE, 2009, pp. 223--227.Google Scholar
Digital Library
- Alam, Fakhrul and Faulkner, Nathaniel and Legg, Mathew and Demidenko, Serge, “Indoor visible light positioning using spring-relaxation technique in real-world setting,” Ieee Access, vol. 7, 2019.Google Scholar
Cross Ref
- Sarfraz, M and Rizvi, SM Ali J, “Indoor navigational aid system for the visually impaired,” in Geometric Modeling and Imaging (GMAI'07). hskip 1em plus 0.5em minus 0.4emrelax IEEE, 2007, pp. 127--132.Google Scholar
- Ayyalasomayajula, Roshan and Arun, Aditya and Wu, Chenfeng and Sharma, Sanatan and Sethi, Abhishek Rajkumar and Vasisht, Deepak and Bharadia, Dinesh, “Deep Learning Based Wireless Localization for Indoor Navigation,” in Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, 2020.Google Scholar
Digital Library
- Jain, Puneet and Manweiler, Justin and Roy Choudhury, Romit, “OverLay: Practical Mobile Augmented Reality,” in Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, 2015.Google Scholar
Digital Library
- “Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names,” https://gombru.github.io/2018/05/23/, 2021.Google Scholar
- Sheen, David M and McMakin, Douglas L and Hall, Thomas E, “Three-dimensional millimeter-wave imaging for concealed weapon detection,” IEEE Transactions on microwave theory and techniques, vol. 49, no. 9, 2001.Google Scholar
- “Real-Time Appearance-Based Mapping,” http://introlab.github.io/rtabmap/, 2021.Google Scholar
- “Optimizers,” https://keras.io/api/optimizers/, 2021.Google Scholar
- BIBentryALTinterwordspacingOpen-Source, “TensorFlow,” 2021. [Online]. Available: https://www.tensorflow.org/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingNVIDIA, “GEFORCE,” 2021. [Online]. Available: https://www.nvidia.com/en-us/geforce/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingGoogle, “Cloud TPU,” 2021. [Online]. Available: https://cloud.google.com/tpuBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingNVIDIA, “RTX A6000,” 2021. [Online]. Available: https://www.nvidia.com/en-us/design-visualization/rtx-a6000/BIBentrySTDinterwordspacingGoogle Scholar
- Erik Dahlman and Stefan Parkvall and Johan Skold, 5G NR: The Next Generation Wireless Access Technology .hskip 1em plus 0.5em minus 0.4emrelax Elsevier, 2018.Google Scholar
- BIBentryALTinterwordspacingGoogle, “Google Street View,” 2021. [Online]. Available: https://www.google.com/streetview/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingVerizon, “Explore 4G LTE and 5G Network Coverage in Your Area,” 2021. [Online]. Available: https://www.verizon.com/coverage-map/BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingT-Mobile USA, Inc., “5G & 4G LTE Coverage,” 2021. [Online]. Available: https://www.t-mobile.com/coverage/coverage-mapBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingAT&T, “Nationwide 5G,” 2021. [Online]. Available: https://www.att.com/5g/coverage-map/BIBentrySTDinterwordspacingGoogle Scholar
- Stefan Parkvall and Erik Dahlman and Anders Furuskar and Mattias Frenne, “NR: The New 5G Radio Access Technology,” IEEE Communications Standards Magazine, vol. 1, no. 4, 2017.Google Scholar
- BIBentryALTinterwordspacingTelefonaktiebolaget L. M. Ericsson, “5G Fixed Wireless Access,” 2021. [Online]. Available: https://www.ericsson.com/en/fixed-wireless-accessBIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingDARPA, “DARPA T-MUSIC,” 2020. [Online]. Available: https://www.darpa.mil/news-events/2020-02-04BIBentrySTDinterwordspacingGoogle Scholar
- BIBentryALTinterwordspacingVTT Technical Research Centre of Finland Ltd., “DREAM: D-Band Radio Solution Enabling Up To 100 Gbps Reconfigurable Approach for Meshed Beyond 5G networks,” 2021. [Online]. Available: http://www.h2020-dream.eu/BIBentrySTDinterwordspacingGoogle Scholar
- Fadhil Firyaguna and Jacek Kibilda and Carlo Galiotto and Nicola Marchetti, “Coverage and Spectral Efficiency of Indoor mmWave Networks with Ceiling-Mounted Access Points,” in IEEE Global Communications Conference, 2017.Google Scholar
- Rony Kumer Saha, “On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks,” MDPI Sensors, vol. 20, no. 6, 2020.Google Scholar
- Alimpertis, Emmanouil and Markopoulou, Athina and Butts, Carter and Psounis, Konstantinos, “City-Wide Signal Strength Maps: Prediction with Random Forests,” in The World Wide Web Conference, 2019.Google Scholar
Digital Library
- Sulyman, Ahmed Iyanda and Nassar, Almuthanna T. and Samimi, Mathew K. and Maccartney, George R. and Rappaport, Theodore S. and Alsanie, Abdulhameed, “Radio propagation path loss models for 5G cellular networks in the 28 GHZ and 38 GHZ millimeter-wave bands,” IEEE Communications Magazine, vol. 52, no. 9, 2014.Google Scholar
Cross Ref
- Deng, Sijia and MacCartney, Geoge R. and Rappaport, Theodore S., “Indoor and Outdoor 5G Diffraction Measurements and Models at 10, 20, and 26 GHz,” in 2016 IEEE Global Communications Conference (GLOBECOM), 2016.Google Scholar
Digital Library
- Hossain, Ferdous and Geok, Tan Kim and Rahman, Tharek Abd and Hindia, Mohammad Nour and Dimyati, Kaharudin and Ahmed, Sharif and Tso, Chih P. and Abd Rahman, Noor Ziela, “An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network,” Electronics, vol. 8, no. 3, 2019.Google Scholar
- Narayanan, Arvind and Ramadan, Eman and Mehta, Rishabh and Hu, Xinyue and Liu, Qingxu and Fezeu, Rostand A. K. and Dayalan, Udhaya Kumar and Verma, Saurabh and Ji, Peiqi and Li, Tao and Qian, Feng and Zhang, Zhi-Li, “Lumos5G: Mapping and Predicting Commercial MmWave 5G Throughput,” in Proceedings of the ACM Internet Measurement Conference, 2020.Google Scholar
Digital Library
- Wei, Teng and Zhang, Xinyu, “Pose Information Assisted 60 GHz Networks: Towards Seamless Coverage and Mobility Support,” in Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking. hskip 1em plus 0.5em minus 0.4emrelax New York, NY, USA: Association for Computing Machinery, 2017.Google Scholar
Digital Library
- Tsukamoto, Yu and Hirayama, Haruhisa and Moon, Seung II and Nanba, Shinobu and Shinbo, Hiroyuki, “Feedback Control for Adaptive Function Placement in Uncertain Traffic Changes on an Advanced 5G System,” in 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC), 2021.Google Scholar
Digital Library
- Mathew, Anitha P and Arthi, M and Babu, K Vinoth, “An uniform clustering based coverage and cost effective placement of serving nodes for 5G,” in 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT), 2017.Google Scholar
Cross Ref
- Khan, Shah Khalid and Naseem, Usman and Sattar, Abdul and Waheed, Nazar and Mir, Adnan and Qazi, Atika and Ismail, Muhammad, “UAV-aided 5G Network in Suburban, Urban, Dense Urban, and High-rise Urban Environments,” in 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA), 2020.Google Scholar
Cross Ref
- Bartoletti, Stefania and Conti, Andrea and Dardari, Davide and Giorgetti, Andrea, “5G localization and context-awareness,” University of Bologna, University of Ferrara, 2018.Google Scholar
- Khan, Muhammad Alee and Saeed, Nasir and Ahmad, Arbab Waheed and Lee, Chankil, “Location Awareness in 5G Networks Using RSS Measurements for Public Safety Applications,” IEEE Access, 2017.Google Scholar
- Belay, Abebe and Yen, Lei and Renu, Sakthidasan and Lin, Hsin-piao and Jeng, Shiann-Shiun, “Indoor Localization at 5 GHz Using Dynamic Machine Learning Approach (DMLA),” in 2017 International Conference on Applied System Innovation (ICASI), 2017.Google Scholar
Cross Ref
- Meng, Jiayi and Sharma, Abhigyan and Tran, Tuyen X. and Balasubramanian, Bharath and Jung, Gueyoung and Hiltunen, Matti and Charlie Hu, Y., “A Study of Network-Side 5G User Localization Using Angle-Based Fingerprints,” in 2020 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN, 2020.Google Scholar
Cross Ref
- Pandey, Ankur and Pinky, Pinky and Kumar, Sudhir, “Localization Using Stochastic Gradient Descent Method in a 5G Network,” in 2018 15th IEEE India Council International Conference (INDICON), 2018.Google Scholar
Cross Ref
- El Boudani, Brahim and Kanaris, Loizos and Kokkinis, Akis and Kyriacou, Michalis and Chrysoulas, Christos and Stavrou, Stavros and Dagiuklas, Tasos, “Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture),” Sensors, vol. 20, 2020.Google Scholar
- Klus, Roman and Klus, Lucie and Solomitckii, Dmitrii and Valkama, Mikko and Talvitie, Jukka, “Deep Learning Based Localization and HO Optimization in 5G NR Networks,” in 2020 International Conference on Localization and GNSS (ICL-GNSS), 2020.Google Scholar
Cross Ref
- Butt, M. Majid and Rao, Anil and Yoon, Daejung, “RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G,” in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020.Google Scholar
Cross Ref
- Jason Orlosky and Kiyoshi Kiyokawa and Haruo Takemura, “Virtual and Augmented Reality on the 5G Highway,” Journal of Information Processing, vol. 25, 2017.Google Scholar
- Baratè, Adriano and Haus, Goffredo and Ludovico, Luca Andrea and Pagani, Elena and Scarabottolo, Nello, “5G technology for augmented and virtual reality in education,” in Proceedings of the International Conference on Education and New Developments, 2019.Google Scholar
Cross Ref
- Verde, Sebastiano and Marcon, Marco and Milani, Simone and Tubaro, Stefano, “Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking,” Sensors, vol. 20, no. 24, 2020.Google Scholar
Index Terms
Argus: Predictable Millimeter-Wave Picocells with Vision and Learning Augmentation
Recommendations
Argus: Predictable Millimeter-Wave Picocells with Vision and Learning Augmentation
SIGMETRICS/PERFORMANCE '22: Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer SystemsWe propose Argus, a system to enable millimeter-wave (mmWave) deployers to quickly complete site-surveys without sacrificing the accuracy and effectiveness of thorough network deployment surveys. Argus first models the mmWave reflection profile of an ...
Argus: Predictable Millimeter-Wave Picocells with Vision and Learning Augmentation
SIGMETRICS '22We propose Argus, a system to enable millimeter-wave (mmWave) deployers to quickly complete site-surveys without sacrificing the accuracy and effectiveness of thorough network deployment surveys. Argus first models the mmWave reflection profile of an ...
Compact Patch Antenna Array for 60 GHz Millimeter-Wave Broadband Applications
AbstractIn this study, a wide-band compact patch antenna array is developed for 60 GHz band applications. The antenna array consists of eight identical elements. Each element is a simple microstrip-fed rectangular radiating patch printed on the top side ...






Comments