Assessing Wireless Sensing Potential with Large Intelligent Surfaces

Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention received by LIS in terms of communication aspects, it can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment relying on the received signal power, we develop techniques to sense the environment, by leveraging the tools of image processing and machine learning. Once a holographic image is obtained, a Denoising Autoencoder (DAE) network can be used for constructing a super-resolution image leading to sensing advantages not available in traditional sensing systems. Also, we derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.

[1]  Jingon Joung,et al.  Machine Learning-Based Antenna Selection in Wireless Communications , 2016, IEEE Communications Letters.

[2]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[3]  Ertugrul Basar,et al.  Transmission Through Large Intelligent Surfaces: A New Frontier in Wireless Communications , 2019, 2019 European Conference on Networks and Communications (EuCNC).

[4]  Fredrik Rusek,et al.  Cramér-Rao Lower Bounds for Positioning with Large Intelligent Surfaces , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[5]  Rob Miller,et al.  Demo: real-time breath monitoring using wireless signals , 2014, MobiCom.

[6]  Merouane Debbah,et al.  Reconfigurable Intelligent Surfaces and Metamaterials: The Potential of Wave Propagation Control for 6G Wireless Communications , 2020, ArXiv.

[7]  Mohamed-Slim Alouini,et al.  Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead , 2020, ArXiv.

[8]  W. F. Scott.  On the Asymptotic Distribution of the Likelihood Ratio Statistic , 2007 .

[9]  Yonina C. Eldar,et al.  Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications , 2020, IEEE Wireless Communications.

[10]  Tareq Y. Al-Naffouri,et al.  On the Distribution of Indefinite Quadratic Forms in Gaussian Random Variables , 2009, IEEE Transactions on Communications.

[11]  Kerstin Vogler,et al.  Table Of Integrals Series And Products , 2016 .

[12]  Suresh Venkatasubramanian,et al.  Radio tomographic imaging and tracking of stationary and moving people via kernel distance , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[13]  Henk Wymeersch,et al.  Radio Localization and Mapping With Reconfigurable Intelligent Surfaces: Challenges, Opportunities, and Research Directions , 2020, IEEE Vehicular Technology Magazine.

[14]  Antonio Torralba,et al.  Through-Wall Human Pose Estimation Using Radio Signals , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Theodore S. Rappaport,et al.  New analytical models and probability density functions for fading in wireless communications , 2002, IEEE Trans. Commun..

[16]  José F. Paris,et al.  New Approximation to Distribution of Positive RVs Applied to Gaussian Quadratic Forms , 2019, IEEE Signal Processing Letters.

[17]  Henk Wymeersch,et al.  Large Intelligent Surface for Positioning in Millimeter Wave MIMO Systems , 2019, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[18]  A. M. Mathai,et al.  Quadratic forms in random variables : theory and applications , 1992 .

[19]  Neal Patwari,et al.  Radio Tomographic Imaging with Wireless Networks , 2010, IEEE Transactions on Mobile Computing.

[20]  Emil Björnson,et al.  Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays , 2019, ArXiv.

[21]  Daqing Zhang,et al.  RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices , 2017, IEEE Transactions on Mobile Computing.

[22]  Alessio Zappone,et al.  Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends , 2020, IEEE Wireless Communications.

[23]  Fumiyuki Adachi,et al.  Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding , 2019, IEEE Transactions on Vehicular Technology.

[24]  Saumik Bhattacharya,et al.  Effects of Degradations on Deep Neural Network Architectures , 2018, ArXiv.

[25]  Rui Zhang,et al.  Secure Wireless Communication via Intelligent Reflecting Surface , 2019, IEEE Wireless Communications Letters.

[26]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[27]  Stefan Winkler,et al.  Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning , 2015, ICMI.

[28]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[29]  Fredrik Rusek,et al.  Beyond Massive MIMO: The Potential of Positioning With Large Intelligent Surfaces , 2017, IEEE Transactions on Signal Processing.

[30]  Guan Gui,et al.  Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System , 2018, IEEE Transactions on Vehicular Technology.

[31]  Emil Björnson,et al.  Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces , 2020, IEEE Open Journal of the Communications Society.

[32]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[33]  Mohamed-Slim Alouini,et al.  Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come , 2019, EURASIP Journal on Wireless Communications and Networking.

[34]  Pan Li,et al.  Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach , 2020, IEEE Transactions on Network Science and Engineering.

[35]  Shi Jin,et al.  Deep Learning for Massive MIMO CSI Feedback , 2017, IEEE Wireless Communications Letters.

[36]  A. Lozano,et al.  What Will 5 G Be ? , 2014 .

[37]  Qingqing Wu,et al.  Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming , 2018, IEEE Transactions on Wireless Communications.

[38]  Justin P. Coon,et al.  Communication Through a Large Reflecting Surface With Phase Errors , 2019, IEEE Wireless Communications Letters.

[39]  Serkan Günal,et al.  A comparative study on machine learning algorithms for indoor positioning , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[40]  J. Idier,et al.  Properties of Fisher information for Rician distributions and consequences in MRI , 2014 .

[41]  Zhen Gao,et al.  Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO , 2020, IEEE Transactions on Vehicular Technology.

[42]  Jan Sijbers,et al.  Maximum-likelihood estimation of Rician distribution parameters , 1998, IEEE Transactions on Medical Imaging.

[43]  Fredrik Rusek,et al.  Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces , 2017, IEEE Transactions on Signal Processing.

[44]  Erik G. Larsson,et al.  Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.

[45]  Mohamed-Slim Alouini,et al.  Wireless Communications Through Reconfigurable Intelligent Surfaces , 2019, IEEE Access.

[46]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[47]  Elisabeth de Carvalho,et al.  A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting , 2020, CrownCom.

[48]  Emil Björnson,et al.  Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning , 2020, IEEE Open Journal of the Communications Society.

[49]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[50]  Wei Xu,et al.  Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications , 2019, IEEE Communications Letters.

[51]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[52]  Chau Yuen,et al.  Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[53]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[54]  D. Dardari.  Communicating With Large Intelligent Surfaces: Fundamental Limits and Models , 2019, IEEE Journal on Selected Areas in Communications.

[55]  Mohamed-Slim Alouini,et al.  Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques , 2020, IEEE Access.

[56]  F. Javier Lopez-Martinez,et al.  Physical Layer Security of Large Reflecting Surface Aided Communications With Phase Errors , 2020, IEEE Wireless Communications Letters.