IP Library › Granted Patent US 12,689,132
Granted Patent B2
US 12,689,132 · App. 18/342,181 · Granted Jul 21, 2026

Large intelligent surfaces with sparse channel sensors

Inventors: Ahmed Alkhateeb (Chandler, AZ); Abdelrahman Taha (Tempe, AZ); Muhammed Alrabeiah (Tempe, AZ)
Assignee: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY
H01Q15/148
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Quick Facts
Patent No.
US 12,689,132
App. No.
18/342,181
Filed
Jun 27, 2023
Granted
Jul 21, 2026
Kind
B2
Art Unit
2649
USPC
455/7
Abstract

Large intelligent surfaces (LISs) with sparse channel sensors are provided. Embodiments described herein provide efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. Consequently, an LIS architecture based on sparse channel sensors is provided where all LIS elements are passive reconfigurable elements except for a few elements that are active (e.g., connected to baseband). Two solutions are developed that design LIS reflection matrices with negligible training overhead. First, compressive sensing tools are leveraged to construct channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. Second, a deep learning-based solution is deployed where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations.

Claims (44)

1 . A method for operating a large intelligent surface (LIS), the method comprising:

configuring the LIS with an array of passive reconfigurable reflecting elements and a plurality of active sparse channel sensing elements;

operating the plurality of active sparse channel sensing elements among the array of passive reconfigurable reflecting elements in a channel sensing mode, wherein each of the array of passive reconfigurable reflecting elements has a reconfigurable phase state; and

adjusting the corresponding phase states of the plurality of passive reconfigurable reflecting elements based on output of the channel sensing mode.

2 . The method of claim 1 , further comprising:

operating the plurality of active sparse channel sensing elements in a reflection mode based on the output of the channel sensing mode.

3 . The method of claim 1 , further comprising:

receiving sensor data from at least one of a video sensor, an audio sensor, a radar sensor, or a proximity sensor,

wherein adjusting the plurality of passive reconfigurable reflecting elements is further based on the sensor data.

4 . The method of claim 1 , wherein operating the plurality of active sparse channel sensing elements in the channel sensing mode comprises:

receiving one or more sampled channel vectors; and

determining controllable parameters of the plurality of passive reconfigurable reflecting elements from the one or more sampled channel vectors.

5 . The method of claim 4 , further comprising:

determining the controllable parameters based on extrapolating channels for some or all of the plurality of passive reconfigurable reflecting elements using sampled channels of the plurality of active sparse channel sensing elements.

6 . The method of claim 4 , further comprising:

determining the controllable parameters based on compressive sensing.

7 . The method of claim 4 , further comprising:

determining the controllable parameters based on artificial intelligence.

8 . The method of claim 7 , wherein the artificial intelligence comprises a deep learning algorithm implemented on a neural network and configured to resolve a reflection beamforming vector for the plurality of passive reconfigurable reflecting elements.

9 . The method of claim 8 , further comprising:

pre-training the deep learning algorithm to learn an environment around the LIS.

10 . The method of claim 1 , further comprising:

coupling the LIS to the plurality of active sparse channel sensing elements with one or more radio frequency (RF) chains.

11 . The method of claim 10 , further comprising:

selectively connecting each of the plurality of active sparse channel sensing elements is to a corresponding RF chain,

wherein each of the plurality of active sparse channel sensing elements is operable in a passive reflection mode when disconnected from the corresponding RF chain.

12 . The method of claim 11 , further comprising:

adjusting the plurality of active sparse channel sensing elements when in the passive reflection mode using a reflection matrix.

13 . The method of claim 12 , further comprising:

coupling the LIS to one or more sensors; and

resolving the reflection matrix based on input from the plurality of active sparse channel sensing elements and the one or more sensors.

14 . The method of claim 1 , wherein the array of passive reconfigurable reflecting elements comprises multiple collocated groups of passive reconfigurable reflecting elements.

15 . The method of claim 10 , wherein the array of passive reconfigurable reflecting elements comprises multiple distributed groups of passive reconfigurable reflecting elements.

16 . The method of claim 10 , further comprising:

estimating a wireless channel using the plurality of active sparse channel sensing elements;

adjusting the reconfigurable phase state of each of the array of passive reconfigurable reflecting elements based on the estimated wireless channel; and

resolving a reflection matrix for the reconfigurable phase state of each of the array of passive reconfigurable reflecting elements to facilitate wireless communication over the estimated wireless channel using a deep learning algorithm trained using past inputs from the plurality of active sparse channel sensing elements,

wherein the plurality of active sparse channel sensing elements is fewer in number than the array of passive reconfigurable reflecting elements.

17 . The method of claim 1 , wherein each of the array of passive reconfigurable reflecting elements comprises a corresponding reconfigurable radio frequency (RF) phase shifter.

18 . The method of claim 11 , wherein each of the plurality of active sparse channel sensing elements comprises a corresponding reconfigurable RF phase shifter.

19 . The method of claim 18 , wherein each of the plurality of active sparse channel sensing elements is operable in an active sensing mode and a passive reflection mode.

20 . The method of claim 19 , wherein

in the active sensing mode, each of the plurality of active sparse channel sensing elements is connected to a corresponding RF chain, and

in the passive reflection mode, each of the plurality of active sparse channel sensing elements is connected to the corresponding RF phase shifter and disconnected from the corresponding RF chain.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 18, 2023
From: ALKHATEEB, AHMED; TAHA, ABDELRAHMAN; ALRABEIAH, MUHAMMED
To: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY
Reel/Frame 064302/0429 →
Continuity (3)
Division 16901899 · Jun 15, 2020
Provisional Application 62873551 · Jul 12, 2019
Related Publication 20230352847A1 · Nov 2, 2023
References Cited (116)
US 7920069B2 · Shain et al. · 2011 [cited by applicant]
US 9453905B2 · Smith · 2016 [cited by applicant]
US 10395539B2 · Slutsky et al. · 2019 [cited by applicant]
US 10469619B2 · Shimizu et al. · 2019 [cited by applicant]
US 20030016737A1 · Wu et al. · 2003 [cited by applicant]
US 20070198227A1 · Dundar et al. · 2007 [cited by applicant]
US 20110244786A1 · Fujii · 2011 [cited by examiner]
US 20130194127A1 · Ishihara et al. · 2013 [cited by applicant]
US 20150111597A1 · Lavery · 2015 [cited by applicant]
US 20160146945A1 · Kamijo et al. · 2016 [cited by applicant]
US 20160359230A1 · Wang · 2016 [cited by examiner]
US 20170006436A1 · Ahmed et al. · 2017 [cited by applicant]
US 20170047663A1 · Hall · 2017 [cited by examiner]
US 20170255360A1 · Hsieh et al. · 2017 [cited by applicant]
US 20170287334A1 · Slutsky et al. · 2017 [cited by applicant]
US 20180316090A1 · Foo · 2018 [cited by examiner]
US 20190044246A1 · Pitsillides et al. · 2019 [cited by applicant]
US 20190101638A1 · Vu et al. · 2019 [cited by applicant]
US 20190318146A1 · Trichopoulos et al. · 2019 [cited by applicant]
US 20190327625A1 · Patel et al. · 2019 [cited by applicant]
US 20200005385A1 · Stout et al. · 2020 [cited by applicant]
US 20200005911A1 · Brooks et al. · 2020 [cited by applicant]
US 20200025911A1 · Rappaport · 2020 [cited by applicant]
US 20200028262A1 · Fang · 2020 [cited by examiner]
US 20200132826A1 · Achour · 2020 [cited by applicant]
US 20200136718A1 · Fang · 2020 [cited by applicant]
US 20200163040A1 · Trichopoulos et al. · 2020 [cited by applicant]
US 20200250409A1 · Hu · 2020 [cited by applicant]
US 20210384637A1 · Sciancalepore et al. · 2021 [cited by applicant]
WO 2017216745A1 · 2017 [cited by applicant]
Durrant-Whyte, H., et al., “Simultaneous Localization and Mapping: Part I,” IEEE Robotics & Automation Magazine, vol. 13, No. 2, Jun. 2006, IEEE, pp. 99-108. [cited by applicant]
Faisal et al., “Ultra-Massive MIMO Systems at Terahertz Bands: Prospects and Challenges,” arXiv eprint, arXiv:1902.11090v1 [eess. SP], Feb. 28, 2019, 7 pages, https://arxiv.org/pdf/1902.11090v1.pdf. [cited by applicant]
Foo, S., “Liquid-crystal Reconfigurable Metasurface Reflectors,” 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Jul. 9-14, 2017, San Diego, CA, pp. 2069-2070. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 16/684,746, mailed Jan. 6, 2021, 9 pages. [cited by applicant]
Guidi, F., et al., “Personal Mobile Radars with Millimeter-Wave Massive Arrays for Indoor Mapping,” IEEE Transactions on Mobile Computing, vol. 15, No. 6, Jun. 2016, IEEE, pp. 1471-1484. [cited by applicant]
Heath et al., “An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, No. 3, Feb. 8, 2016, pp. 436-453. [cited by applicant]
Hornik et al., “Multilayer Feedforward Networks are Universal Approximators,” Neural Networks, vol. 2, No. 5, 1989, pp. 359-366. [cited by applicant]
Hu et al., “Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces,” IEEE Transactions on Signal Processing, vo. 66, No. 10, Mar. 19, 2018, pp. 2746-2758. [cited by applicant]
Huang et al., “Large Intelligent Surfaces for Energy Efficiency in Wireless Communication,” arXiv eprint, arXiv:1810.06934v1 [cs.IT], Oct. 16, 2018, <https://arxiv.org/abs/1810.06934v1>. [cited by applicant]
Huang et al., “Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication,” IEEE Transactions on Wireless Communications, vol. 18, No. 8, Jun. 19, 2019, pp. 4157-4170. [cited by applicant]
Hum et al., “Reconfigurable Reflectarrays and Array Lenses for Dynamic Antenna Beam Control: A Review,” IEEE Transactions on Antennas and Propagation, vol. 62, No. 1, Oct. 25, 2013, pp. 183-198. [cited by applicant]
Hur et al., “Millimeter Wave Beamforrning for Wireless Backhaul and Access in Small Cell Networks,” IEEE Transactions on Communications, vol. 61, No. 10, Sep. 16, 2013, pp. 4391-4403. [cited by applicant]
Jung et al., “Performance Analysis of Large Intelligent Surfaces {LISs): Asymptotic Data Rate and Channel Hardenin 1 Effects,” arXiv eprint, arXiv:1810.05667v1 [cs.IT], Oct. 12, 2018, 30 pages, https://arxiv.org/pdf/181… [cited by applicant]
Kanhere, O., et al., “Map-Assisted Millimeter Wave Localization for Accurate Position Location,” 2019 IEEE Global Communications Conference, Dec. 2019, IEEE, pp. 1-6. [cited by applicant]
Kanhere, 0., et al., “Position Locationing for Millimeter Wave Systems,” 2018 IEEE Global Communications Conference, Dec. 2018, pp. 1-6. [cited by applicant]
Lecun et al., “Efficient BackProp,” Neural Networks: Tricks of the Trade, 2nd edition, Springer-Verlag Berlin Heidelberg, 2012, pp. 9-48. [cited by applicant]
Lee et al., “Exploiting Spatial Sparsity for Estimating Channels of Hybrid MIMO Systems in Millimeter Wave Communications,” 2014 IEEE Global Communications Conference, Dec. 8-12, 2014, Austin, TX, pp. 3326-3331. [cited by applicant]
Lemic, F., et al., “Localization as a Feature of mmWave Communication,” 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Sep. 2016, IEEE, pp. 1033-1038. [cited by applicant]
Li et al., Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems, 2018 52nd Asilomar Conference on Signals, Systems, and Computers, IEEE, Oct. 28-31, 2018, Pacific Grove, CA, pp. 1… [cited by applicant]
Liaskos et al., “A new wireless communication paradigm through software-controlled metasurfaces,” IEEE Communications Magazine, vol. 56, No. 9, Sep. 17, 2018, pp. 162-169. [cited by applicant]
Lim et al., “A 100 MS/s, 10.5 Bil, 2.46 mW Comparatorless Pipeline ADC Using Self-biased Ring Amplifiers,” IEEE Journal of Solid-Slate Circuits, vol. 50, No. 10, Jul. 27, 2015, pp. 2331-2341. [cited by applicant]
Maletic, N., et al., “Device Localization Using mmWave Ranging with Sub-6-Assisled Angle of Arrival Estimation,” 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Jun. 2018, IEEE… [cited by applicant]
Mendez-Rial et al., “Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?” IEEE Access, vol. 4, Jan. 1, 2016, pp. 247-267. [cited by applicant]
Mo et al., “Hybrid Architectures with Few-bit ADC Receivers: Achievable Rates and Energy-rate Tradeoffs,” IEEE Transactions on Wireless Communications, vol. 16, No. 4, Mar. 13, 2017, pp. 2274-2287. [cited by applicant]
Murmann, B., “ADC Performance Survey 1997-2020,” updated Oct. 3, 2023, 18 pages [retrieved on Dec. 28, 2023]. Retrievedfrom the Internet: <URL: https://github.com/bmurmann/ADC-survey>. [cited by applicant]
Nadeem et al., “Large Intelligent Surface Assisted MIMO Communications,” arXiv eprint, arXiv: 1903.08127v1 [cs.IT], Mar. 19, 2019, 37 pages, https://arxiv.org/pdf/1903.08127v1.pdf. [cited by applicant]
O'Toole, M., et al., “Confocal non-line-of-sight imaging based on the light-cone transform,” Nature, vol. 555, No. 7696, Mar. 2018, Macmillan Publishers Limited, pp. 338-341. [cited by applicant]
Pal et al., “Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom,” IEEE Transactions on Signal Processing, vol. 58, No. 8, Apr. 29, 2010, pp. 4167-4181. [cited by applicant]
Palacios, J., et al., “Communication-Driven Localization and Mapping for Millimeter Wave Networks,” IEEE Conference on Computer Communications (IEEE INFOCOM 2018), Apr. 2018, IEEE, pp. 2402-2410. [cited by applicant]
Palacios, J., et al., “JADE: Zero-Knowledge Device Localization and Environment Mapping for Millimeter Wave Systems,” IEEE INFOCOM 2017—IEEE Conference on Computer Communications, May 1-4, 2017, Atlanta, GA, IEEE, 9 pag… [cited by applicant]
Puglielli et al., “A Scalable Massive MIMO Array Architecture Based on Common Modules,” 2015 IEEE Intemational Conference on Communication Workshop (ICCW), Jun. 8-12, 2015, London, UK, pp. 1310-1315. [cited by applicant]
Rappaport et al., “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. 1, De… [cited by applicant]
Rappaport, T. S., et al., “Small-Scale, Local Area, and Transitional Millimeter Wave Propagation for 5G Communications,” IEEE Transactions on Antennas and Propagation, vol. 65, No. 12, Dec. 2017, IEEE, pp. 6474-6490. [cited by applicant]
Remcom, “Wireless InSite 3D Wireless Prediction Software,” May 29, 2020, <https://web.archive.org/web/20200529093755/http://www.remcom.com/wireless-insite-em-propagation-software/>. [cited by applicant]
Reitmayr, G., et al., “Simultaneous Localization and Mapping for Augmented Reality,” 2010 International Symposium on Ubiquitous Virtual Reality, Jul. 7-10, 2010, Gwangju, South Korea, IEEE, pp. 5-8. [cited by applicant]
Rusu et al., “Algorithms for the Construction of Incoherent Frames under Various Design Constraints,” Signal Processing, European Association for Signal Processing (EURASIP), vol. 152, Jun. 21, 2018, 2018, pp. 363-372. [cited by applicant]
Intemational Search Report and Written Opinion for International Patent Application No. PCT/US2021/036953, mailed Sep. 16, 2021, 19 pages. [cited by applicant]
Basar, E. et al., “Wireless Communications Through Reconfigurable Intelligent Surfaces,” IEEE Access, vol. 7, Aug. 13, 2019, IEEE, 21 pages. [cited by applicant]
Björnson, E. et al., “Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces,” IEEE Open Journal of the Communications Society, Sep. 2020, IEEE, 19 pages. [cited by applicant]
Björnson, E. et al., “Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?” IEEE Wireless Communications Letters, vol. 9, Issue 2, Feb. 2020, IEEE, 5 pages. [cited by applicant]
Larsson, E.G. et al., “Massive MIMO for Next Generation Wireless Systems,” IEEE Communications Magazine, vol. 52, No. 2, Feb. 2014, IEEE, pp. 186-195. [cited by applicant]
Lu, L. et al., “An Overview of Massive MIMO: Benefits and Challenges,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, Issue 5, Oct. 2014, IEEE, 18 pages. [cited by applicant]
Marzetta, T., “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Transactions on Wireless Communications, vol. 9, No. 11, Nov. 2010, IEEE, pp. 3590-3600. [cited by applicant]
Renzo, M.D. et al., “Reconfigurable Intelligent Surfaces vs. Relaying: Differences, Similarities, and Performance Comparison,” IEEE Open Journal of the Communications Society, vol. 1, Jun. 2020, IEEE, pp. 798-807. [cited by applicant]
Renzo, M.D. et al., “Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead,” IEEE Journal on Selected Areas in Communications, vol. 38, Issue 11, … [cited by applicant]
Roh, W. et al., “Millimeter-wave beamforming as an enabling technology for 5G cellular communications: ,” IEEE Communications Magazine, vol. 52, Issue 2, Feb. 2014, IEEE, pp. 106-113. [cited by applicant]
Taha, A. et al., “Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation,” IEEE 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAW… [cited by applicant]
Taha, A. et al., “Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Leaming,” arXiv:1904.10136v2 [cs.IT], Apr. 30, 2019, 33 pages. [cited by applicant]
Samimi et al., “Ultra-Wideband Statistical Channel Model for Non Line of Sight Millimeter-wave Urban Channels,” 2014 IEEE Global Communications Conference, Dec. 8-12, 2014, Austin, TX, pp. 3483-3489. [cited by applicant]
Sanguinetti et al., “Towards Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination,” arXiv eprint, arXiv:1904.03406v1 [eess.SP], Apr. 6, 2019, 25 pages, https://arxiv.org… [cited by applicant]
Setlur, P., et al., “Multipath Exploitation in Non-LOS Urban Synthetic Aperture Radar,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, No. 1, Feb. 2014, IEEE, pp. 137-152. [cited by applicant]
Shahmansoori, A., et al., “Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems,” IEEE Transactions on Wireless Communications, vol. 17, No. 3, Mar. 2018, IEEE, pp. 1822-1835. [cited by applicant]
Sheen, D., et al., “Three-Dimensional Millimeter-Wave Imaging for Concealed Weapon Detection,” IEEE Transactions on Microwave Theory and Techniques, vol. 49, No. 9, Sep. 2001, IEEE, pp. 1581-1592. [cited by applicant]
Sume, A., et al., “Radar Detection of Moving Targets Behind Comers,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, No. 6, Jun. 2011, IEEE, pp. 2259-2267. [cited by applicant]
Tan et al., “Direction of Arrival Estimation Using Co-Prime Arrays: A Super Resolution Viewpoint,” IEEE Transactions on Signal Proceedings, vol. 62, No. 21, Sep. 4, 2014, pp. 5565-5576. [cited by applicant]
Tan et al., “Enabling indoor mobile millimeter-wave networks based on smart reflect-arrays,” IEEE INFOCOM 2018—IEEE Conference on Computer Communications, Apr. 16-19, 2018, Honolulu, HI, 9 pages. [cited by applicant]
Thai, K., et al., “Around-the-Corner Radar: Detection and Localization of a Target in Non-Line of Sight,” 2017 IEEE Radar Conference (RadarConf), May 2017, IEEE, pp. 0842-0847. [cited by applicant]
Trichopoulos, G. C., et al., “A Broadband Focal Plane Array Camera for Real-time THz Imaging Applications,” IEEE Transactions on Antennas and Propagation, vol. 61, No. 4, Apr. 2013, IEEE, pp. 1733-1740. [cited by applicant]
Tropp, J., “Greed is Good: Algorithmic Results for Sparse Approximation,” IEEE Transactions on Information Theory, vol. 50, No. 10, Sep. 27, 2004, pp. 2231-2242. [cited by applicant]
Velten, A., et al., “Recovering three-dimensional shape around a comer using ultrafast time-of-flight imaging,” Nature communications, vol. 3, Mar. 2012, Macmillan Publishers Limited, 8 pages. [cited by applicant]
Walden, R. H., “Analog-to-Digital Converter Survey and Analysis,” IEEE Journal on Selected Areas in Communications, vol. 17, No. 4, Apr. 1999, pp. 539-550. [cited by applicant]
Wang et al., “Beam Codebook Based Beamforming Protocol for Multi-Gbps Millimeter-Wave WPAN Systems,” IEEE Journal on Selected Areas in Communications, vol. 27, No. 8, Sep. 29, 2009, pp. 1390-1399. [cited by applicant]
Wang, H., et al., “A Parametric Formulation of the UTD Diffraction Coefficient for Real-Time Propagation Prediction Modeling,” IEEE Antennas and Wireless Propagation Letters, vol. 4, Aug. 15, 2005, IEEE, pp. 253-257. [cited by applicant]
Wei, Z., et al., “DoA-LF: A Location Fingerprint Positioning Algorithm With Millimeter-Wave,” IEEE Access, vol. 5, Sep. 18, 2017, IEEE, pp. 22678-22688. [cited by applicant]
Witrisal, K. et al., “High-Accuracy Localization for Assisted Living: 5G systems will turn multipath channels from foe to friend,” IEEE Signal Processing Magazine, vol. 33, No. 2, Mar. 2016, IEEE, pp. 59-70. [cited by applicant]
Wu et al., “Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming,” IEEE Transactions on Wireless Communications, vol. 18, No. 11, Aug. 23, 2019, pp. 5394-5409. [cited by applicant]
Xia et al., “Achieving the Welch bound with difference sets,” IEEE Transactions on Information Theory, vol. 51, No. 5, Apr. 25, 2005, pp. 1900-1907. [cited by applicant]
Xu, F., et al., “Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging,” Optics Express, vol. 26, No. 8, Apr. 2018, Optical Society of America, pp. 9945-9962. [cited by applicant]
Zetik, R., et al., “Looking Behind a Corner Using Multipath-Exploiting UWB Radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, No. 3, Jul. 2015, IEEE, pp. 1916-1926. [cited by applicant]
Zhang et al., “A 4-bit Programmable Metamaterial Based on VO2Mediums,” 2018 IEEE/MTT-S International Microwave Symposium—IMS, Jun. 10-15, 2018, Philadephia, PA, pp. 984-986. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 16/684,746, mailed Apr. 28, 2020, 7 pages. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 16/684,746, mailed Aug. 5, 2020, 11 pages. [cited by applicant]
Alkhateeb et al., “Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, No. 5, Jul. 1, 2014, pp. 831-846. [cited by applicant]
Alkhateeb et al., “Deep Leaming Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems,” IEEE Access, vol. 6, Jun. 25, 2018, pp. 37328-37348. [cited by applicant]
Alkhateeb et al., “Machine Learning for Reliable Mmwave Systems: Blockage Prediction and Proactive Handoff,” 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Nov. 26-29, 2018, Anaheim, CA, p… [cited by applicant]
Alkhateeb et al., “MIMO Precoding and Combining Solutions for Millimeter-Wave Systems,” IEEE Communications Magazine, vol. 52, No. 12, Dec. 11, 2014, pp. 122-131. [cited by applicant]
Alkhateeb, A., “DeepMIMO: A Generic Deep Leaming Dataset for Millimeter Wave and Massive MIMO Applications,” Information Theory and Applications (ITA) Workshop, Feb. 18, 2019, San Diego, CA, 8 pages. [cited by applicant]
Basar, E., “Large Intelligent Surface-Based Index Modulation: A New Beyond MIMO Paradigm for 6G,” arXiv eprint, arXiv:1904.06704v1 [cs.IT], Apr. 14, 2019, 10 pages, https://arxiv.org/pdf/1904.06704v1.pdf. [cited by applicant]
Björnson et al., “Massive MIMO is a Reality—What is Next? Five Promising Research Directions for Antenna Arrays,” arXiv eprint, arXiv: 1902.07678v1 [eess.SP], Feb. 11, 2019, 19 pages, https://arxiv.org/pdf/1902.07678v1.… [cited by applicant]
Cai et al., “A Constrained 11 Minimization Approach to Sparse Precision Matrix Estimation,” Journal of American Statistical Association, vol. 106, No. 494, Theory and Methods, Jun. 2011, pp. 594-607. [cited by applicant]
Challa, S., et al., “Simultaneous Localization and Mapping in Wireless Sensor Networks,” 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Dec. 2005, IEEE, pp. 81-87. [cited by applicant]
Chi, T., et al., “A Packaged 90-to-300 GHz Transmitter and 115-to-325GHz Coherent Receiver in CMOS for Full-Band Continuous-Wave mm-Wave Hyperspectral Imaging,” 2017 IEEE International Solid-Slate Circuits Conference (I… [cited by applicant]
De Carvalho et al., “Non-Stationarities in Extra-Large Scale Massive MIMO,” arXiv eprint, arXiv:1903.03085v1 [cs. IT], Mar. 7, 2019, 7 pages, https://arxiv.org/abs/1903.03085v1. [cited by applicant]
Deng et al., “Deep Learning: Methods and Applications,” Foundations and Trends® in Signal Processing, vol. 7, No. 3-4, Jun. 30, 2014, pp. 197-387. [cited by applicant]
Doddalla, S. K., et al., “Non-Line of Sight Terahertz imaging from a Single Viewpoint,” 2018 IEEE/MTT-S Intemational Microwave Symposium-IMS, Jun. 2018, IEEE, pp. 1527-1529. [cited by applicant]
Dumont, T., et al., “Simultaneous localization and mapping in wireless sensor networks,” Signal Processing, vol. 101, Aug. 2014, Elsevier B.V., pp. 192-203, [Online] Available: http://www.sciencedirect.com/science/artic… [cited by applicant]