Spatial Modulation for Multi-Antenna Communication

08/12/2017
Auteurs : Marco di Renzo
OAI : oai:www.see.asso.fr:1165:20947
DOI : You do not have permission to access embedded form.

Résumé

Spatial Modulation for Multi-Antenna Communication

Média

Voir la vidéo

Métriques

6
0
3.18 Mo
 application/pdf
bitcache://573389d9021ed0f337fefaa4df3ebb92cb74c61f

Licence

Creative Commons Aucune (Tous droits réservés)
<resource  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
                xmlns="http://datacite.org/schema/kernel-4"
                xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
        <identifier identifierType="DOI">10.23723/1165/20947</identifier><creators><creator><creatorName>Marco di Renzo</creatorName></creator></creators><titles>
            <title>Spatial Modulation for Multi-Antenna Communication</title></titles>
        <publisher>SEE</publisher>
        <publicationYear>2017</publicationYear>
        <resourceType resourceTypeGeneral="Text">Text</resourceType><dates>
	    <date dateType="Created">Fri 8 Dec 2017</date>
	    <date dateType="Updated">Sat 9 Dec 2017</date>
            <date dateType="Submitted">Sat 17 Feb 2018</date>
	</dates>
        <alternateIdentifiers>
	    <alternateIdentifier alternateIdentifierType="bitstream">573389d9021ed0f337fefaa4df3ebb92cb74c61f</alternateIdentifier>
	</alternateIdentifiers>
        <formats>
	    <format>application/pdf</format>
	</formats>
	<version>35427</version>
        <descriptions>
            <description descriptionType="Abstract"></description>
        </descriptions>
    </resource>
.

1 Société de l’électricité, de l’électronique et des technologies de l’information et de la communication PRIX BRILLOUIN-GLAVIEUX 2017 Marco Di Renzo Paris-Saclay University Laboratory of Signals and Systems (L2S) – UMR8506 CNRS – CentraleSupelec – University Paris-Sud Paris, France marco.direnzo@l2s.centralesupelec.fr GRANDS PRIX SEE 2017 Espace Hamelin, Paris, France December 4, 2017 About Me 2  Current Position  Chargé de Recherche CNRS de classe normale (CRCN CNRS)  Education  April 2003: Laurea (5 years), University of L’Aquila (Italy)  January 2007: Ph.D. (3 years), University of L’Aquila (Italy)  October 2013: H.D.R., Université Paris-Sud (France)  Employment  December 2004-present: Co-founder, CSO, WEST s.r.l. (Italy)  Fall 2006: Research Scholar, Virginia Tech (USA)  2007: Postdoctoral Fellow, University of L’Aquila (Italy)  2008: Permanent Research Scientist, CTTC (Spain)  2009: EPSRC Research Fellow, University of Edinburgh (UK)  2010-2017: Chargé de Recherche (CR2, CR1) CNRS (France) Research Interests 3  Expertise: Network Communication Theory & Wireless Commun.  Mathematical modeling, performance evaluation & optimization of large-scale communication systems and networks  Research topics  Master & Ph.D. (2002-2006): Ultra Wide Band, Impulse Radio  Italy & Spain (2007, 2008): Relaying, Cooperative Commun.  UK (2009 - present): Spatial Modulation  France (2010 - present): Cooperative Communications, Network Coding, Spatial Modulation, Stochastic Geometry  From “Links” to “Networks” RX TX Channel noise-limited interference-limited 4 Spatial Modulation for Multi-Antenna Communication 5 MIMO – Better Rate and Performance…  Array gain (beamforming), spatial division multiple access  Spatial multiplexing: Rate = min(Nt, Nr)log2(1+SNR)  Reliability: BEP ~ SNR-(NtNr) 6 … But, How About Complexity, Cost, Consumption? A. Mohammadi and F. M. Ghannouchi, “Single RF Front-End MIMO Transceivers”, IEEE Commun. Mag., Vol. 49, No. 12, pp. 104-109, Dec. 2011. Conventional MIMO 7 Single-RF MIMO – The Paradigm Shift A. Mohammadi and F. M. Ghannouchi, “Single RF Front-End MIMO Transceivers”, IEEE Commun. Mag., Vol. 49, No. 12, pp. 104-109, Dec. 2011. Conventional MIMO Single-RF MIMO 8 Why? Internet of Things (IoT): New Optimization Space Energy consumption Spectral Efficiency (bits/s/Hz) uplink min consumption for 1 RF Mobile device with N RFs, NxN MIMO Sensor with 1 RF, SISO/SIMO N times less consumption twice more rate 1 stream limitation 2 streams limitation Mobile device with N RFs, Nx2 MIMO minimum consumption for N RFs SM-MIMO 1RF Spatial Modulation: Can we Increase the Data Rate of IoT Devices Without Increasing their Power Consumption, Complexity, and Cost ? / cost 9 Spatial Modulation for Multi-Antenna Communication S2 S1 Vertical Bell Laboratories Layered Space-Time S1 S2 S2 S1 Orthogonal Space-Time-Block Coding S1 S2 -S2* S1* S2 S1 Spatial Modulation S2 = 0/1 0 1 S1 Spatial Multiplexing Transmit Diversity Spatial Modulation 10 Spatial Modulation – How It Works? M. Di Renzo, H. Haas, and P. M. Grant, “Spatial Modulation for Multiple-Antenna Wireless Systems - A Survey”, IEEE Communications Magazine, Vol. 49, No. 12, pp. 182-191, December 2011. (00)00 (00)01 (00)10 (00)11 (11)00 (11)01 (11)10 (11)11 00 (Tx0) 01 (Tx1) 10 (Tx2) 11 (Tx3) Signal Constellation for Tx0 Signal Constellation for Tx3 Spatial Constellation Re Im Im Im Re Re Signal Constellation for Tx1 11 Spatial Modulation – How It Works? M. Di Renzo, H. Haas, and P. M. Grant, “Spatial Modulation for Multiple-Antenna Wireless Systems - A Survey”, IEEE Communications Magazine, Vol. 49, No. 12, pp. 182-191, December 2011. (00)00 (00)01 (00)10 (00)11 (11)00 (11)01 (11)10 (11)11 00 (Tx0) 01 (Tx1) 10 (Tx2) 11 (Tx3) Signal Constellation for Tx0 Signal Constellation for Tx3 Spatial Constellation Re Im Im Im Re Re Signal Constellation for Tx1 … 1110 0001 … 12 Spatial Modulation – How It Works? M. Di Renzo, H. Haas, and P. M. Grant, “Spatial Modulation for Multiple-Antenna Wireless Systems - A Survey”, IEEE Communications Magazine, Vol. 49, No. 12, pp. 182-191, December 2011. (00)00 (00)01 (00)10 (00)11 (11)00 (11)01 (11)10 (11)11 00 (Tx0) 01 (Tx1) 10 (Tx2) 11 (Tx3) Signal Constellation for Tx0 Signal Constellation for Tx3 Spatial Constellation Re Im Im Im Re Re Signal Constellation for Tx1 … 1110 0001 … 13 Spatial Modulation – How It Works? M. Di Renzo, H. Haas, and P. M. Grant, “Spatial Modulation for Multiple-Antenna Wireless Systems - A Survey”, IEEE Communications Magazine, Vol. 49, No. 12, pp. 182-191, December 2011. (00)00 (00)01 (00)10 (00)11 (11)00 (11)01 (11)10 (11)11 00 (Tx0) 01 (Tx1) 10 (Tx2) 11 (Tx3) Signal Constellation for Tx0 Signal Constellation for Tx3 Spatial Constellation Re Im Im Im Re Re Signal Constellation for Tx1 … 1110 0001 … SM-MIMO: (Some) Results and Their Impact 14  First mathematical methodology for analysis & optimization  2015 IEEE Jack Neubauer Memorial Award  First experimental validation through measurements  2013 IEEE VTC-Fall Best Paper Award  First testbed implementation & test  2013 IEEE Trans. Veh. Technol.  First design & measurements at mmWave  2016 IEEE TWC & COMML  2017 IEEE TCOM Edinburgh Bristol Brno (Czech Republic) 15 Spatial Modulation – A New Air Interface for the IoT Industrial ANR Project (Orange Labs leads, Jan. 2016) 16 The SM Challenge: Make it Simple, Cheap, Compact… S2 S1 Vertical Bell Laboratories Layered Space-Time S1 S2 S2 S1 Orthogonal Space-Time-Block Coding S1 S2 -S2* S1* S2 S1 Spatial Modulation S2 = 0/1 0 1 S1 Spatial Multiplexing Transmit Diversity Spatial Modulation RectAnt-SM: SM Based on Reconfigurable Antennas 17 Reconfigurable Antenna M. Di Renzo, H. Haas, A. Ghrayeb, S. Sugiura, and L. Hanzo, “Spatial Modulation for Generalized MIMO: Challenges, Opportunities and Implementation”, Proc. of the IEEE, vol. 102, no. 1, pp. 56-103, Jan. 2014. 4-State Mono-Port Reconfigurable Antenna 18 Proof-of-Concept @ IEEE ICC 2017, Paris, France 19 https://www.youtube.com/watch?v=L7xAeU2jh5s 20 Stochastic Geometry For Communication Networks Stochastic Geometry for Communication Networks 21  What’s that?  Stochastic geometry is the field of mathematical research that provides us with suitable mathematical models and appropriate statistical methods to model, study, and optimize spatial patterns/networks  Why?  Conventional approaches are not accurate for modeling actual network deployments, are not mathematically tractable, and are not scalable for dense network deployments         0 0 2 1 1 cov 0 MGF exp P PDF agg I r r T P P T d                          SG for CellularNets: (Some) Results and Their Impact 22  Tractable mathematical framework (rate over fading channels)  2013 Network of Excellence (NEWCOM#) Best Paper Award  Tractable mathematical framework (relay-aided cellular nets)  2014 IEEE CAMAD Best Paper Award  Tractable mathematical framework (interference-aware uplink cellnets)  2015 IEEE ComManTel Best Paper Award  Tractable mathematical framework (energy harvesting at mmWave)  2017 IEEE SigTelCom Best Paper Award  First experimental validation through empirical data (base station deployments with buildings – OFCOM & OS, UK)  2015 ACM MSWiM (micro-wave cellular nets)  2015 IEEE M&N (mmWave cellular nets)  2015 Royal Academy Eng. Distinguished Visiting Fellowship, UK SG for CellularNets: Data-Driven Experimental Validation 23  Methodology:  Actual base station locations from OFCOM (UK)  Actual building footprints from ORDNANCE SURVEY (UK)  Channel model added on top (LOS/NLOS links) OFCOM: http://stakeholders.ofcom.org.uk/ sitefinder/sitefinder-dataset/ OS: https://www.ordnancesurvey.co.uk/ opendatadownload/products.html Understanding of Network Densification (in 2011) 24 o Standard model (NLOS): r-αn - 2011 towards interference-limited J. G. Andrews, F. Baccelli, and R. K. Ganti, “A Tractable Approach to Coverage and Rate in Cellular Networks”, IEEE Trans. Commun., vol. 59, no. 11, pp. 3122–3134, Nov. 2011.     cov cov 2 1 1 P P , 1, 2 ,1 2 , T F T         Understanding of Network Densification (in 2015) 25 M. Di Renzo, W. Lu, and P. Guan, “The Intensity Matching Approach: A Tractable Stochastic Geometry Approximation to System-Level Analysis of Cellular Networks”, IEEE Trans. Wireless Commun., Sep. 2016. * With blockages (LOS+NLOS) - 2015 o Standard model (NLOS): r-αn - 2011 towards interference-limited     cov cov 2 1 1 P P , 1, 2 ,1 2 , T F T         26 SG for mmWave: Data-Driven Experimental Validation The channel model matters (based on NYU-WIRELESS measurements) - Path-loss model - Shadowing model - LOS/NLOS/outage link state - Directional beamforming with errors - Multi-tier with various cell associations T. S. Rappaport et al. “Millimeter wave channel modeling and cellular capacity evaluation”, IEEE JSAC, June 2014. M. Di Renzo, “Stochastic Geometry Modeling and Analysis of Multi-Tier Millimeter Wave Cellular Networks”, IEEE Trans. Wireless Commun., vol. 14, no. 9, pp. 5038-5057, Sep. 2015. Coverage Probability of Best Cell Association Criterion 27 Deep Understanding of mmWave Cellular  100% of outage for distances longer than 200 meters ()  0% of outage for distances shorter than 50 meters ()  > 80% of LOS commun. for distances shorter than 50 meters ()  mmWave cellular networks are noise-limited () DENSIFICATION IS THE KEY average cell radius of the order of 50 – 200 meters 28 Does mmWave Outperform µWave Cellular ? Coverage Probability: mmWave vs. μWave Cellular Rc = 50m Rc = 100m Rc = 150m Rc = 200m 29 Latest Works: SG for Energy Efficiency Optimization * A single user is selected & owns the resources o All users are selected & share the resources x x 30 Latest Works: Modeling Realistic Cellular Networks ◊ Empirical data (locations of base stations) * Proposed mathematical abstraction model o Conventional abstraction model (Poisson) 31 PostMicroWaveNets: Communication Beyond RF PostMicroWaveNets: New Ways to Communicate 32 From RF Waves … 33 … to Chemical Molecules 34 Molecular Communication Networks - Applications 35 MCNs: It is NOT a Science Fiction Movie… 36 HW Testbed @ York University Understanding Molecular Interference Networks 37 … a mathematical theory to establish the conditions when a molecular communication link can coexist with a biochemical system … Molecular Comm. Nets: « Le Vivant Comme Modèle… » 38 Andrea Goldsmith, Stephen Harris Professor, School of Engineering & Neurosciences Institute, Stanford University (Nov. 2016) - https://youtu.be/zUiB1iKxH_g Société de l’électricité, de l’électronique et des technologies de l’information et de la communication PRIX BRILLOUIN-GLAVIEUX 2017 Marco Di Renzo “For outstanding results in developing several mathematical abstractions (for mobile network modeling), innovating ideas, as well as demonstrating their usefulness in future wireless communications systems ”