Marco Di Renzo - Program - Eusipco2016€¦ · RF Energy Harvesting, Wireless Power Transfer,...

1

Energy-Neutral System-Level Analysis and Optimization of 5G Wireless Networks

(energy harvesting and wireless power transfer)

Marco Di Renzo

Paris-Saclay University Laboratory of Signals and Systems (L2S) – UMR8506

CNRS – CentraleSupelec – University Paris-SudParis, France

[email protected]

IEEE European Signal Processing Conference2016 IEEE EUSIPCO – Budapest, Hungary, Aug. 29 – Sep. 2, 2016

H2020-MCSA

Energy Neutrality – Part II

2

5G-PPP – 5G Network Vision

35G-PPP 5G Vision Document, “The next-generation of communication networks and services”, March2015. Available: http://5g-ppp.eu/wp-content/uploads/2015/02/5G-Vision-Brochure-v1.pdf.

5G-PPP – 5G New Service Capabilities


5G-PPP in a nuthsell: To conduct research and innovation work that will form the basis of the 5G

infrastructure for the Future Internet for a wide range of applications

5G is a key enabler for the IoT, providing a platform to connect a massive numberof sensors, devices, actuators with stringent energy and transmission constraints

5G will be designed to be a sustainable and scalable technology

5G will bring drastic energy efficiency improvement and harvest energy fromeverywhere, solar, thermal, vibration and electromagnetic (RF) sources

Energy-Neutral Cellular Base Stations …

5

… and Beyond Cellular …

6S. Bi, C. K. Ho, and R. Zhang, “Wireless powered communication: Opportunities and challenges”, IEEECommun. Mag., vol. 53, no. 4. pp. 117–125, Apr. 2015.

The 5G (Cellular) Network of the Future

7

Buzzword 1: Densification

1. Access Points (Network Topology, HetNets)

2. Radiating Elements (Large-Scale/Massive MIMO)

Buzzword 2: Spectral vs. Energy Efficiency Trade-Off

1. Shorter Transmission Distance (Relaying, Femto, D2D)

2. Total Power Dissipation (Single-RF MIMO, Antenna Muting)

3. RF Energy Harvesting, Wireless Power Transfer, Full-Duplex

Buzzword 3: Spectrum Scarcity

1. Cognitive Radio and Opportunistic Communications

2. mmWave Cellular Communications

Buzzword 4: Software-Defined, Centrally-Controlled, Shared, Virtualized

1. SDN, NFV, Network Resource Virtualization (NRV)

Wireless Power Transfer & Energy Harvesting… Potential / Futuristic Scenarios based on Renewable Energy …

Y. Mao, Y. Luo, J. Zhang, and K. B. Letaief, "Energy Harvesting Small Cell Networks: Feasibility,Deployment, and Operation", IEEE Commun. Mag., June 2015.

Wireless Power Transfer & Energy Harvesting… Potential / Futuristic Scenarios based on RF Power Transfer …

A. Ghazanfari, H. Tabassum, and E. Hossain, "Ambient RF Energy Harvesting in Ultra-Dense Small CellNetworks: Performance and Trade-offs", IEEE Wireless Commun. Mag., Apr. 2015.

10

Wireless Power Transfer – RF Energy HarvestingSWIPT: Simultaneous Wireless Information and Power Transfer

Information + EnergyReceiver

Why Now? Is it Feasible?


Why Now? Is it Feasible?

12Elia, Power generation, available online at http://www.elia.be/en/grid-data/power-generation.

SOLAR and WIND can provide the necessary electric power to Small Cells 100 W electric power can be generated by a 121 cm x 53.6 cm solar panel under

sunlight radiation or by a rotor with a 1 m diameter under an 8 m/s wind speed

They nicely complement each other over a short and a long term horizons

Solar Powered BSs Exist…

13V. Chamola and B. Sikdar, "Solar Powered Cellular Base Stations: Current Scenario, Issues and ProposedSolutions", IEEE Commun. Mag., May 2016.

Very Recent Developments: NB-LTE for IoT

14

Very Recent Developments: NB-LTE for IoT

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WHAT’S THAT? NB-LTE is one of the proposed “clean slate” options for adapting LTE

technology to make it suitable for low cost, low power, wide area networksfor IoT applications

NB-LTE is one of the 4G LTE variants being looked at for IoT. Standardsbody 3GPP is studying no less than four possible ways to adapt 4G LTE tomake it suitable for low power wide area IoT networks

WHY THAT? NB-LTE is well-suited for the IoT market segment “because of its low

implementation cost, ease of use and power efficiency”

Cellular networks already cover 90 percent of the world’s population so itmakes sense to leverage this global footprint to support and drive IoTadoption through the standardization of NB-LTE

Very Recent Developments: Freevolt Technology

16

WHAT’S THAT? A patented technology developed by an international team from Drayson

Technologies and Imperial College London

Drayson Technologies claims to be the first to market this technology, whichwas recently commercially licensed (PA Consulting Group was granted it)

HOW IT WORKS? Freevolt harvests indoor & outdoor ambient RF waves across multiple bands

and uses the energy to power low energy electronic devices

Freevolt is able to pick up unused electro-magnetic energy from sources suchas mobile cellular networks and Wi-Fi without the need for charging by cableor a dedicated transmitter

Freevolt can harvest this energy without interrupting the data signal. The typeof devices it is able to power will depend the device’s energy budget, formfactor and the amount of available ambient radio energy

The range of possible applications is endless, but IoT sensors, beacons andwearables, such as fitness bands, clothing and medical garments, are obviousmarkets. The company has developed a commercially available personal airpollution sensor called CleanSpace Tag

Very Recent Developments: Freevolt Technology

17

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SWIPT – System-Level Modeling and Optimization

M. Di Renzo and W. Lu, “System-Level Analysis of Cellular Networks with Simultaneous Wireless Informationand Power Transfer: Stochastic Geometry Modeling”, IEEE Trans. Vehicular Technol., IEEE Early Access.

Joint Statistical Characterization of Harvested Energy and Achievable Rate in the Presence of Other-Cell Interference

Major Difference: Information (rate) and harvesting (energy) requirements need to be jointly satisfiedSetup: LOS/NLOS, beamforming, etc.

Harvesting: Interference is GOOD Information: Interference is BAD

SWIPT – System-Level Modeling

19

Directional beamforming

Accurate channel modeling: LOS/NLOS links

Cell association criterion: smallest path-loss

maxBS BS

BS minBS BS

if 2

if 2

Gg

G

D

SWIPT – System-Level Modeling

20

Stochastic geometry is used for system-level analysis

Experimental validation with actual BSs and building deployments

Modeling (PPP)

Validation (OFCOM + OS in London, UK)

SWIPT – PPP + LOS/NLOS, etc…

21

… not an easy mathematical problem …

SWIPT – PPP + LOS/NLOS, etc…

22

… not an easy mathematical problem …

23

System-Level Modeling of Cellular Networks – IndustryThe NTT DOCOMO 5G Real-Time Simulator

DOCOMO 5G White Paper, “5G Radio Access: Requirements, Concept and Technologies”, July 2014.

24

Life of a 3GPP Simulation Expert (according to Samsung)

Charlie Zhang, Simons Conference on Networks and Stochastic Geometry, October 2015, Austin, USA.

Modeling Cellular Networks – In Academia

25

Conventional approaches to the analysis and design of cellularnetworks (abstraction models) are:

The Wyner model

The single-cell interfering model or dominant interferers model

The regular hexagonal or square grid modelD. H. Ring and W. R. Young, “The hexagonal cells concept”, Bell Labs TechnicalJournal, Dec. 1947. http://www.privateline.com/archive/Ringcellreport1947.pdf.

Modeling Cellular Networks – In Academia

26

Conventional approaches to the analysis and design of cellularnetworks (abstraction models) are:

The Wyner model

The single-cell interfering model or dominant interferers model

The regular hexagonal or square grid modelD. H. Ring and W. R. Young, “The hexagonal cells concept”, Bell Labs TechnicalJournal, Dec. 1947. http://www.privateline.com/archive/Ringcellreport1947.pdf.

Realityvs.

AbstractionModeling

The Conventional Grid-Based Approach

27

Probe mobile terminal

Macro base station


28


Macro base station

w 211 1

0 01, B log 1 SINR ,i ir rrC r


29


Macro base station

w 222 2



30


Macro base station

w 233 3



31

0 0 0

0

,1

w 21

1E , ,

1 B log 1 SINR ,

i

Nn

i irn

n

nN

i

nr

n

rr r r

r

C C

r

CN

N


32

Simple enough… So, where is the issue?

0 0 0

0

,1

w 21

1E , ,

1 B log 1 SINR ,

i

Nn

i irn

n

nN

i

nr

n

rr r r

r

C C

r

CN

N


33

Simple enough… So, where is the issue?

The answer: …this spatial expectation

cannot be computed mathematically…

0 0 0

0

,1

w 21

1E , ,

1 B log 1 SINR ,

i

Nn

i irn

n

nN

i

nr

n

rr r r

r

C C

r

CN

N

The Conventional Grid-Based Approach: (Some) Issues Advantages:

Dozens of system parameters can be modeled and tuned in suchsimulations, and the results have been sufficiently accurate as to enablethe evaluation of new proposed techniques and guide field deployments

Limitations: Actual coverage regions deviate from a regular grid Mathematical modeling and optimization are not possible. Any elegant

and insightful Shannon formulas for cellular networks? The abstraction model is not scalable for application to ultra-dense

HetNets (different densities, transmit powers, access technologies, etc…)

34

Let’s Change the Abstraction Model, Then…

35

Regulardeployment

Let’s Change the Abstraction Model, Then…

36

Regulardeployment

Randomdeployment

(PPP)

Stochastic Geometry Based Abstraction Model

37

A RANDOM SPATIAL MODEL for Heterogeneous CellularNetworks (HetNets): K-tier network with BS locations modeled as independent marked

Poisson Point Processes (PPPs)

The PPP model is surprisingly good for 1-tier as well (macro BSs):lower/upper bound to reality and trends still hold

The PPP model makes even more sense for HetNets due to lessregular BSs placements for lower tiers (femto, etc.)

Stochastic Geometry emerges as a powerful tool for theanalysis, design and optimization

of ultra-dense HetNets

An Emerging (Tractable) Approach

Beyond the PPP: Possible, but Math is More Complicated

38

Y. J. Chun, M. O. Hasna, A. Ghrayeb, and M. Di Renzo, “On modeling heterogeneous wireless networksusing non-Poisson point processes”, IEEE Commun. Mag., submitted. [Online]. Available:http://arxiv.org/pdf/1506.06296.pdf.

Matern Hard-Core PPTake a homogeneous PPP and remove any pairs of points that are closer to each other

than a predefined minimum distance R

PPP-based Abstraction

39

How It Works (Downlink – 1-tier)


PPP-distributed macro base station


40





41





42




Intended link

w 211 1



43




Intended link

w 222 2



44




Intended link

w 233 3



45

0 0 0

0

,1

w 21

1E , ,

1 B log 1 SINR ,

i

Nn

i irn

n

nN

i

nr

n

rr r r

r

C C

r

CN

N


46

Are you kidding me? ... What makes it different?

0 0 0

0

,1

w 21

1E , ,

1 B log 1 SINR ,

i

Nn

i irn

n

nN

i

nr

n

rr r r

r

C C

r

CN

N


47

Are you kidding me? ... What makes it different?

The answer: …this spatial expectation

can be computed mathematically…

0 0 0

0

,1

w 21

1E , ,

1 B log 1 SINR ,

i

Nn

i irn

n

nN

i

nr

n

rr r r

r

C C

r

CN

N

… On Abstraction Modeling …

48

George Edward Pelham Box (18 October 1919 – 28 March 2013)

StatisticianFellow of the Royal Society (UK)

Director of the Statistical Research Group (Princeton University)

Emeritus Professor(University of Wisconsin-Madison)

“…all models are wrong, but some are useful…”

Is This Abstraction Model Accurate?

49OFCOM: http://stakeholders.ofcom.org.uk/sitefinder/sitefinder-dataset/ORDNANCE SURVEY: https://www.ordnancesurvey.co.uk/opendatadownload/products.html

Methodology:



Methodology: Actual base station locations from OFCOM (UK)

OFCOM:London“London

Bridge area”




Actual building footprints from ORDNANCE SURVEY (UK)

ORDNANCESURVEY:London“London

Bridge area”




Actual building footprints from ORDNANCE SURVEY (UK)

Channel model added on top (1-state and 2-state with LOS/NLOS)

Mobile terminal

Base station (outdoor)

Base station (rooftop)NLOS

LOS

NLOS

2-state: the location of MTs and BSsand the location/shape of buildingsdetermine LOS/NLOS conditions

1-state: all links are either in LOS orNLOS regardless of the topology

The London Case Study (1/7)

53

O2 + Vodafone O2 Vodafone

Number of BSs 319 183 136

Number of rooftop BSs 95 62 33

Number of outdoor BSs 224 121 103

Average cell radius (m) 63.1771 83.4122 96.7577


54


55


56

PPP Accuracy: 1-State Channel Model

O2+VODAFONE O2 VODAFONE

OFCOM: Actual base station locations, (actual building footprints), actual channels

PPP: Random base station locations, (actual building footprints), actual channels


57

PPP Accuracy: 2-State Channel Model

O2+VODAFONE

O2 VODAFONE


58

1-State vs. 2-State Channel Models: Only LOSWorse coverage, as interference is enhanced

Only NLOS In-between, as interference is reduced but probe link gets worse

LOS and NLOSMore realistic: we can model it with stochastic geometry


59

Omni-Directional vs. 3GPP Radiation Patterns

Why Is This Modeling Approach So Accurate?

60

O2 + Vodafone O2 Vodafone

Number of BSs 319 183 136

Number of rooftop BSs 95 62 33

Number of outdoor BSs 224 121 103

Average cell radius (m) 63.1771 83.4122 96.7577

Intrigued Enough?

61

W. Lu and M. Di Renzo, “Stochastic Geometry Modeling of Cellular Networks: Analysis, Simulation andExperimental Validation”, ACM Int. Conf. Modeling, Analysis and Simulation of Wireless and MobileSystems, Nov. 2015. [Online]. Available: http://arxiv.org/pdf/1506.03857.pdf.

… Further Information and Case Studies …

How It Works: The Magic of Stochastic Geometry (1/5)

62

… understanding the basic math …

0rir0BS

covP Pr SINR T

2

20

SINR o o

agg

P h rI r

0

20

\agg i i

i BSI r P h r

2

cov 20

P Pr ...o o

agg

P h rT

I r

is a PPP

iBS


63


0 0

0 0

2

cov 20

2 2 10

2 10,

2 1 1

P Pr

Pr

E exp

E exp MGF

agg

agg

o o

agg

o agg o

agg oI r r

r o oI r

P h rT

I r

h I r P Tr

I r P Tr

P Tr P Tr

2 expoh

MGF

EX

sXX

s

e


64


0 0

00

2 1 1cov

2 1 1

0

P E exp MGF

exp MGF PDF

agg

agg

r o oI r

rI r

T P r P Tr

T P P T d


65


0 0

00

2 1 1cov

2 1 1

0

P E exp MGF

exp MGF PDF

agg

agg

r o oI r

rI r

T P r P Tr

T P P T d

Trivial so far… where is the magic?


66


Trivial so far… where is the magic?Stochastic Geometry provides us with themathematical tools for computing, in closed-form,the MGF and the PDF of the equation above

0 0

00

2 1 1cov

2 1 1

0

P E exp MGF

e Mxp GF PDF

agg

agg

r o oI r

rI r

T P r P Tr

T P P T d


67


0

20

\agg i i

i BSI r P h r

The aggregate other-cell interferenceconstitues a Marked PPP, where themarks are the channel power gains

0

2PDF 2 expr The PDF of the closest-distancefollows from the null probability ofspatial PPPs

0

MGF ...aggI r s

The MGF of the aggregate other-cell interference follows from theProbability Generating Functional(PGFL) of Marked PPPs


68


200

2

0

2

0

2

,\

2

\

2

MGF E exp

E E exp

exp 2 1 E exp

agg i

i

i

i iI r hi BS

i ihi BS

i i i ihr

s s P h r

sP h r

sP h d

PGFL

available in closed-form in papers

So Powerful and Just Two Lemmas Need to be Used…

69

Stochastic Geometry: Advantages and Limitations

70

Advantages: “What the Lovers Say” Elegant mathematical formulation for network-wide performance metrics

Often closed-form and insightful

Provides utility functions for system design and optimization

…

Limitations: “What the Others Say” – MISCONCEPTION The PPP assumption may not be realistic for some tiers of BSs

Practical transmission technologies are more complicated than SISO

Practical path-loss models are bounded and different for LOS/NLOS

Practical channel models are more complicated than Rayleigh fading

Closed-form formulation only for specific parameters

In general, one or two integrals need to be accepted …

71

Three New and General Mathematical Tools

1. Average Rate: The MGF-Based Approach M. Di Renzo, A. Guidotti, and G. E. Corazza, “Average Rate of Downlink Heterogeneous

Cellular Networks over Generalized Fading Channels – A Stochastic Geometry Approach”,IEEE Trans. Commun., vol. 61, no. 7, pp. 3050–3071, July 2013.

2. Average Error Probability: The EiD-Based Approach M. Di Renzo and W. Lu, “The Equivalent–in–Distribution (EiD)–based Approach: On

the Analysis of Cellular Networks Using Stochastic Geometry”, IEEE Commun. Lett.,vol. 18, no. 5, pp. 761-764, May 2014.

M. Di Renzo and W. Lu, “Stochastic Geometry Modeling and Performance Evaluation ofMIMO Cellular Networks by Using the Equivalent-in-Distribution (EiD)-BasedApproach”, IEEE Trans. Commun., vol. 63, no. 3, pp. 977-996, March 2015.

3. Coverage Probability: The Gil-Pelaez-Based Approach M. Di Renzo and P. Guan, “Stochastic Geometry Modeling of Coverage and Rate of

Cellular Networks Using the Gil-Pelaez Inversion Theorem”, IEEE Commun. Lett., vol.18, no. 9, pp. 1575–1578, September 2014.

72

Many Tools/Results are Now Available… M. Di Renzo, C. Merola, A. Guidotti, F. Santucci, and G. E. Corazza, “Error Performance of

Multi–Antenna Receivers in a Poisson Field of Interferers – A Stochastic Geometry Approach”,IEEE Trans. Commun., vol. 61, no. 5, pp. 2025–2047, May 2013.

M. Di Renzo, A. Guidotti, and G. E. Corazza, “Average Rate of Downlink Heterogeneous CellularNetworks over Generalized Fading Channels – A Stochastic Geometry Approach”, IEEE Trans.Commun., vol. 61, no. 7, pp. 3050–3071, July 2013.

M. Di Renzo and W. Lu, “The Equivalent–in–Distribution (EiD)–based Approach: On theAnalysis of Cellular Networks Using Stochastic Geometry”, IEEE Commun. Lett., vol. 18, no. 5,pp. 761-764, May 2014.

M. Di Renzo and P. Guan, “A Mathematical Framework to the Computation of the ErrorProbability of Downlink MIMO Cellular Networks by Using Stochastic Geometry”, IEEE Trans.Commun., vol. 62, no. 8, pp. 2860–2879, July 2014.

M. Di Renzo and P. Guan, “Stochastic Geometry Modeling of Coverage and Rate of CellularNetworks Using the Gil-Pelaez Inversion Theorem”, IEEE Commun. Lett., vol. 18, no. 9, pp.1575–1578, September 2014.

M. Di Renzo and W. Lu, “End-to-End Error Probability and Diversity Analysis of AF-Based Dual-Hop Cooperative Relaying in a Poisson Field of Interferers at the Destination”, IEEE Trans.Wireless Commun., vol. 14, no. 1, pp. 15–32, January 2015.

M. Di Renzo and W. Lu, “Stochastic Geometry Modeling and Performance Evaluation of MIMOCellular Networks by Using the Equivalent-in-Distribution (EiD)-Based Approach”, IEEE Trans.Commun., vol. 63, no. 3, pp. 977-996, March 2015.

M. Di Renzo and W. Lu, “On the Diversity Order of Selection Combining Dual-Branch Dual-HopAF Relaying in a Poisson Field of Interferers at the Destination”, IEEE Trans. Veh. Technol., vol.64, no. 4, pp. 1620-1628, June 2015.

… and, Recently, Have Been Proposed 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.

W. Lu and M. Di Renzo, “Stochastic Geometry Modeling and System-LevelAnalysis/Optimization of Relay-Aided Downlink Cellular Networks”, IEEE Trans. Commun., vol63, no. 11, pp. 4063-4085, Nov. 2015.

M. Di Renzo and P. Guan, “Stochastic Geometry Modeling, System-Level Analysis andOptimization of Uplink Heterogeneous Cellular Networks with Multi-Antenna Base Stations”,IEEE Trans. Commun., IEEE Early Access.

M. Di Renzo and W. Lu, “System-Level Analysis/Optimization of Cellular Networks withSimultaneous Wireless Information and Power Transfer: Stochastic Geometry Modeling”, IEEETrans. Vehicular Technol., IEEE Early Access.

F. J. Martin-Vega, G. Gomez, M. C. Aguayo Torres, and M. Di Renzo, “Analytical Modeling ofInterference Aware Power Control for the Uplink of Heterogeneous Cellular Networks”, IEEETrans. Wireless Commun., IEEE Early Access.

Y. Deng, L. Wang, M. Elkashlan, M. Di Renzo, and J. Yuan, “Modeling and Analysis of WirelessPower Transfer in Heterogeneous Cellular Networks”, IEEE Trans. Commun., IEEE EarlyAccess.

73

… and, Recently, Have Been Proposed

A Complete Mathematical Framework for System-Level Analysis

M. Di Renzo, W. Lu, and P. Guan, “The Intensity MatchingApproach: A Tractable Stochastic Geometry Approximation toSystem-Level Analysis of Cellular Networks”, IEEE Trans.Wireless Commun., IEEE Early Access.

Realistic path-loss model with LOS/NLOS conditions

Arbitrary shadowing and fading

General antenna-array radiation pattern

Multi-tier topology with practical cell association

Realistic traffic load models as a function of the densities ofBSs and MTs

…

74

IM Approach: Why So Many Details are Needed?

75

... Impact of LOS/NLOS …

Mobile terminal

Base station

NLOS

LOS

3GPP


76

... Impact of LOS/NLOS …


77

... Impact of LOS/NLOS (fully-loaded) …

0 100 200 300 400 500 600 700 800 900 10000.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Rcell [m]

Rat

e [b

ps/H

z]

3GPP link stateonly LOSonly NLOS

Current assumption

(for tractability)in stochastic

geometry modeling(99.99%

of papers)


78

... Impact of Load of Base Stations …

Resource Blocks Resource Blocks

Inactive for these resource blocksBlocked


79

... Impact of Load of Base Stations …

2 5 10 25 50 1000

0.5

1

1.5

2

2.5

3

Rcell [m]

Rat

e [b

ps/H

z]

full loadpractical load, NRB=1

practical load, NRB=4

practical load, NRB=8


80

... Impact of Antenna Directionality …

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180-15

-10

-5

0

5

10

Azimuth in degrees

Gai

n in

dB

Omni-directional3GPP antenna pattern

Omni-directional antennas

Directional antennas


81

... Impact of Antenna Directionality …

2 5 10 25 50 1000

1

2

3

4

5

6

7

8

9

10

Rcell [m]

Rat

e [b

ps/H

z]

Omni-directional3GPP antenna pattern


82

... Sub-Linear Trend of the Area Spectral Efficiency …

RATE ASE

Intrigued Enough? On Experimental Validation…

83

W. Lu and M. Di Renzo, “Stochastic Geometry Modeling of Cellular Networks: Analysis, Simulation andExperimental Validation”, ACM Int. Conf. Modeling, Analysis and Simulation of Wireless and MobileSystems, Nov. 2015. [Online]. Available: http://arxiv.org/pdf/1506.03857.pdf.W. Lu and M. Di Renzo, “Stochastic Geometry Modeling of mmWave Cellular Networks: Analysis andExperimental Validation”, IEEE Int. Workshop on Measurement and Networking (M&N) – SpecialSession on Advances in 5G Wireless Networks, Oct. 12-13, 2015.

84

… the approach (e.g., 3-ball case) … Practical link-state models are approximated using a multi-ball model

The related parameters are computed using the “intensity matching” criterion

d1

d3

d2

1 1

1

3, ,

,1 LOS,NLOS,...

with 1 1,2, ,n n n n

n n

Nd d d d

S S Sd dn S

p r q r q n N

1

0 0

2

actual approx, max , max

LOS,NLOS, LOS,NLOS,minimize ln 0, ln 0,r S r S

S SF

x x

Rationale of IM Approach: Multi-Ball Approximation

Rationale of IM Approach: Multi-Ball Approximation

85

Why Matching the Intensity Measures ?

Consider the general association criterion as follows:

Ф is a (non-homogeneous) PPP of BSs with density λ(r) = λ*p(r)

l(r) denotes the path-loss function

Υ is a random variable that accounts for all random variables that are takeninto account for cell association except for the distance (e.g., shadowing)

Based on the displacement theorem of PPPs, the set Ψ is a PPP in R+ whoseintensity measure is the following:

0BS is chosen as the of the set minimum ,n

n

l rn

0

0

0, 2 Pr 0,

2 E Pr 0,

l rx x p r rdr

l r x p r rdr

86

Since the intensity measure is now known and Ψ is still a PPP, the coverageprobability can be formulated, after some algebra, as follows:

LOS

NLOS

2

,LOS LOScov LOS NLOS LOS LOS2

LOS

2

,NLOS NLOSLOS LOS NLOS NLOS2

NLOS

2

,LOS

2

P E Pr Pr

E Pr Pr

Pr

o

lagg

o

lagg

o

agg

P h lT l l l l

I l

P h lT l l l l

I l

P h xT x

I x

NLOS LOS

LOS NLOS

0

2

,NLOS

20

CCDF PDF

Pr CCDF PDF

l l

o

l lagg

x x dx

P h yT y y y dy

I y

LOS LOS LOS NLOS NLOS NLOSmin minl l r l l r

Rationale of IM Approach: Multi-Ball ApproximationWhy Matching the Intensity Measures ?

87

void probability th.

CCDF exp 0, PDF CCDFSS S Sl l ld d

2,

2,

2,

,LOS,

2

, , ,,

2

, , ,

PGF

N OS

L

,

, L

2

MGF ; MGF ;MGF ;

E exp

E E e

MGF ;

xp

exp 1 E exp

agg

QQ k Q

agg ag

Q Q

g

k

Q

g

Q

k

a gI SI Q S

k Q k Q k Q Skh

k Q k Q k Q Sk h

k Q

S I

h

I Sw l

w P h l l l

w P h l l l

w P l

l

h

w l w l w

1

1

1

0,

0,Q

S

Q

l

d dl l

l dl

Rationale of IM Approach: Multi-Ball ApproximationWhy Matching the Intensity Measures ?

Intrigued Enough? On Mathematical Modeling…

88M. Di Renzo et al., “The Intensity Matching Approach: A Tractable Stochastic Geometry Approximation toSystem-Level Analysis of Cellular Networks”, IEEE Trans. Wireless Commun., IEEE Early Access.

The Intensity Matching Approach: Main Takes

89

2 5 10 25 50 100 300 10000

0.5

1

1.5

2

2.5

3

3.5

Rcell [m]

Rat

e [b

ps/H

z]

Very Dense

Dense SparseVery Sparse

M. Di Renzo et al., “The Intensity Matching Approach: A Tractable Stochastic Geometry Approximation toSystem-Level Analysis of Cellular Networks”, IEEE Trans. Wireless Commun., IEEE Early Access.


90M. Di Renzo et al., “The Intensity Matching Approach: A Tractable Stochastic Geometry Approximation toSystem-Level Analysis of Cellular Networks”, IEEE Trans. Wireless Commun., IEEE Early Access.


91

2 5 10 25 50 100 300 10000

0.5

1

1.5

2

2.5

3

3.5

Rcell [m]

Rat

e [b

ps/H

z]

Depends on the density of blockages

Depends on the base station load

M. Di Renzo et al., “The Intensity Matching Approach: A Tractable Stochastic Geometry Approximation toSystem-Level Analysis of Cellular Networks”, IEEE Trans. Wireless Commun., IEEE Early Access.

92

SWIPT – System-Level Modeling and Optimization


Joint Statistical Characterization of Harvested Energy and Achievable Rate in the Presence of Other-Cell Interference

Major Difference: Information (rate) and harvesting (energy) requirements need to be jointly satisfiedSetup: LOS/NLOS, beamforming, etc.

Harvesting: Interference is GOOD Information: Interference is BAD

93

SWIPT – The Math in Simple Terms…


94

SWIPT – Trends and Insight

Feasibility Regions: Rate and Energy Targets are BOTH Achieved

(a)

(b)

95


System-Level Optimization & Importance of Channel Modeling

(a)

(b)

96


Impact of Cellular Network Density: Network Densification

97


Impact of Multi-Antenna Transmission: Massive MIMO

R0 [Mbits/sec]

Q0 [d

Bm

]

0 50 100 150 200 250-65

-60

-55

-50

-45

-40

-35

-30

1x12x1

4x1

8x1

16x1

32x1

64x1

128x1

256x1

512x1

1024x1

98


Impact of Multi-Antenna Transmission: Massive MIMO

99

SWIPT – If The Receiver is NOT Adaptive

Impact of Parameter Setups: Two Receive Antennas (MRC, SC)

100

SWIPT – If The Receiver is Adaptive

Impact of Parameter Setups: Adaptation as a function of ρ

101

SWIPT – If The Receiver is Adaptive

Impact of Parameter Setups: Two Receive Antennas (MRC, SC)

102

SWIPT – How Much Power Can We Harvest?

0 1 2 3 4 5 6 7 8 9 10-80

-70

-60

-50

-40

-30

-20

-10

0

Directive Antennas (ULA: Nq = 16)

Q* [d

Bm

]

log2(Nt)

SWIPT – Main Takes

103

Takeaway messages for system-level analysis (proofs in the paper): Optima power splitting and time switching ratios exist and are unique

Power splitting outperforms time switching if they operate at theirrespective optima

Impact of directional beamforming: reducing the other-cell interferenceleads to the optimum

Impact of base stations density: existence of an optimal deploymentdensity

Wireless power transfer: Densification of base stations and antennas ismandatory for (possibly) making it a reality Energy-Neutral design

Design RuleNetwork densification: To bring the access points closer to the users

Directional beamforming: To reduce the other-cell interference generated by network densification and to enhance the power gain of the intended link

The System-Level Side of 5G – YouTube Video

104https://youtu.be/MB8IvOYYvB0

105

Some Reference Papers… M. Di Renzo and W. Lu, “System-Level Analysis/Optimization of Cellular Networks

with Simultaneous Wireless Information and Power Transfer: Stochastic GeometryModeling”, IEEE Trans. Vehicular Technol., IEEE Early Access.

Y. Deng, L. Wang, M. Elkashlan, M. Di Renzo, and J. Yuan, “Modeling and Analysis ofWireless Power Transfer in Heterogeneous Cellular Networks”, IEEE Trans.Commun., IEEE Early Access.

T. Tu Lam, M. Di Renzo, and J. P. Coon, “System-Level Analysis of SWIPT MIMOCellular Networks”, IEEE Commun. Lett., IEEE Early Access.

T. Tu Lam, M. Di Renzo, and J. P. Coon, “System-Level Analysis of Receiver Diversityin SWIPT-Enabled Cellular Networks”, IEEE/KICS J. Commun. & Networks, IEEEEarly Access.

W. Lu, M. Di Renzo, and T. Q. Duong, “On Stochastic Geometry Analysis andOptimization of Wireless-Powered Cellular Networks”, IEEE GLOBECOM, Dec.2015.

T. Tu Lam, M. Di Renzo, and J. P. Coon, “MIMO Cellular Networks withSimultaneous Wireless Information and Power Transfer”, IEEE SPAWC, July 2016.

Thank You for Your Attention

Marco Di Renzo, Ph.D., H.D.R.Chargé de Recherche CNRS (Associate Professor)Editor, IEEE Communications LettersEditor, IEEE Transactions on CommunicationsDistinguished Lecturer, IEEE Veh. Technol. SocietyDistinguished Visiting Fellow, RAEng-UK

Paris-Saclay UniversityLaboratory of Signals and Systems (L2S) – UMR-8506CNRS – CentraleSupelec – University Paris-Sud3 rue Joliot-Curie, 91192 Gif-sur-Yvette (Paris), France

E-Mail: [email protected]: http://www.l2s.centralesupelec.fr/perso/marco.direnzo

ETN-5Gwireless (H2020-MCSA, grant 641985)

An European Training Network on 5G Wireless Networks

http://cordis.europa.eu/project/rcn/193871_en.html (Jan. 2015, 4 years)

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