wireless powered communication: from theory to applications · wireless powered communication: from...

1Globecom 2016

Wireless Powered Communication: From Theory to Applications

Rui Zhang

ECE Department, National University of Singapore

Globecom 2016, Washington, DC USA

Rui Zhang, National University of Singapore

Agenda

• Part I: Wireless Power Transfer (WPT): Introduction & Applications

• Part II: Communications and Signals Design for Microwave WPT

• Part III: WPT and Wireless Communication Co-design

Globecom 2016 2

Part I: WPT: Introduction & Applications Rui Zhang, National University of Singapore

3Globecom 2016

Why Wireless Power?Rui Zhang, National University of Singapore

Wireless power transfer (WPT): deliver power without wires Advantages over traditional energy supply methods:

Convenient: without the hassle of connecting wires and replacing batteries Cost-effective: on-demand power supply with uninterrupted operations Environmental friendly: avoid battery disposal

Extensive applications: Consumer electronics wireless charging Biomedical implants wireless charging Wireless sensor/IoT devices charging Backscatter/RFID communications Simultaneous wireless information and power transfer (SWIPT) Wireless powered communications (WPC)

Part I: WPT: Introduction & Applications

Globecom 2016 4


Inductive coupling

Magnetic resonant coupling

Electromagnetic (EM) radiation

Laser power beaming

Overview of Main WPT Technologies


Globecom 2016 5


Near-field technique based on magnetic induction Main advantage: Very high efficiency (e.g. >90%) Main limitations

Require precise tx/rx coil alignment, very short range, single receiver only Example Applications

Electric vehicle charging, smart phone charging, RFID, smart cards, … Industry standard: Qi (Chee) Representative companies: Powermat, Delphi, GetPowerPad,

WildCharge, Primove, …

Inductive Wireless Power Transfer


Globecom 2016 6


Near-field technique based on magnetic resonant coupling Main advantages: high efficiency and mid-range, one-to-many (multicast) charging Main limitations: sensitive to tx/rx coil alignment, large tx/rx size Applications

Similar to inductive coupling, but target for longer range and multicasting Industry standard: Qi, AirFuel,… Representative companies: Intel, PowerbyProxi, WiTricity, WiPower,….

Magnetic Resonant Wireless Power Transfer


Wireless Power Transfer via Magnetic Resonant Coupling in 2000s

Globecom 2016 7

Demonstration of magnetic coupling to power light bulb (Intel Corp.) and charge mobile phones (Witricity Corp.)

Rui Zhang, National University of SingaporePart I: WPT: Introduction & Applications

Globecom 2016 8


Far-field WPT technique via EM/microwave radiation Main advantages:

long range, small tx/rx form factors, flexible deployment, support power multicasting with mobility, applicable for both LoS and Non-LoS environment, integration with wireless communication (backscatter, SWIPT, WPCN)

Main limitations: low efficiency, safety and health issues Extensive Applications

Wireless sensor/IoT devices charging, RFID, solar power satellite,… Representative companies: Intel, Energous, PowerCast, Ossia,…

Radiative Wireless Power Transmission

Energy flow


Radiative Wireless Power Transmission

Globecom 2016 9


Globecom 2016 10


WPT via highly concentrated laser emission Main advantages

long range, compact size, high energy concentration, no interference to existing communication systems or electronics

Main limitations laser radiation is hazardous, require LoS link and accurate rx focusing,

vulnerable to cloud, fog, and rain Applications

Laser-powered UAVs, laser-powered solar power satellite,… Representative company: LaserMotive, …

Laser Power BeamingPart I: WPT: Introduction & Applications

NASA’s Wireless Power Transfer Project Using Laser Beam

Globecom 2016 11


Wireless Power Transfer: State-of-the-Art Technology

Range

Efficiency

inductive coupling

magnetic resonant coupling

Electromagnetic (EM) radiation

<5cm pre-determined distance, e.g., 15-30cm

>1m

Globecom 2016 12


Application Examples

Globecom 2016 13

Inductive Coupling

Magnetic ResonantCoupling

EM Radiation

The Qi wireless mobile device charging Standard Electric tooth brush

Wireless powered medical implants

Qualcomm eZonewireless charging

Qualcomm Halo electric vehicle powered by charging pad

Haier wireless powered HDTV

Intel WISP RFID tags harvest energy from RF radiation

Powercast RF harvesting circuit for sensor networks

The SHARP unmanned plane receives energy beamed from the ground


Comparison of the Main WPT Technologies

Strength Efficiency Distance Multicast Mobility Safety

Inductive Coupling Very high Very high Very short No No Yes

Magnetic Resonant Coupling

High High Short Yes Difficult Yes

EM Radiation

Omni-directional

Low Low Long Yes Yes Yes

Beamforming (microwave)

High High Very long(LOS)

Yes Yes Safety constraints may apply

Laser beaming High High Long No Difficult Safety constraints may apply

Globecom 2016 14


This tutorial will focus on EM radiation WPT technology and its applications in wireless powered communication


15

Energy-Aware Wireless Communications: An Overview Rui Zhang, National University of Singapore

Wireless power transfer

Green communications

Smart grid

Energy harvesting

Globecom 2016


Wireless Communication Powered by Batteries (Conventional)


Need manual battery recharging/replacement Costly, inconvenient, abruption to use Inapplicable in some scenarios, e.g., implanted medical devices,

sensors built in cement structures

16Globecom 2016


Wireless Communication Powered by Energy Harvesting (More Recent)Rui Zhang, National University of Singapore

External energy source: solar, wind, vibration, ambient radio power, etc. Inexpensive, green, renewable Intermittent and uncontrollable, costly/bulky harvesting and storage devices

17Globecom 2016


Wireless Communication Powered by Wireless Power Transfer (Emerging)Rui Zhang, National University of Singapore

Wireless charging fully controllable Wide coverage, low production cost, and small receiver Main challenges: low efficiency of wireless power transfer, wireless

information and power transfer co-design

18Globecom 2016


Wireless Powered Communication Applications (1)Rui Zhang, National University of Singapore

19Globecom 2016



20Globecom 2016



21Globecom 2016



Energy transfer

Information transfer

Energy Receivers

Information Receivers

Hybrid Information and Energy Access Point

22Globecom 2016


A Generic Model

Hybrid Access point

Energy and/or InformationReceiver

Information flowEnergy flow

Downlink (DL)

Uplink (UL)

Three “Canonical” Models/Modes Wireless Power Transfer (WPT) in DL

Wireless Powered Communication Network (WPCN): DL WPT and UL wireless information transmission (WIT)

Simultaneous wireless information and power transfer (SWIPT): DL WPT and WIT at the same time

Energy and/or InformationReceiver


23Globecom 2016


General Network Model


Three canonical operating modes Wireless power transfer (WPT): AP2 -> WD5 Wireless powered communication (WPC): AP1 <-> WD3, AP2->WD6->AP3 Simultaneous wireless information power transfer (SWIPT): AP1->WD4, AP1->WD1/WD2

24Globecom 2016


Agenda




Globecom 2016 25


Outline of Part II

Microwave WPT: Historical development and contemporary design

WPT and energy receiver model

Single-user WPT

Multi-user WPT

Extensions and future work

Globecom 2016 26

Part II: Communications & Signals Design for Microwave WPT Rui Zhang, National University of Singapore

Microwave Wireless Power Transmission: Historical MilestonesYear Main activity and achievement1888 Heinrich Hertz demonstrated electromagnetic wave propagation in free space

1899 Nicola Tesla conducted the first experiment on dedicated WPT

1901 Nicola Tesla started the Wardenclyffe Tower project

1964 William C. Brown invented rectenna

1964 William C. Brown successfully demonstrated the wireless-powered tethered helicopter

1968 William C. Brown demonstrated the beam-positioned Helicopter

1968 Peter Glaser proposed the SPS concept

1975 Over 30kW DC power was obtained over 1.54km in the JPL Goldstone demonstration

1983 Japan launched the MINIX project

1987 Canada demonstrated the free-flying wireless-powered aircraft 150m above the ground

1992 Japan conducted the MILAX experiment with the phased array transmitter

1993 Japan conducted the ISY-METS experiment

2008 Power was successfully transmitted over 148km in Hawaii

2015 Japan announced successful power beaming to a small device

Globecom 2016 27

Rui Zhang, National University of SingaporePart II: Communications & Signals Design for Microwave WPT

Radiative Wireless Power Transmission: Nikola Tesla and his Wardenclyffe Project in early 1900

150 KHz and 300 kW. Unsuccessful and never put into practical use.

Globecom 2016 28


The Invention of ``Rectenna” for Microwave Power Transmission:the Microwave Powered Helicopter by William C. Brown in 1960s

2.45 GHz and less than 1kW. Overall 26% transfer efficiency at 7.6 meters high.

Globecom 2016 29


Solar Satellite with Microwave Power Transmission (1970s-current)

NASA Sun Tower

Target at GW-level power transfer with more than 50% efficiency

Globecom 2016 30


Globecom 2016 31


2.411 GHz288 elements phased array on

the roof of the car120 rectennas on the fuel-free

airplane DC output power ~88W

Microwave Power Transfer Field Experiment with Phased Array (1992)

Part II: Communications & Signals Design for Microwave WPT

Microwave Wireless Power Transmission: A Fresh New Look

Globecom 2016 32


Historical microwave WPT: Targeting for long distance and high power Mainly driven by the wireless-powered aircraft and SPS applications Requires high transmission power, huge tx/rx antennas, clear LoS link

Contemporary WPT systems: Low-power delivery over moderate distances Reliable and convenient WPT network for low-power devices (sensors, IoT

devices, RFID tags, smart phone, etc.) New design challenges and requirements:

Range: a few meters to hundreds of meters Efficiency: a fractional of percent Non-LoS: closed-loop WPT with channel state information Mobility support: device tracking Ubiquitous and authenticated accessibility Inter-operate with wireless communication systems Safety and health guarantees


Outline of Part II



Single-user WPT

Multi-user WPT


Globecom 2016 33


Wireless Power Transmission: A Generic Model

Globecom 2016 34


End-to-end efficiency:

e1: DC-to-RF conversion efficiency at energy transmitter (ET) e2: RF-to-RF transmission efficiency, main bottleneck

Require highly directional transmission with multi-antenna and accurate channel knowledge at ET

e3: RF-to-DC conversion efficiency at energy receiver (ER) Require efficient rectenna design and power waveform optimization


Narrowband Wireless Power Transmission: Channel Model

Globecom 2016 35


Modulated vs. Unmodulated Energy Signal

Globecom 2016 36

Use pseudo-random modulated energy signal to avoid the spike in the power spectral density (PSD) caused by constant unmodulated energy signal


Wireless Power Transmission: Receiver Model (1)

Globecom 2016 37


Only keep the second-order term since y(t) is typically small


Wireless Power Transmission: Receiver Model (2)

Globecom 2016 38

[4]


The harvested DC power is proportional to the input RF power (linear model) Nonlinear model if higher-order terms are considered [47]


Outline of Part II



Single-user WPT

Multi-user WPT


Globecom 2016 39


Single-User Multi-Band MIMO WPT

Globecom 2016 40


Single-user MIMO WPT with Mt antennas at ET and Mr antennas at ER N frequency sub-bands, with MIMO channel gains H1,…,HN The received power is (assuming linear model):

Power maximization problem:

: transmit covariance matrix at sub-band n nS

rf

rf

: sum-power limit: per-subband power limit

' , where 1 '

t

st

s

PP

P N P N N= ≤ ≤


Energy Beamforming for Multi-Band MIMO WPT

Globecom 2016 41


Optimal solution:

: dominant eigenvector of Hn n nv H H

[ ] : permutation of sub-bands with theirdominant eigvenvalues in decreasing order•

Concentrate power to the N’strongest sub-bands

For each sub-band, concentrate power to the strongest eigen-direction

In contrast to multi-band MIMO communication systems

Optimal value:

Exploit both frequency-diversity gain and spatial energy-beamforming gain

max,[ ] : the dominant eigenvalue of the th strongest sub-bandn nλ


Channel Acquisition for MIMO WPT

Globecom 2016 42


Energy beamforming requires channel state information (CSI) at the ET Unique considerations for CSI acquisition in WPT in contrast to conventional

wireless communication: CSI at (energy) receiver: not required for WPT Net energy maximization: to balance the energy overhead for CSI acquisition and

the energy harvested with CSI-based energy beamforming Hardware constraint: no/low signal processing capability for low-cost ERs

Candidate solutions depending on the antenna architecture at the ER Forward-link training with CSI feedback Reverse-link training via channel reciprocity Power probing with limited energy feedback


Antenna Architecture of ER

Globecom 2016 43


For enabling CSI acquisition, each ER must have a communication module, in addition to the energy harvesting module

Shared-antenna architecture The same set of antennas used for both energy harvesting and communication Energy harvesting and communication take place in a time-division manner Compact receiver form factor, easy channel estimation But require communication and energy harvesting at the same frequency, and

new frontend design of ER Separate-antenna architecture

Different antennas for energy harvesting and communication, independent and concurrent operations, and commercial off-the-shelf hardware available


CSI Acquisition (1): Forward-Link Training with CSI Feedback

Globecom 2016 44


Applicable for shared-antenna architecture only Similar to conventional wireless communications, pilot signals sent by the

ET to the ER for channel estimation ER then feeds back the estimated channel to ET Limitations:

Training overhead scales with the number of antennas at ET, not suitable for massive MIMO WPT

Requires channel estimation and/or feedback by ER, though it does not require CSI for energy harvesting


CSI Acquisition (2): Reverse-Link Training via Channel Reciprocity

Globecom 2016 45


Applicable for shared-antenna architecture only Exploits channel reciprocity: ER sends pilot signals to ET for channel estimation Advantages:

No channel estimation or feedback required at ER Time/energy training overhead independent of number of ET antennas, suitable for

massive MIMO WPT Limitations: Critically depends on channel reciprocity (holds in practice?) New design trade-offs:

Too little training: coarsely estimated channel, reduced energy beamforming gain Too much training: consumes excessive energy at ER, less time for energy transfer

Maximize net energy at ER: harvested energy – energy consumed for training


Example of Reverse-Link Training

Globecom 2016 46


Single-band WPT over Rayleigh fading MIMO channel with average gain β Quasi-block fading channel with channel coherence time T Reverse-link training with Mr’≤Mr ER antennas trained Average harvested energy at ER [28]:

Net harvested energy:

( ) ''max

: training power by ER

( , ) , with having i.i.d. Gaussian entries of unit variancer t

r

M MHt r

p

M M Cλ × Λ = ∈ XE X X X


Net Harvested Power versus Number of Trained ER Antennas

Globecom 2016 47


rf

Number of ET antennas: =5Number of ER antennas: =10

1 Watt, =-60 dB

t

rt

MM

P β=


CSI Acquisition (3): Power-Probing with Energy Feedback

Globecom 2016 48


Applicable for separate-antenna architecture ET sends energy signals with online designed transmit covariance matrices ER measures the amount of harvested energy during each interval ER sends a finite-bit feedback based on its present and past energy measurements ET obtains refined CSI estimation based on the feedback bits

Advantages: Low signal processing requirement at the ER, no need for hardware change Simultaneous energy harvesting not interrupted

Limitations: Training overhead increases with the number of ET antennas


Power-Probing with One-Bit Feedback: Case Study [6]

ER k feeds back one-bit information indicating increase or decrease of harvested energy between time slots n and n-1

With one-bit feedbacks, ET Adjusts transmit beamforming for next slot Obtains improved estimation of channels

Globecom 2016 49


Two-Phase WPT Protocol (1)

Phase 1 Channel Learning: feedback intervals each of length In the nth interval ET sends one or more energy beams with covariance Each ER measures its harvested energy Each ER feeds back one bit to ET based on

ET updates based on ’s After channel learning phase, ET estimates as , and obtains

estimated dominant eigenvector of as

Globecom 2016 50


Two-Phase WPT Protocol (2)

Phase 2 Energy transmission: ET sends one single energy beam with covariance

Total harvested energy over two phases

Globecom 2016 51

energy harvested in Phase 1

energy harvested in Phase 2


ACCPM Based Channel Learning

Objective: find any point in target set Analytic center cutting plane method (ACCPM) [24] : Iteratively shrink working set towards target set. In the nth iteration Find analytic center of working set Find cutting plane whose boundary passes (neutral cutting plane) Cut away half space according to cutting plane to obtain new working set

Q: How to cut half-space? A: Based on one-bit feedback (energy increase or decrease at ER)

Globecom 2016 52

1n−P

X

Cutting planenH

1n n n−= P P H

X

G(n) G(n)~ ~


Convergence Analysis

ACCPM based single user channel learning algorithm obtains estimation for with in at most intervals

Convergence speed only depends on No. of transmit antennas , but not on No. of receive antennas

Reason: Dimension of :

Theoretical bound only, faster convergence is often observed in simulation

Globecom 2016 53


Simulation Result: Setup

6 ERs, all 5 meters from ET ET with antennas, each ER with antennas Rician fading channels Transmit power: Energy transfer efficiency:

Globecom 2016 54

ET

ER 1ER 2

ER 3

ER 4

ER 5

ER 6

5 meters


Simulation Result: Baseline Schemes

Globecom 2016 55

Partial CSIT: existing one-bit feedback based channel learning schemes Cyclic Jacobi technique (CJT) [25]

One-bit feedback: increase or decrease in received power Usage of feedback: perform blind estimate of EVD of MIMO channel Application: MIMO, one receiver only

Gradient sign [26] One-bit feedback: increase or decrease in received power Usage of feedback: adjust transmit beam with random perturbation Application: MIMO, one receiver only

Distributed beamforming [27] One-bit feedback: larger or smaller than prior highest received power Usage of feedback: adjust phase of transmit beam Application: MISO, one receiver only

Perfect CSIT: optimal EB


Simulation Result

Globecom 2016 56

CJT: discrete error points Gradient sign and distributed beamforming: larger step size yields faster

convergence but more fluctuations ACCPM: best accuracy & convergence

Consider only ER 1 is activeAbsolute error of harvested power versus No. of feedback intervals


Outline of Part II



Single-user WPT

Multi-user WPT


Globecom 2016 57


Multi-User MIMO Energy Multicasting


Utilize the broadcast nature of microwave propagation for energy multicast Energy near-far problem: fairness is a key issue in the multi-user EB designMultiple beams are needed in general to balance the energy harvesting

performance among users

58Globecom 2016

Multi-User WPT: Network Architecture

J distributed ETs simultaneously serve K ERs each having multiple antennas Three main networking architectures (with complexity from high to low):1. CoMP (Coordinated Multi-Point) WPT

All ETs jointly design energy signals to the K ERs based on global CSI Only requires exchange of CSI and waveform parameters among ETs, as opposed

to message exchange in CoMP communications2. Locally-coordinated WPT

Each ER is served by a subset of ETs ET-oriented association: group the ETs into clusters, with each cluster ETs

cooperatively serving a subset of ERs ER-oriented association: each ER is freely associated with a subset of ETs

3. Single-ET WPT: each ER served by exactly one ET

Globecom 2016 59


Multi-User WPT: Power Region Characterization

Considering CoMP-based WPT, the harvested power at the ERs are

Power region: the set of all achievable power tuples by the K ERs

Pareto-boundary: the power-tuples at which it is impossible to increase the power of one ER without reducing that of others

Pareto-boundary characterization (analogous to capacity region in multi-user communications) Weighted-sum-power maximization (WSPMax) Power-profile method

Globecom 2016 60


: MIMO channel from all the ETs to ER : the covariance matrix of the signal transmitted by all ETsk J k

JHS

Weighted-Sum-Power Maximization

The WSPMax problem for power region characterization can be formulated as

Semidefinite programming (SDP) problem, can be efficiently solved by standard convex optimization techniques or existing software toolbox

For single ET with J=1, equivalent to point-to-point MIMO WPT with an equivalent channel

For Pareto boundary with hyper-planes, WSPMax only obtains the vertex points

Time sharing is thus needed in general to attain inner points on the boundary

Globecom 2016 61


1

0 : weight for ER

1

kK

kk

kµ

µ=

≥

=∑

Power-Profile Method for Pareto Boundary Characterization

The power-profile approach for power region characterization solves the problem

SDP problem again, thus can be efficiently solved The optimal solution has rank greater than 1 in general, i.e., multi-beam WPT The same performance can be achieved with single-beam WPT together with

time sharing

Globecom 2016 62


1

0 : weight for ER

1

kK

kk

kα

α=

≥

=∑

Simulation Results

Globecom 2016 63


A WPT system that serves a square area of 30m x 30m with co-located versus distributed antennas

Co-located antennas: a single ET with 9-element uniform linear array (ULA) at the center of the serving area

Distributed antennas: 9 ETs each with single antenna equally spaced in the area

Two single-antenna ERs at (15m, 5m) and (18.88m, 29.49m), which are 10m and 15m away from the area center, respectively

Total transmit power of the system is 2W Simulation 1: maximize the minimum (max-min) harvested power

by the two ERs Simulation 2: find the achievable power region of the two users

Spatial Power Distribution with Max-Min Solution

Globecom 2016 64


(a) Co-located antenna system (b) Distributed antenna system

Power beamed towards the ERs in co-located antenna system More even spatial power distribution for distributed antenna system

Achievable Power Region with Co-located vs Distributed Antennas

Globecom 2016 65


Distributed antenna system improves the performance of ER2 at the cost of degrading the performance of ER1, thus helps mitigating the near-far problem in co-located antenna system

Outline of Part II



Single-user WPT

Multi-user WPT


Globecom 2016 66


Nonlinear Energy Harvesting Model (1): Efficiency vs. Input Power

In practice, the RF-DC conversion efficiency varies with input power Energy beamforming needs to take into account this non-linear model


67Globecom 2016


Harvested Power vs. Input Power with Curve Fitting

The non-linear model can be obtained via curve fitting based on measured data [48]


68Globecom 2016


Nonlinear Energy Harvesting Model (2): Efficiency vs. Waveform

Waveform with high peak-to-average power ratio (PAPR) tends to give better energy conversion efficiency, thus new waveform design is needed for WPT


69Globecom 2016


Harvested Power versus Signal PAPR

Waveform optimization by exploiting non-linear energy harvesting model [47]


70Globecom 2016


Other Extensions & Future Work

Globecom 2016 71


Channel acquisition in frequency-selective [49] and/or multi-user channels

WPT with retrodirective amplification [50]

Energy outage minimization in delay-sensitive applications

Distributed channel training and energy beamforming [51]

Massive MIMO and mmWave WPT [52][53][54]

WPT with safety and health related constraints

Coexisting with wireless communication and interference management [55]

Higher layer (MAC, Network, etc.) design issues in WPT [56]

Hardware development and applications


Agenda




Globecom 2016 72


Outline of Part III

Wireless powered communication network (WPCN)

Simultaneous wireless information and power transfer (SWIPT)

Extensions and conclusions

Globecom 2016 73

Rui Zhang, National University of SingaporePart III: WPT and Wireless Communication Co-design

Wireless Powered Communication Network (WPCN)

Harvest-then-transmit protocol [2] Phase I: AP broadcasts energy to all wireless devices (WDs) to harvest energy in DL Phase II: WDs transmit individual messages with harvested energy to the AP in UL

TDMA-based multiple access Orthogonal transmission in the UL

SDMA-based multiple access Spatial multiplexing in the UL

Globecom 2016 74


“Doubly” Near-far Problem

Doubly Near-Far Problem Due to distance-dependent signal attenuation in both DL and UL “Near” user harvests more energy in DL but transmits less power in UL “Far” user harvest less energy in DL but transmits more power in UL Results in unbalanced energy consumptions in the network

Globecom 2016 75


Potential Solutions to Doubly Near-far Problem

(a): Joint communication and energy scheduling, transmit (energy)/receive (information) beamforming

(b): Wireless powered cooperative communication


76Globecom 2016

Part III: WPT and Wireless Communication Co-design

Wireless Powered Communications: Various Setups


77

(a): Separate energy/information access point (AP) (b): Co-located energy/information AP

(c): Out-band half-duplex energy/information (d): In-band full-duplex energy/information

Globecom 2016


Throughput Comparison of Different Setups


1 2 3 4 5 6 7 8 92

4

6

8

10

12

14

16

18

d (meters)

Thro

ughp

ut (b

ps/H

z)

Half duplex, co-locatedHalf duplex, separatedFull duplex, co-locatedFull duplex, separated

For full-duplex: 80 dB self-interference

cancellation at AP 10% self-energy recycling [46]

at wireless device (WD)

78Globecom 2016


Outline of Part III




Globecom 2016 79



SWIPT: Rate-Energy Tradeoff at Transmitter Side

Wireless Power Transfer vs. Wireless Information Transfer Power Transfer :

Information Transfer :

Optimal transmit power allocation in frequency-selective channel

Maximize data rateMaximize energy transfer

Q hPTζ∝

( )2log 1R T hP∝ +

80


MIMO SWIPT with Two Separate EH and ID Terminals

Rate-energy region: all the achievable rate and energy pairs under a given transmit power constraint P

Globecom 2016 81

Each terminal has 4 antennas, P = 1W, energy receiver 1m/information receiver 10m from transmitter

eigenmode transmission + WF

energy beamforming



SWIPT: Rate-Energy Tradeoff at Receiver Side

Practical receiver cannot harvest energy and decode information from the same signal

Time switching receiver

Power splitting receiver

Integrated EH/ID receiver

Antenna switching receiver

82



Rate-Energy Region of SWIPT in Point-to-Point AWGN

0 1 2 3 4 5 60

10

20

30

40

50

60

Rate(bits/channel use)

Ene

rgy

Uni

t

Ideal RxPower Splitting RxTime Switching RxIntegrated Rx

?

83



Dual Role of Interference in SWIPT

Interference is harmful to information receiver but useful to energy harvesting Opportunistic EH and ID in fading channel via receiver mode switching In general, this opens a new paradigm for interference management

84


Dynamic Power Splitting vs. Dynamic Antenna Switching

Globecom 2016 85

Dynamic antenna switching is a special case of power splitting with on/off (two-level) power splitting ratio per receive antenna

Dynamic power splitting achieves better R-E region than antenna switching, but antenna switching has much lower hardware complexity


Joint Information and Energy Beamforming for SWIPT

SWIPT with Separate EH/ID Receivers SWIPT with Co-located EH/ID Receivers

Energy transferInformation transfer

1U

h1

hK

g1

gK

UK

UK +1

UK +K

E

E

E

E

I

I

Energy transferInformation transfer

1U

2U

KU

h1

g1=h1h2

hK

g2=h2

gK=hK


Joint transmit beamforming and receiver design optimization to maximize transferred energy and information under heterogeneous power/rate requirements of the users

86


Outline of Part III




Globecom 2016 87


Wireless Power Meets Energy Harvesting


Hybrid energy supplies via both environmental energy harvesting and dedicated wireless power transfer

Wireless powered communication needs to be jointly designed with energy harvesting communication

88


Wireless Powered Cognitive Radio Network


Conventional cognitive radio (CR): secondary user is idle when nearby primary user is transmitting

Wireless powered CR: secondary user harvests energy from nearby active primary transmitters

89


Wireless Information and Power Transfer Coexisting


Wireless power transfer/wireless powered communication coexists with existing communication systems

New spectrum sharing models and techniques needed to maximize spectrum/energy efficiency [55]

90


Secure Communication in SWIPT

Security issue in SWIPT ER can easily eavesdrop IR’s information Two conflicting goals: Energy transfer: received power at each ER should be large Secure information transfer: received power at each ER should be small How to resolve this conflict? Exploiting artificial noise [32]

energy signal artificial noise

AP

ERs

IRs

information signal


Alice

BobEve

91


Multi-Transmitter Collaborative SWIPT

An 2×2 interference channel for SWIPT with TS receivers

Receivers use time switching (TS) or power splitting (PS) Transmitters cooperate in joint information and energy transmission Interference channel rate-energy tradeoff


92


SWIPT with Energy/Information Relaying

Time Switching Relay


Power Splitting Relay

Source Relay Destinationh g

energy transmissionA relay-assisted link, where the relay is wirelessly charged by RF signals from the source

Information transmission

Full Duplex Relay

Globecom 2016 93


Globecom 2016 94

SWIPT in Multi-User OFDM

OFDMA with PS receivers : PS is performed before digital OFDM demodulation. Thus, all subcarriers would have the same PS ratio at each receiver.

TDMA with TS receivers : Each user performs ID when information is scheduled for that user, and performs EH in all other time

R-E tradeoff characterizations for multi-carrier SWIPT

Multi-user OFDM OFDM Receiver with Power Splitting (PS)


Conclusions


Wireless power transfer (WPT)

Wireless poweredcommunication network

(WPCN)

Simultaneous wireless information and power transfer

(SWIPT)

Energy beamforming

Energy feedback

Energy/Communication full-duplex

Joint energy and communication scheduling

Joint information and energy beamforming

Separated vs. Integrated receivers

Nonlinear energy receiver model

Doubly near-far problem

Rate-energy tradeoff

Waveform optimization

Self-energy recycling Secrecy SWIPT

Energy

Energy

Information

Energy

Information

Multiuser power region

Wireless information and power transfer coexisting

Harmful vs. useful interference

Energy multicasting

95Globecom 2016

Conclusions


Future Work Directions

Nonlinear energy harvesting model, waveform design for WPT/WPCN/SWIPT Near-field WPT/WPCN/SWIPT Fundamental limits and signal processing methods for WPT/WPCN/SWIPT Backscatter/re-scatter communications Massive MIMO/Millimeter wave based WPT/WPCN/SWIPT Small-cell, C-RAN, and distributed antennas for WPT/WPCN/SWIPT Imperfect CSIT and practical channel acquisition for WPT/WPCN/SWIPT Full-duplex WPCN/SWIPT Coexistence of wireless communication and power transfer systems Higher layer (MAC, Network, etc.) design issues in WPT/WPCN/SWIPT Safety/security/economic issues in WPT/WPCN/SWIPT Hardware development, applications, …

96

Conclusions


For more details, please refer to

S. Bi, C. K. Ho, and R. Zhang, “Wireless powered communication: opportunities and challenges,” IEEE Communications Magazine, vol. 53, no. 4, pp.117-125, April, 2015.

S. Bi, Y. Zeng, and R. Zhang, “Wireless powered communication networks: an overview,” IEEE Wireless Communications, vol. 23, no. 4, pp. 10-18, April 2016.

Y. Zeng, B. Clerckx, and R. Zhang, “Communications and signals design for wireless power transmission,” submitted to IEEE Trans. Commun. (Invited Paper), Nov., 2016.

97Globecom 2016

References

Globecom 2016 98

References

References[1] R. Zhang and C. K. Ho, “MIMO broadcasting for simultaneous wireless information and power transfer,” IEEE Transactions on Wireless Communications, vol. 12, no. 5, pp. 1989-2001, May 2013. [2] H. Ju and R. Zhang, “Throughput maximization in wireless powered communication networks,” IEEE Transactions on Wireless Communications, vol. 13, no. 1, pp. 418-428, Jan. 2014.[3] L. Xie, Y. Shi, Y. T. Hou, and H. D. Sherali, “Making sensor networks immortal: an energy-renewable approach with wireless power transfer,” IEEE/ACM Transactions on Networking, vol. 20, no. 6 pp. 1748-1761, Dec. 2012.[4] X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer: architecture design and rate-energy tradeoff,” IEEE Transactions on Communications, vol. 61, no. 11, pp. 4757-4767, Nov. 2013.[5] S. Lee, L. Liu, and R. Zhang, “Collaborative wireless energy and information transfer in interference channel,” IEEE Transactions on Wireless Communications, vol. 14, no. 1, pp. 545-557, Jan., 2015.[6] J. Xu and R. Zhang, “Energy beamforming with one-bit feedback,” IEEE Transactions on Signal Processing. vol. 62, no. 20, pp. 5370-5381, Oct., 2014.[7] H. Ju and R. Zhang, “User cooperation in wireless powered communication networks,” IEEE Global Communications Conference (Globecom), 2014. [8] K. Huang and V. K. N. Lau, “Enabling wireless power transfer in cellular networks: architecture, modelling and deployment,” IEEE Transactions on Wireless Communications, vol. 13, no. 2, pp. 902-912, Feb. 2014.


Globecom 2016 99

References

[9] S. Lee, R. Zhang, and K. B. Huang, “Opportunistic wireless energy harvesting in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 12, no. 9, pp. 4788-4799, Sept. 2013.[10] P. Grover and A. Sahai, “Shannon meets Tesla: wireless information and power transfer”, in Proc. IEEE ISIT, pp. 2363-2367, 2010.[11] L. R. Varshney, “Transporting information and energy simultaneously,” in Proc. IEEE ISIT, pp. 1612-1616, 2008.[12] L. Liu, R. Zhang, and K. C. Chua, “Wireless information transfer with opportunistic energy harvesting,” IEEE Transactions on Wireless Communications, vol. 12, no. 1, pp. 288-300, Jan. 2013.[13] L. Liu, R. Zhang, and K. C. Chua, “Wireless information and power transfer: a dynamic power splitting approach,” IEEE Trans. Communications, vol. 61, no. 9, pp. 3990-4001, Sept. 2013.[14] J. Park and B. Clerckx, “Joint wireless information and energy transfer in a two-user MIMO interference channel,” IEEE Trans. Wireless Communications, vol. 12, no. 8, pp. 4210-4221, Aug. 2013.[15] C. Shen, W. C. Li, and T. H. Chang, “Simultaneous information and energy transfer: a two-user MISO interference channel case,” in Proc. IEEE Globecom, 2012. [16] S. Timotheou, I. Krikidis, and B. Ottersten, “MISO interference channel with QoS and RF energy harvesting constraints,” in Proc. IEEE ICC, 2013.[17] A. A. Nasir, X. Zhou, S. Durrani, and R. A. Kennedy, “Relaying protocols for wireless energy harvesting and information processing,” IEEE Trans. Wireless Communications, vol. 12, no. 7, Jul. 2013.[18] I. Krikidis, S. Timotheou, and S. Sasaki, “RF energy transfer for cooperative networks: data relaying or energy harvesting,” IEEE Wireless Communication Letters, vol. 16, no. 11, pp. 1772-1775, Nov. 2012.


Globecom 2016 100

References

[19] X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer in multiuser OFDM systems,” IEEE Trans. Wireless Communications, vol. 13, no. 4, pp. 2282-2294, Apr. 2014.[20] K. Huang and E. Larsson, “Simultaneous information and power transfer for broadband wireless systems", IEEE Trans. Signal Processing, vol. 61, No. 23, pp. 5972-5986, Dec 2013.[21] D. W. K. Ng, E. S. Lo, and R. Schober, ``Wireless information and power transfer: energy efficiency optimization in OFDMA systems," IEEE Trans. Wireless Communications, vol. 12, no. 12, pp. 6352-6370, Dec. 2013.[22] J. Xu, L. Liu, and R. Zhang, “Multiuser MISO beamforming for simultaneous wireless information and power transfer,” IEEE Transactions on Signal Processing, vol. 62, no. 18, pp. 4798-4810, Sep., 2014.[23] Q. Shi, L. Liu, W. Xu, and R. Zhang, “Joint transmit beamforming and receive power splitting for MISO SWIPT systems,” IEEE Transactions on Wireless Communication, vol. 13, no. 6, pp. 3269-3280, June, 2014.[24] S. Boyd, “Convex optimization II,” Stanford University. Available online at http://www.stanford.edu/class/ee364b/lectures.html.[25] Y. Noam and A. Goldsmith, “The one-bit null space learning algorithm and its convergence,” IEEE Transactions on Signal Processing, vol. 61, no. 24, pp. 6135-6149, Dec. 2013.[26] B. C. Banister and J. R. Zeidler, “A simple gradient sign algorithm for transmit antenna weight adaptation with feedback,” IEEE Transactions on Signal Processing, vol. 51, no. 5, pp. 1156-1171, May 2003.[27] R. Mudumbai, J. Hespanha, U. Madhow, and G. Barriac, “Distributed transmit beamforming using feedback control,” IEEE Transactions on Information Theory, vol. 56, no. 1, pp. 411-426, Jan. 2010.


Globecom 2016 101

References

[28] Y. Zeng and R. Zhang, “Optimized training design for wireless energy transfer,” IEEE Transactions on Communications, vol. 63, no. 2, pp. 536-550, Feb., 2015.[29] L. Liu, R. Zhang, and K. C. Chua, “Multi-antenna wireless powered communication with energy beamforming,” IEEE Transactions on Communications, vol. 62, no. 12, pp. 4349-4361, Dec., 2014.[30] H. Ju and R. Zhang, “Optimal resource allocation in full-duplex wireless powered communication network,” IEEE Transactions on Communications, vol. 62, no. 10, pp. 3528-3540, Oct., 2014.[31] H. Ju and R. Zhang, “A novel model switching scheme utilizing random beamforming for opportunistic energy harvesting,” IEEE Transactions on Wireless Communication, vol. 13, no. 4, pp. 2150-2162, Apr. 2014.[32] L. Liu, R. Zhang, and K. C. Chua, “Secrecy wireless information and power transfer with MISO beamforming,” IEEE Transactions on Signal Processing, vol. 62, no. 7, pp. 1850-1863, Apr. 2014.[33] P. Popovski, A. M. Fouladgar, and O. Simeone, “Interactive joint transfer of energy and information,” IEEE Transactions on Communications, vol. 61, no. 5, pp. 2086–2097, May 2013.[35] Z. Xiang, and M. Tao, “Robust beamforming for wireless information and power transmission,” IEEE Wireless Communication Letters, vol. 1, no. 4, pp. 372-375, Aug. 2012.[35] A. M. Fouladgar and O. Simeone, “On the transfer of information and energy in multi-user systems,” IEEE Wireless Communication Letters, vol. 16, no. 11, pp. 1733-1736, Nov. 2012.[36] B. Gurakan, O. Ozel, J. Yang, and S. Ulukus, “Energy cooperation in energy harvesting wireless systems,” in Proc. IEEE ISIT, pp. 965-969, 2012.[37] B. K. Chalise, Y. D. Zhang, and M. G. Amin, “Energy harvesting in an OSTBC based amplify-and-forward MIMO relay system,” in Proc. IEEE ICASSP, 2012.


Globecom 2016 102

References

[38] X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “Wireless Networks With RF Energy Harvesting: A Contemporary Survey,” IEEE Communications Surveys and Tutorials, vol. 17, no. 2, pp.757-789, 2015.[39] S. Bi, C. K. Ho, and R. Zhang, “Wireless powered communication: opportunities and challenges,”IEEE Communications Magazine, vol. 53, no. 4, pp. 117-125, April 2015.[40] I. Krikidis, S. Timotheou, S. Nikolaou, G. Zheng, D. W. K. Ng, and R. Schober, “Simultaneous wirelessinformation and power transfer in modern communication systems”, IEEE Communications Magazine ,vol. 52, no. 11, pp. 104-110, Nov. 2014.[41] S. Bi, Y. Zeng, and R. Zhang, “Wireless powered communication networks: an overview,” IEEE Wireless Commun., vol. 23, no. 2, pp. 10–18, Apr. 2016.[42] T. Yoo and K. Chang, “Theoretical and experimental develop ment of 10 and 35 GHz rectennas,” IEEE Trans. Microwave Theory Tech., vol. 40, no. 6, p. 8, June 1992 .[43] A. Boaventura, A. Collado, N. B. Carvalho, and A. Georgiadis, “Optimum behavior: Wireless powertransmission system design through behavioral models and efficient synthesis techniques”, IEEEMicrowave Magazine, vol. 14, no. 2, pp. 26-35, Mar., 2013.[44] A. Massa, G. Oliveri, F. Viani, and P. Rocca, “Array Designs for Long-Distance Wireless PowerTransmission: State-of-the-Art and Innovative Solutions,” Proceedings of IEEE, vol. 101, no. 6, pp.1464-1481, Jun. 2013.[45] C. Zhong, H. A. Suraweera, G. Zheng, I. Krikidis, and Z. Zhang, “Wireless information and power transfer with full duplex relaying,” IEEE Trans. Commun. , vol. 62, no. 10, pp. 3447–3461, Oct. 2014.[46] Y. Zeng and R. Zhang, “Full-duplex wireless-powered relay with self-energy recycling,” IEEE Wireless Communications Letters, vol. 4, no. 2, pp. 201-204, Apr., 2015.[47] B. Clerckx and E. Bayguzina, “Waveform design for wireless power transfer,” IEEE Trans. Signal Process., vol. 64, no. 23, pp. 6313–6328, Dec. 2016.


Globecom 2016 103

References Rui Zhang, National University of Singapore

[48] E. Boshkovska, D. W. K. Ng, N. Zlatanov, and R. Schober, “Practical non-linear energy harvesting model and resource allocation for SWIPT systems,” IEEE Commun. Letters, vol. 19, no. 12, pp. 2082–2085, Dec. 2015.[49] Y. Zeng and R. Zhang, “Optimized training for net energy maximization in multi-antenna wireless energy transfer over frequency-selective channel,” IEEE Trans. Commun., vol. 63, no. 6, pp. 2360–2373, Jun. 2015.[50] X. Wang, S. Sha, J. He, L. Guo, and M. Lu, “Wireless power delivery to low-power mobile devices based on retro-reflective beamforming,” IEEE Antennas and Wireless Propag. Letters, vol. 13, pp. 919–922, May 2014.[51] S. Lee and R. Zhang, “Distributed wireless power transfer with energy feedback,” submitted to IEEE Trans. Signal Process., available online at https://arxiv.org/abs/1606.07232.[52] G. Yang, C. K. Ho, R. Zhang, and Y. L. Guan, “Throughput optimization for massive MIMO systems powered by wireless energy transfer,” IEEE J. Sel. Areas Commun., vol. 33, no. 8, pp. 1640–1650, Aug. 2015.[53] S. Kashyap, E. Bjornson, and E. G. Larsson, “On the feasibility of wireless energy transfer using massive antenna arrays,” IEEE Trans. Wireless Commun., vol. 15, no. 5, pp. 3466–3480, May 2016.[54] T. A. Khan, A. Alkhateeb, and R. W. Heath Jr, “Millimeter wave energy harvesting,” IEEE Trans. Wireless Commun., vol. 15, no. 9, pp. 6048– 6062, Sep. 2016.[55] S. Lee and R. Zhang, “Cognitive wireless powered network: spectrum sharing models and throughput maximization,” IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 3, pp. 335-346, Sep. 2015. [56] S. Bi and R. Zhang, “Distributed charging control in broadband wireless power transfer networks,” IEEE Journal on Selected Areas in Communications, Dec. 2016.

wireless powered communication: from theory to applications · wireless powered communication: from...

Documents