Wireless Powered Communication: Challenges and Solutions · 2013. 7. 9. · Part II – Wireless Powered Communication: Challenges & Solutions Part III – Extended Work and Concluding

NUS Presentation Title 2001

Wireless Powered Communication: Challenges and Solutions

Rui Zhang Department of Electrical and Computer Engineering

National University of Singapore

MIIS 2013 at Xi’an, China

June 30, 2013


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Technology Trend: Integrated Communication and Energy Research

Power-line Communication Smart Grid

Green Communication Energy Harvesting


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Radio Signal Enabled Wireless Power Transfer

Energy Transmitter Energy Receiver

Wireless Energy Receiver Architecture

Why RF-based Far-Field Wireless Power Transfer (WPT)? longer distance than near-/mid-field WPT (e.g., RFID) advantages over traditional energy sources

low cost: no need to replace/dispose batteries safer: in e.g. toxic environment robust: overcome lack of sunlight/wind etc. (conventional energy harvesting) convenient: controllable, scheduled on demand

abundant applications sensor networks: building automation, healthcare, smart grid, structural monitoring….. consumer electronics: mice/keyboards, headsets, remote controllers, low-power displays…. functionality-enhanced RFID


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Wireless Powered Sensor Network [XSHS]

A wireless charging vehicle (WCV) travels inside the sensor network to charge the sensor nodes wirelessly

Making a sensor network powered forever Wireless power transfer only, how about information collection

at the same time?

A sensor network powered by WCV. A WCV visits every sensor node to charge it.


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Beyond Sensor Network: Dual Wireless Information/Energy Access Network [ZH]

Hybrid Access Point (H-AP)

Dual use of (the same) wireless signal for simultaneous wireless information and power transfer (SWIPT): Exploit the “broadcast” nature of wireless channels Make more convenience to users (get mobile devices charged anytime anywhere without any wire)

Power Flow

Information Flow

power charging

video downloading


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Agenda

Part I – Wireless Power: History & State of the Art Part II – Wireless Powered Communication: Challenges & Solutions

Part III – Extended Work and Concluding Remarks


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Part I Wireless Power: History & State of the Art


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Microwave Enabled Wireless Power Transfer: Nikola Tesla and his Wardenclyffe Project in early 1900


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Solar Satellite with Microwave Power Transmission


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NASA’s Wireless Power Transfer Project Using Laser Beam


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Induction Coupling Enabled Wireless Power Transfer: Radio Frequency Identification (RFID)


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Wireless Power Transfer via Magnetic Resonant Coupling


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Wireless RF Power Transfer via Electromagnetic Radiation


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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


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Summary of Performance

Strength Efficiency Distance Multicast Mobility

Inductive Coupling

Very High

Very High Very Short

Yes No

Magnetic Resonant Coupling

High High Short Difficult No

EM Radiation

Low Low Long Yes Yes


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Application Example [XSHL]


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Part II Wireless Powered Communication: Challenges &

Solutions


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Outline

Overview of Challenges and Solutions Downlink Simultaneous Wireless Information and Power Transfer (SWIPT) Receiver Architecture and Rate-Energy Tradeoff Joint Energy/Information Beamforming Joint Energy/Communication Scheduling

Uplink Wireless Powered Communication

Throughput Maximization in Wireless Powered TDMA Network


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Wireless (RF) Powered Communication Network: A General Model [ZH]

H-AP w/ constant

power supply

Energy Flow

Information Flow

RF Powered Wireless Network Downlink (Access Point → Wireless Terminals ) Uplink (Wireless Terminals → Access Point)

“Asymmetric” information/energy flow Need joint energy and communication scheduling and resource allocation

Wireless information and power transfer (DL) orthogonal vs. simultaneous information and energy transmissions

Wireless Stations (each w/ a rechargeable battery)

Information transfer with wireless harvested energy (UL) performance tradeoff between DL (energy + information) vs. UL (information)


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Fundamental Difference (1)

Wired vs. Wireless Information and Power Transfer Wired (e.g. power-line communication) Frequency duplex: low-freq. for power transfer, high-freq. for information transfer Minimal cross-talk between information/energy transmissions and among different wires/lines

Wireless Limited bandwidth: thus suboptimal with orthogonal (over time or freq.) information and energy transmissions in general Co-channel interference present due to wireless broadcast nature


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Ambient RF Energy Harvesting vs. Wireless Power Transfer Ambient RF Energy Harvesting Opportunistic energy harvesting from ambient transmitting radios: intermittent and random energy arrival Design challenge: how to achieve reliable communication with transmit energy drawn from time-varying sources [HZ], [OTYUY]

Wireless Power Transfer Intentional energy transmitter: continuous and reliable power transfer Design challenge: how to maximize end-to-end energy transfer efficiency with given transmitter power budget


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Wireless Power Transfer vs. Wireless Information Transfer Wireless Power Transfer Energy (in Joule) is linearly proportional to both time and power

Wireless Information Transfer Information quantity (in bits) increases linearly with time, but logarithmically with power

E =T × P Vs.

C = T × log(1+P)


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Challenge One: High-Efficiency Wireless Power Transfer via Energy Beamforming [ZH]

Energy Receivers Energy

Transmitter

Energy Beamforming

Pr =Pt ×Ga×(D-α)×η

Path Loss Antenna Gain

Ga ≈ (# of Tx antennas)×(# of Rx antennas) e.g.: 2×1 (3dB gain), 4×1 (6dB gain)…

Diversity gain (additional) Power multicast (via multiple beams)

Jointly with information beamforming (to be given later)

More efficiently implemented via distributed antennas: collaborative energy beamforming

Energy Conversion Efficiency: 30%-70%


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Challenge Two: Optimal Receiver Architecture for SWIPT

Integrated information and energy receivers Unavailable yet since practical energy harvesting circuits cannot decode information directly, and vice versa (more to be given later)

Separated information and energy receivers Use “time switching” or “power splitting” [ZH] Pros: easy to implement, off-the-shelf hardware available Cons: may not be optimal in terms of spectral/energy efficiency


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Challenge Three: Receiver Sensitivity

Hybrid Access Point (H-AP)

The receiver “sensitivity” issue (different receiver operating power) wireless information receiver: > -60dBm wireless energy receiver: > -10dBm

Solution 1: Dynamic power splitting [ZZH-b], [LZC-b] Need digitally controllable RF power splitter

Solution 2: Near-Far based transmission scheduling [ZH], [XLZ] Harvest energy when user is close to H-AP Receive information when user is far from H-AP

Power Flow

Information Flow

“near” user: power charging

“far” user: video downloading


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Challenge Four: Rate-Energy Tradeoff

Energy (Joule)

Rate (bits/sec/Hz)

?

Time Switching

Power Splitting

Rate-Energy Region [V] Integrated Receiver (Upper Bound)


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Challenge Five: Harmful vs. Helpful Interference Co-Channel Interference Harmful to wireless information transmission (treated as noise if not decodable at receiver) Helpful to wireless energy transmission (additional source of energy harvesting at receiver) Interference management in SWIPT: a new open challenge (more to be given later)


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Challenge Six: “Doubly” Near-Far Problem

Uplink wireless information transmission (WIT) powered by downlink wireless power transfer (WPT) Doubly Near-Far Problem [JZ-b] 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 Unbalanced energy consumptions in the network: need more careful resource allocation (more to be given later)


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SWIPT: Receiver Architecture and Rate-Energy Tradeoff

[ZZH-a]


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Channel Model


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Information Receiver


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Energy Receiver


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Performance Upper Bound


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Separated Information and Energy Receivers: General Model

Received signal split at RF band (point A) Power splitting ratio can be set different over time: Dynamic Power Splitting (DPS)


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Dynamic Power Splitting


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Rate-Energy Region of Separated Receivers


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Numerical Example (Separated Receivers)


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Integrated Information and Energy Receivers: Proposed Model


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Rate-Energy Region of Integrated Receivers (1)


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Rate-Energy Region of Integrated Receivers (2)


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Numerical Example (Separated vs. Integrated Receivers)


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Rate-Energy Region with Receiver Circuit Power Consideration


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Numerical Example (with Receiver Circuit Power Consideration)


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Practical Example


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SWIPT: Joint Energy/Information Beamforming

[ZH], [XLZ]


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MIMO SWIPT System (K=2)

G

H

A three-node MIMO broadcast system with perfect CSIT/CSIR

Two scenarios: separated receivers: G ≠ H co-located receivers: G = H

Objective: Find the optimal “rate-energy” trade-offs

Optimization problem (convex):

generalized linear transmit power constraint


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Separated Receivers (G ≠ H) • Semi-closed-form optimal solution:

(1)


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Rate-Energy Region of Separated Receivers

energy beamforming

spatial multiplexing


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Co-Located Receivers (G=H)

• Special case: H is circulant (OFDM channel) [GS] • Provide R-E outer bound only, due to practical receiver circuit constraint

• Optimal solution in (1) simplified as


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Co-Located Receivers: Practical Schemes

Power Splitting

Two Special Cases: Uniform Power Splitting: On-Off Power Splitting (Antenna Switching):

Time Switching


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Rate-Energy Region of Co-Located Receivers

0 2 4 6 8 10 120

50

100

150

200

250

Rate (bits/channel use)

Ene

rgy

Uni

t

Outer BoundTime Switching Uniform Power Splitting Antenna Switching


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MISO SWIPT System (K>2)

MISO broadcast system for SWIPT – Exploiting Near-Far channel conditions “Near” users are scheduled for energy harvesting (EH), while “Far” users are scheduled for information decoding (ID)


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Information and Energy Signals

The AP sends both information and energy signals at the same time with properly designed beamforming weights and power control Balance the trade-offs between information and energy broadcasting


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Two Types of Information Receivers

Type I ID receivers: Do not possess the capability of cancelling the interference from energy signals

Type II ID receivers: Possess the capability of cancelling the interference from energy signals

Received signals at ID receivers: Interference from energy beams

Energy beams carry no information but only pseudorandom signals


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Energy Receivers

Harvested energy at EH receivers


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Problem Formulation Energy weight

Type I ID receivers: Type II ID receivers:

Challenge: Both problems (P1) and (P2) maximize a convex quadratic function; thus they are in general non-convex quadratically constrained quadratic programs (QCQPs), for which the globally optimal solutions are difficult to be found in general.

Objective: Maximize the weighted sum-power transferred to all EH receivers Subject to individual SINR constraints at ID receivers,


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Optimal Solution for (P1) by Semi-definite Relaxation (SDR) (1)

reformulation

Semi-definite Programming (SDP)

with rank constraints: non-convex!


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Optimal Solution for (P1) by SDR (2)

Dedicated energy beamforming is not needed for Type I ID receivers

Dropping rank

constraints

Standard SDP, solvable by e.g. CVX


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reformulation

SDP with rank constraints: non-convex!


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Sending no more than one dedicated energy beam aligning with vE is optimal for Type II ID receivers

Dropping rank

constraints

Standard SDP, solvable by e.g. CVX


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Numerical Example


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Other extensions on MISO SWIPT

MISO SWIPT with Imperfect CSIT [XT] Robust energy/information beamforming

MISO SWIPT with Dynamic Power Splitting Receiver [S] Non-convex problem, approximate solution obtained by semi-definite relaxation (SDR)

MISO SWIPT without CSIT [JZ-a] Random information beamforming for opportunistic energy harvesting via threshold-based mode switching (TS): artificial channel fading improves the R-E region over static channel with periodic mode switching (PS)


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Joint Energy/Communication Scheduling

[LZC-a],[LZC-b]


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Dynamic Time Switching: Exploiting Channel/Interference Variations

Receiver Mode Switching (assume perfect CSIR) Information Decoding: when h is good and I is weak Energy Harvesting: when h is poor and I is strong (helpful interference) Optimal mode switching rule with random (h, I)? should be consistent with receiver sensitivity difference

With vs. Without CSIT?


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Case without CSIT


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Case with CSIT


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No-Delay-Limited Information Transmission


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Rate-Energy Region

Similar results obtained for the case with delay-limited information transmission (outage capacity): “Outage-Energy” Region [LZC-a]


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Example of Rate-Energy Region


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Problem Formulation

Non-convex problems: but strong duality holds, thus solvable by Lagrange dual decomposition


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Optimal Switching Rule (without CSIT)

Consistent with ID and EH receivers’ different sensitivity


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Optimal Switching Rule (with CSIT)


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Rate-Energy Region


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Example of Rate-Energy Region: DPS vs. TS


Optimal Decision Rule (without CSIT)

Power Splitting

Time Switching

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Optimal Decision Rule (with CSIT)

Power Splitting

Time Switching

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Similar to the case without CSIT, both information decoding and energy harvesting can benefit from good fading states with dynamic power splitting


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Antenna Switching for SIMO SWIPT System


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Performance Comparison


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Uplink Wireless Powered Communication

[JZ-b]


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Wireless Powered TDMA Network

user wireless network

Uplink WIT powered by downlink WPT

Harvest-then-Transmit Protocol TDD-TDMA protocol for wireless powered network Open challenge: optimal resource/rate allocation


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Harvest-then-Transmit Protocol

WPT in downlink Energy harvested by each user:

WIT in uplink Transmit power at :

Achievable throughput at

( : portion of harvested energy used for uplink WIT)

( : energy harvesting efficiency)

( )


Problem formulation

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Sum-Throughput Maximization

Convex optimization since is concave function of

Optimal solution:

where and is the solution of is a monotonically decreasing function of


Optimal time allocation solution

, i.e.,

Doubly near-far problem due to distance-dependent signal attenuation

“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

Severely unfair time and thus rate allocation among users 85

Doubly Near-Far Problem (1)

•

•

•


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Doubly Near-Far Problem (2)

• Wireless powered network:

• Conventional TDMA network: , and since no DL WPT is needed

Wireless powered network vs. conventional network (with constant power)

Wireless powered network suffers from more severe near-far problem

Rate ratio of two users in wireless powered network decreases twice faster than that in conventional TDMA in logarithm scale due to doubly near-far problem


To circumvent doubly near-far problem

Guarantees an equal rate to all users (P2) can be iteratively solved via bisection search on (feasibility problem), together with weighted sum-rate (WSR) maximization problem

87

Common-Throughput Maximization (1)

• Convex, but difficult to obtain closed-form solution

• Optimal with

Feasibility problem WSR maximization problem


Feasibility certificate of (P2) from feasibility problem

Optimal time allocation for WSR maximization problem

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the feasibility problem is infeasible if and only if there exists an such that , where

is the dual function of the feasibility problem.

the optimal time allocation solution, denoted by , is

where is the solution of the following equations:

with being a constant.


Algorithm to solve (P2)

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Initialize and

optimal time allocation solution for WSR maximization problem

Given compute using

ellipsoid method Update using

Is stopping criteria met?

Yes

No

Yes

No

Yes No


The time portion allocated to the near user, , is decreased substantially, while that to the far user, , is greatly increased

The time ratio of (P1) in the logarithm scale decreases linearly with to maximize sum-throughput, while that of (P2) increases with since increases with

90



Throughput of near user dominates over that of far user in maximum sum-throughput

Due to the doubly near-far problem Maximum common throughput is smaller than normalized maximum sum-throughput

Cost to pay in order to ensure strictly fair rate allocation regardless of user distance

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Simulation Result

• • • • : • : • : •


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Part III Extensions and Concluding Remarks


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SWIPT in Multi-User OFDM [HL-b], [NLS], [ZZH-b]

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

ρj,n= ρj,∀n, ∀j TDMA with TS: Each user performs ID when information is scheduled for

that user, and performs EH in all other time slots Which one is better in R-E trade-off ? [ZZH-b]


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Multi-Transmitter Collaborative SWIPT

An 2×2 IC for SWIPT with TS receivers

Receivers: Ideal Receiver: each receiver can do EH and ID simultaneously Practical Receiver: [SLC], [PC]: receivers use time switching from four modes

(EH,EH), (EH,ID), (ID,EH),(ID,ID) [TKO]: receivers use power splitting

Transmitters: collaborative information and/or energy transmissions


95

SWIPT with Energy/Information Relaying [CZA], [FO], [KTS], [NZDK], [MS], [GOYU]

Source Relay Destination h g

S R Information Transmission

S R Energy Transmission

R D Information Transmission

T

αT (1-α)T/2 (1-α)T/2

S R Energy Transmission (1-ρ)P

S R Information Transmission ρP

R D Information Transmission

T

T/2 T/2

TS Relay

PS Relay

P R O T O C O L

information transmission energy transmission

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


96

Wireless Powered Network Capacity [HL-a]

(1) Cellular Network (2) Cognitive Radio Network

Architecture: ST can harvest energy from any nearby

PT if it is in the PT’s harvesting zone ST cannot transmit if it is in the guard

zone of any PT Objective: maximize the secondary network

throughput under outage probability constraints

Hybrid networks: cellular network + power beacons to power mobiles

Parameters: p ,q: transmit power of BSs and PBs λb, λp: density of PPP of BSs and PBs

Objective: optimize (p, q, λb, λp) to guarantee the outage probability of information and power transfer

[LZH]


97

Secrecy Communication in SWIPT [LZC-c], [NS]

information signal energy signal artificial noise

ERs may eavesdrop the information intended to IR: More challenging than conventional secrecy communication since

gk » h (due to near-far transmission scheduling)

transmitted signal:

A SWIPT system with K ERs and one single IR

Eve?

secrecy communication


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Concluding Remarks

Wireless RF Powered Communication Networks fundamental limits: still open many new design challenges in PHY, MAC, and Network layers

Hardware Development wireless power transfer (e.g., energy beamforming, high-efficiency rectenna,…) practical receivers for SWIPT (e.g., time switching, power splitting, antenna switching, integrated receiver,…)

Applications wireless sensor networks cellular networks (small cells? millimeter-wave?) …


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Acknowledgement

Young Investigator Award, National University of Singapore Postdoctoral Fellows Dr Jie Xu Dr Hyungsik Ju

Ph.D. Students Liang Liu Xun Zhou


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References (1) [V] L. R. Varshney, “Transporting information and energy simultaneously,” IEEE ISIT, pp. 1612–1616, 2008. [GS] P. Grover and A. Sahai, “Shannon meets Tesla: wireless information and power transfer,” IEEE ISIT, pp. 2363–2367, 2010. [HZ] C. K. Ho and R. Zhang, “Optimal energy allocation for wireless communications with energy harvesting constraints,” IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4808-4818, Sep. 2012. [OTYUY] O. Ozel, K. Tutuncuoglu, J. Yang, S. Ulukus, and A. Yener, “Transmission with energy harvesting nodes in fading wireless channels: Optimal policies,” IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1732–1743, Sep. 2011. [ZH] 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. [ZZH-a] X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer: architecture design and rate-energy tradeoff,” submitted to IEEE Transactions on Communications. (available on-line at arXiv:1205.0618) [ZZH-b] X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer in multiuser OMDM systems,” accepted in IEEE Global Communications Conference (Globecom), 2013. [LZC-a] 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, January 2013.

http://arxiv.org/abs/1205.0618�






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References (2) [LZC-b] L. Liu, R. Zhang, and K. C. Chua, “Wireless information and power transfer: a dynamic power splitting approach,” accepted in IEEE Transactions on Communications.(available on-line at arXiv:1302.0585) [LZC-c] L. Liu, R. Zhang, and K. C. Chua, “Secrecy wireless information and power transfer with MISO beamforming,” accepted in IEEE Global Communications Conference (Globecom), 2013. (available on-line at arXiv:1306.0969) [XLZ] J. Xu, L. Liu, and R. Zhang, “Multiuser MISO beamforming for simultaneous wireless information and power transfer,” IEEE ICASSP, 2013. (available on-line at arXiv:1303.1911). [JZ-a] H. Ju and R. Zhang, “A novel model switching scheme utilizing random beamforming for opportunistic energy harvesting,” submitted to IEEE Transactions on Wireless Communications. (available on-line at arXiv:1301.4798) [JZ-b] H. Ju and R. Zhang, “Throughput maximization in wireless powered communication networks,” submitted to IEEE Transactions on Wireless Communications. (available on-line at arXiv:1301.4798) [LZH] S. Lee, R. Zhang, and K. B. Huang, “Opportunistic wireless energy harvesting in cognitive radio networks,” accepted in IEEE Transactions on Wireless Communications.(available on-line at arXiv:1302.4793) [XSHL] L. Xie, Y. Shi, Y. T. Hou, and W. Lou, “Wireless power transfer and applications to sensor networks,” IEEE Wireless Communications, to appear. [XSHS] L. Xie, Y. Shi, Y. T. Hou, and H. D. Sherali, “Making sensor networks immortal: an energy-renewable approach with wireless power transfer,” to appear in IEEE/ACM Trans. Netw.
























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References (3) [XT] 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. [CZA] B. K. Chalise, Y. D. Zhang, and M. G. Amin, “Energy harvesting in an OSTBC based amplify-and-forward MIMO relay system,” IEEE ICASSP, 2012. [FS] 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. [HL-a] K. Huang and V. K. N. Lau, “Enabling wireless power transfer in cellular networks: architecture, modeling and deployment,” submitted to IEEE Transactions on Wireless Communications. (available on-line at arXiv:1207.5640) [KTS] 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. [NZDK] A. A. Nasir, X. Zhou, S. Durrani, and R. A. Kennedy, “Relaying protocols for wireless energy harvesting and information processing,” submitted to IEEE Transactions on Wireless Communications. (available on-line at arXiv:1212.5406) [HL-b] K. Huang, and E. G. Larsson, “Simultaneous information and power transfer for broadband wireless systems,” submitted to IEEE Transactions on Signal Processing. (available on-line at arXiv:1211.6868)

















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References (4) [NLS] D. W. K. Ng, E. S. Lo, and R. Schober, “Wireless information and power transfer: energy efficiency optimization in OFDMA systems,” submitted to IEEE Transactions on Wireless Communications. (available on-line at arXiv:1303.4006) [MSS] D. S. Michalopoulos, H. A. Suraweera, and R. Schober, “Relay selection for simultaneous information transmission and wireless energy transfer: a trade-off perspective,” submitted to IEEE Transactions on Wireless Communications. (available on-line at arXiv:1303.1647) [TKO] S. Timotheou, I. Krikidis, and B. Ottersten, “MISO interference channel with QoS and RF energy harvesting constraints,” IEEE ICC, 2013. [S] Q. Shi, “Joint beamforming and power splitting for multi-user MISO SWIPT system,” available on-line at arXiv:1304.0062 [SLC] C. Shen, W. C. Li, and T. H. Chang, “Simultaneous information and energy transfer: a two-user MISO interference channel case,” IEEE Globecom, 2012. [PC] J. Park and B. Clerckx, “Joint wireless informatio and energy transfer in a two-user MIMO interference channel,” submitted to IEEE Transactions on Wireless Communications. (available on-line at arXiv:1303.1693) [NS] D. W. K. Ng and R. Schober, “Resource allocation for secure communication in systems with wireless information and power transfer,” available on-line at arXiv:1306.0712 [GOYU] B. Gurakan, O. Ozel, J. Yang, and S. Ulukus, “Energy cooperation in energy harvesting wireless systems,” IEEE ISIT, pp. 965-969, 2012.



























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Wireless Powered Communication: Challenges and Solutions · 2013. 7. 9. · Part II – Wireless Powered Communication: Challenges & Solutions Part III – Extended Work and Concluding