reconfigurable intelligent surface: energy …yuenchau/6gworkshop/chau yuen...2018, abu dhabi, uae)...

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Reconfigurable Intelligent Surface: Energy Efficiency andIntelligent Configuration in Wireless Communication

Chongwen Huang1, Alessio Zappone3, George C. Alexandropoulos2,Merouane Debbah2,3, and Chau Yuen1

1Singapore University of Technology and Design, Singapore2Mathematical and Algorithmic Sciences Lab,

Huawei Technologies France SASU3CentraleSupelec, Universite Paris-Saclay, France

IEEE VTS and Comsoc Joint Workshop on 6G

SUTD, Apr. 24, 2019

C. Huang, et al. () Communication via Intelligent Surfaces SUTD, Apr. 24, 2019 1 / 34

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Outline

...1 Introduction & Background

Introduction

Background

Application Scenarios

...2 Recent Research Results

Reconfigurable Intelligent Surfaces (RIS) for Energy Efficiency inWireless Networks (TWC Under third reviewing)

Achievable Rate Maximization By Passive Intelligent Mirrors (ICASSP,Apr. 2018, Calgary, Alberta, Canada)

Low Resolution Reconfigurable Intelligent Surfaces (Goblecom Dec.2018, Abu Dhabi, UAE)


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Outline


Introduction

Background



Reconfigurable Intelligent Surfaces (RIS) for Energy Efficiency inWireless Networks (TWC Under third reviewing)




. . . . . .

Introduction & Background

5G Use Cases


. . . . . .


A Vision of Beyond 5G Communication

Figure: Source: A Speculative Study on 6G, https://arxiv.org/abs/1902.06700.


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Concerns for Beyond 5G (Post-5G and 6G) Communication

Tbps throughput per connection.

Centimeter level localization.

Latency in the order of nanoseconds.

Increased Energy Efficiency (EE).

Intelligent algorithms in all network layers: learn, predict, and adapt.


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.What’s Reconfigurable Intelligent Surfaces (RIS)?..

......

RIS: Man-made surface composed of many small-unit reflectors equipped withsimple low-cost sensors and a cognitive enginea b.

Capability of soft controlling each of the units, which can effectively produce moreflexible manipulation of impinging electromagnetic field, such as polarization,scattering, focusing reflection control, absorbing, etc . c.

aHu et al., “The potential of using large antenna arrays on intelligent surfaces,”in 2017 IEEE 85th VTC

Spring, June 2017, pp. 1–6.bHum et al., “Reconfigurable reflect arrays and array lenses for dynamic antenna beam Control: A review,”

IEEE TAP, 2014.cH. Yang, et al., “A programmable metasurface with dynamic polarization, scattering and focusing control”.

Scientific reports, 6, 35692, 2016.


. . . . . .


1

Figure: The programming and control of impinging electromagnetic field, such aspolarization, scattering control, focusing reflection control.

1H. Yang, et al., “A programmable metasurface with dynamic polarization, scattering and focusing control”.

Scientific reports, 6, 35692, 2016.


. . . . . .

Background 2 (1/2)

Currently, Mostimplementation by 2Dmaterials (Meta-surface)

Example: 0.4m2 and 1.5mmthickness surface consisting of102 controllable cells andoperating at 2.47GHz.

Each unit cell is a rectangularpatch sitting on a ground planeand offering binary phasemodulation.

2Kaina et al., ‘Shaping complex microwave fields in reverberating media with binary tunable metasurfaces,”

Scientific Reports, 2014.


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Background (2/2)

.Key RIS Features..

......

...1 Passive (nearly): No need any dedicated energy source

...2 Can be contiguous surface: Ideally, each point can reflect

...3 No receiver noise: due to no ADCs/DACs and Amplifier

...4 Soft programming: Ideally, each point of surface can be reconfigured

...5 Full-band response (Ideally): due to the reconfigurable; can work at anyoperating frequency wave

...6 Easily deployment: buildings facades,factory ceilings, human clothing, etc.


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Application Scenarios-Outdoor

Figure: Outdoor wireless network operation (a) Current network (b) Smart network(O1, O2 and O3 coated with RIS).

Notes: The objects O1, O2, O3 are now coated with reconfigurablemeta-surfaces that modify the radio waves according to the generalizedlaws of reflection and refraction.


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Application Scenarios-Indoor

Figure: (a) Without RIS Wall, (b) With RIS Wall

Notes: (a) the signal experiences path loss and multi-path fading andhardly reaches the target user. Whereas in (b), the signal propagation canbe reconfigured by RIS coated in the surfaces so that the signal is directedtowards the target user.


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Outline


Introduction

Background



Reconfigurable Intelligent Surfaces for Energy Efficiency in WirelessNetworks (TWC Under third reviewing) 3



3Chongwen Huang, Alessio Zappone, George C. Alexandropoulos, Merouane Debbah and Chau Yuen,

“Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Networks” Submitted to the IEEETransactions on Wireless Communications


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System Model

Large Intelligent Surface (LIS)

Base station

User

User

User

Reflecting Element

ͳ

ܭ

Figure: The considered RIS-basedmulti-user MISO system comprising ofa M-antenna base station serving in thedownlink K single-antenna users. RIS isassumed to be attached to asurrounding building’s facade.

The received signal at the k-thmobile user

yk = h2,kΦH1x+ nk ,

where:

h2,k ∈ C1×N represents thechannel between RIS and thek-th mobile user.H1 ∈ CN×M for the channelbetween BS and RIS.Φ , diag[ϕ1, ϕ2, . . . , ϕN ] is adiagonal matrix including theeffective phase shiftingvalues ϕn ∀n = 1, 2, . . . ,Nfor all RIS elements.


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Problem Formulation.Objective..

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Jointly optimal design of the RIS phase shifting matrix and the transmitpower allocation matrix by maximizing the EE under QoS constraints.

.Problem Formulation..

......

maxΦ,P

∑Kk=1 log2

(1 + pkσ

−2)

ξ∑K

k=1 pk + PBS + KPUE + NPn(b)(1a)

s.t. log2(1 + pkσ

−2)≥ Rmin,k ∀k = 1, 2, . . . ,K , (1b)

tr((H2ΦH1)+P(H2ΦH1)

+H) ≤ Pmax, (1c)

|ϕn| = 1 ∀n = 1, 2, . . . ,N, (1d)

Problem (4) is non-convex; and

Optimizing Φ is challenging due to the its discrete nature.


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Proposed Solution (1/3)

Alternating optimization:

Optimization with respect to Φ

minΦ


+H) (2a)

s.t. |ϕn| = 1 ∀n = 1, 2, . . . ,N (2b)

Using Sequential Fractional Programming, (4) will becomes

maxy

2Re(yH(λmaxIN2 − A)y(t)) (3a)

s.t. |yi | = 1 , ∀i = (j − 1)N + j , j = 1, 2, . . . ,N, (3b)

yi = 0 , ∀i = (j − 1)N + j , j = 1, 2, . . . ,N. (3c)

where y = vec(Φ−1) , A , (H+H2 ⊗H+

1 )H(H

+H2 ⊗H+

1 ) ∈ CN2×N2.


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Proposed Solution (2/3)Alternating optimization:

Optimization with respect to Φ

minΦ


+H) (4a)

s.t. |ϕn| = 1 ∀n = 1, 2, . . . ,N (4b)

Using Gradient Descent Approach, (4) will becomes

vec(Θ)(t+1) = vec(Θ)(t) + µd(t), (5)

y(t+1) = e jvec(Θ)(t+1) ◦ vec(IN) = y(t) ◦ e jµd(t) , (6)

d(t+1) = −q(t+1) +(q(t+1) − q(t))Tq(t+1)

∥q(t)∥2d(t), (7)

q(t) , ∇Θ

((y(t))HAy(t)

).


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Proposed Solution (3/3)

Alternating optimization:

Optimization with respect to P

maxP

∑Kk=1 log2

(1 + pkσ

−2)

ξ∑K

k=1 pk + PBS + KPUE + NPn(b)(8a)

s.t. pk ≥ σ2(2Rmin,k − 1), ∀k = 1, 2, . . . ,K , (8b)


+H) ≤ Pmax. (8c)

For fixed Φ, the numerator of (13a) is concave in P, globally solvedwith limited complexity using Dinkelbach’s algorithm.


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Simulation Results

Figure: The simulated RIS-based K -user MISO communication scenariocomprising of a M-antenna base station and a N-element intelligent surface.


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Simulation Results

Figure: Average EE using either RIS orAF relay versus Pmax forRmin = 0bps/Hz.

Particularly, the EE of theRIS-based system is 300%larger than that of the onebased on the AF relay.


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Simulation Results

-20 -10 0 10 20 30 400

50

100

150

Figure: Average EE using RIS versusPmax for M = 32, K = 16, and N = 16using: a) EE maximization forRmin = {0, 0.2R}; b) full powerallocation; and c)sum ratemaximization.

all designs perform similarly forPmax ≤ 15dBm, whichindicates that the EE and SEobjectives are nearly equivalentfor such transmit power levels.

using full BS transmit powerfor low Pmax is optimal.

for Pmax > 15dBm, the SEmaximization design naturallyincreases the SE, but leads todecreasing EE.

EE is maximized subject toQoS,the excess transmit poweris used in order to fulfill thethose constraints, but reducethe EE.


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Simulation Results

Figure: Average sum rate using RISversus N for SNR = 20dB, M = 64,K = 64 and Rmin = 2bps/Hz with bothour presented algorithms as well asexhaustive global optimization.

both proposed algorithms yieldvery similar performance curvesthat are quite close to the onesobtained from the globaloptimization.

larger the N value is, the largeris the achievable SE.


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Simulation Results

0 10 20 30 40 50100

110

120

130

140

150

160

170

Figure: Average EE using RIS versus Nfor SNR = −10dB andRmin = 0bps/Hz,

when N is quite small, alldesigns exhibit the same trend.

large N to observe EEdecreasing, this behavior seemsnot to happen forPn(b) = 0.01dBm due to smallPn(b).

an optimal trade-off existsbetween the rate benefit ofdeploying larger and larger RISstructure and its correspondingenergy consumption cost.


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Outline


Introduction

Background



Reconfigurable Intelligent Surfaces for Energy Efficiency in WirelessNetworks (TWC Under third reviewing)


Low Resolution Reconfigurable Intelligent Surfaces (Goblecom Dec.2018, Abu Dhabi, UAE) 4

4Chongwen Huang, George C.Alexandropoulos, Alessio Zappone, Merouane Debbah and Chau Yuen, “Energy

Efficient Multi-User MISO Communication using Low Resolution Reconfigurable Intelligent Surfaces” IEEEGLOBECOM, Abu Dhabi, UAE, 2018.


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Simulation Results

Large Intelligent Surface (LIS)

Base station

User

User

Direct signal path

User

Low resolution

antenna element

ଵǡܐͳ

ܭ

Figure: RIS-assisted multi-user MISOcommunication comprising of a M-antennabase station simultaneously serving in thedownlink K single-antenna users

The received signal at the k-thmobile user

yk = (h2,kΦH1 + h1,k) x+ nk ,

where: h1,k ∈ C1×M the directchannel between BS and thek-th mobile user.

Phase shifting value (finiteresolution) for the n-thelement:

ϕn ∈ F ,{exp

(j2πm

2b

)}2b−1

m=0

.

(9)


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Proposed Joint Design Formulation

EE maximization problem is given:

maxΦ,P

∑Kk=1 log2

(1 + pk

σ2

)∑Kk=1 µkpk + KPc + NPn(b)

(10a)

s.t. log2

(1 +

pkσ2

)≥ Rmin,k ∀k = 1, 2, . . . ,K , (10b)

tr((H2ΦH1 +H)+P((H2ΦH1 +H)+)H) ≤ P , (10c)

ϕn ∈ F = {1, e j21−bπ, . . . , e j2π(2b−1)/2b}, b = 1, 2, . . .

∀n = 1, 2, . . . ,N. (10d)

Challenges:

(10) is non-convex; andOptimizing Φ is challenging due to the its discrete nature.


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Proposed Joint Design (1/4).Solution Approach..

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Employ alternating optimization to separately and iteratively solve formatrices P and Φ..RIS with 1-bit Phase Resolution..

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First, optimization with respect to Φ:

minΦ

tr((H2ΦH1 +H)+P((H2ΦH1 +H)+)H) (11a)

s.t. θn = {0, π} ∀n = 1, 2, . . . ,N. (11b)

Still non-convex!

Proposed Solution: Relax the θn: constraint as

0 ≤ θn ≤ 2π.


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Proposed Joint Design (2/4)

.RIS with 1-bit Phase Resolution..

......

Optimization with respect to Φ:

minΦ

tr((H2ΦH1 +H)+P((H2ΦH1 +H)+)H) (12a)

s.t. 0 ≤ θn ≤ 2π ∀n = 1, 2, . . . ,N. (12b)

Convex! Leveraging the function fmincon in MATLAB

Approximated solution

θn = 0: When 3π2 ≤ θn < 2π and 0 ≤ θn < π

2 .θn = π: When π

2 ≤ θn < 3π2 .

Low Complexity approach!


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Proposed Joint Design (3/4)

.RIS with 1-bit Phase Resolution..

......

Optimization with respect to P:

maxP

∑Kk=1 log2

(1 + pk

σ2

)∑Kk=1 µkpk + KPc + NPn(1)

(13a)

s.t. pk ≥ σ2(2Rmin,k − 1) ∀k = 1, 2, . . . ,K , (13b)

tr((H2ΦH1 +H)+P((H2ΦH1 +H)+)H) ≤ P . (13c)

Convex!Problem (13) can be globally solved with limited complexity usingDinkelbach’s methoda.

aDinkelbach, “On nonlinear fractional programming,” Manag. Science, 1967.


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Proposed Joint Design (4/4).RIS with Finite Phase Resolution Elements..

......

2-bit resolution case:

minΦ

tr((H2ΦH1 +H)+P((H2ΦH1 +H)+)H) (14a)

s.t. θn = {0, π2, π,

3π

2} ∀n = 1, 2, . . . ,N. (14b)

Similar with One-bit!

Approximated solution

θn = 0: When 0 ≤ θn < π4 and 7π

4 ≤ θn < 2π.θn = π

2 : When π4 ≤ θn < 3π

4 .θn = π: When 3π

4 ≤ θn < 5π4 .

θn = 3π2 : Otherwise.

Similarly, it can be extended to other finite phase resolution.


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Simulation Results

Figure: The simulated RIS-assisted K -user MISO communication scenariocomprising of a M-antenna base station and a N-element intelligent surface.


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Simulation Results: Globecom Dec. 2018 (1)

-20 -15 -10 -5 0 5 10 15 20 25 3060

80

100

120

140

160

180

200

220

240

260

280

Infinite resolution exhaustive search

1-bit resolution exhaustive search

Proposed with 1-bit resolution


Amplify-and-forward relay

Figure: Achievable sum rate vs thetransmit SNR for K = 16, M = 12,N = 32, and Rmin,k = log2(1 +

SNR2K )

bps/Hz ∀k = 1, 2, . . . , 16.

2-bit phase resolution performsquite close to the infinite one.


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Simulation Results: Globecom Dec. 2018 (2)

-10 -5 0 5 10 15 20 25 30 35 400

20

40

60

80

100

120

140

160

180

Infinite resolution exhaustive search

1-bit resolution exhaustive search



Amplify-and-forward relay

Figure: Energy efficiency maximizationvs the total BS transmit power P forK = 16, M = 12, N = 32, andRmin,k = 0 bps/Hz ∀k = 1, 2, . . . , 16.

The two low resolution casesresult in the highest EE.

Up to 45% Higher EE than therelay-assisted case.


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Simulation Results: ICASSP Apr. 2018

-10 -5 0 5 10 15 20 25 300

20

40

60

80

100

120

140

160

180

200

Fig. R2.2. The average SE

comparison of the proposed

RIS system with no RIS

system. a) M = 32,K = 16,

N = 16; b) K = 8, M = 8,

N = 8.

The proposed methodapproaches the optimal.

With RIS, there are huge gainthan that of without RIS.


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