limited feedback based double-sided full-dimension mimo...

Limited Feedback based Double-SidedFull-Dimension MIMO for Mobile Backhauling

Asilomar Conference on Signals, Systems and Computers

Stefan Schwarz and Markus RuppNovember 7, 2016

Dependable Wireless Connectivity for the Society in Motion

Motivation – Mobile Backhauling

base station

I Increasing usage of mobile devices in public/private transportation

I Frequently unsatisfactory QoS at high mobility due to PHY/MAC inefficiency

I Promising solution: vehicle-mounted small cells for traffic aggregation

⇒ High-performance wireless backhaul required

⇒ Directive beamforming utilizing full-dimension (FD) MIMO arrays

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Contents

System Model

Geometric vs. Algebraic Beamforming

Simulations

Conclusion

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System Model (I)

scatterer

U mobile users

scatterer

base station

LOS component

NLO

S comp

onen

t

clusters

rays ;ÒD;u " )

(c;r )(¶D;u

(c;r)

;ÒA ";u )(c;r )

(¶A;u

(c;r)

I Double-directional channel of user u between RX antenna i and TX antenna j

h(u)i,j (t , τ, φR , θR , φT , θT ) =

Nc∑c=1

g(c)u

Nr∑r=1

α(c,r)u a(R)

i,u (φR , θR) a(T )j (φT , θT )

δ(τ − τ (c)u ) δ(φR − φ

(c,r)A,u ) δ(θR − θ

(c,r)A,u ) δ(φT − φ

(c,r)D,u )δ(θT − θ

(c,r)D,u )

I Time-variant channel impulse response by integrating over (φR , θR), (φT , θT )

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System Model (II)

RF/BB

processing

base station

RF/BB

process.

user 1

RF/BB

process.

user U

channel

DoD

CSI feedback

CSI feedback

I Effective subcarrier-wise base-band channel after OFDM processing

yu [n, k ] = gu [n, k ]HHu [n, k ]∑j∈S

fj [n, k ]sj [n, k ] + gu [n, k ]Hnu [n, k ]

I CSI at TX obtained via dedicated limited feedback from users

⇒ CSI pilots inserted every mTs TTIs on pilot subcarriers P ⊆ {1, . . . ,K}

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Contents

System Model


Simulations

Conclusion

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Transceiver Strategies

I Single layer transmission per user + multi-user multiplexing

I Users estimate channel matrices Hu [n, k ], ∀k ∈ P and n = mTs

I Geometric approach

I Non-frequency-selective/wide-band processing⇒ potentially in RF

I Max-angular-gain/max-angular SLNR beamforming at TX(updated every mTs TTIs)

I Max-angular-gain antenna combining at RX (updated every TTI)

I Algebraic approach

I Frequency-selective⇒ base-band processing

I Zero-forcing beamforming at TX(updated every mTs TTIs, nearest-neighbor interpolation)

I MET or blind residual interference cancellation at RX(updated every TTI on each subcarrier)

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Geometric Approach – RX Beamforming

I Max-angular-gain receive beamforming1

gu [n] = argmax∑k∈P

∥∥gHHu [n, k ]∥∥2,

subject to:

g = a(R)u (φ, θ) , (φ, θ) ∈ Φq ×Θq

I Effective channel-vector

hu [n, k ] = Hu [n, k ]Hgu [n]

1For concreteness we consider UPAs

[a(φ, θ)](`−1)Nh+k =1√

Nrexp

(j2π(δv (`− 1) cos θ) + δh(k − 1) sinφ sin θ

),

` ∈ {1, . . . ,Nv}, k ∈ {1, . . . ,Nh}

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Geometric Approach – CSI FeedbackFeedback of Ne strongest angular directions and corresponding gains

I Consider the angular energy-distribution

A1(φ, θ) =∑k∈P

∣∣a(φ, θ)Hhu [n, k ]∣∣2 , (φ, θ) ∈ Φq ×Θq

I First direction:

(φ(1)T ,u , θ

(1)T ,u) = argmax

(φ,θ)∈Φq×Θq

A1(φ, θ),

g1,u = quant(

A1(φ(1)T ,u , θ

(1)T ,u)

)I Subtract energy-contribution corresponding to this direction

A2(φ, θ) = max(A1(φ, θ)− g1,uP(φ, θ), 0

),

P(φ, θ) =∣∣∣a(φ, θ)Ha(φ

(1)T ,u , θ

(1)T ,u)

∣∣∣2I Iterate to find further directionsI Incoherent energy-based channel decomposition

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Geometric Approach – TX Processing

I Max-angular-gain transmit beamforming

fj [n] = a(φ(1)T ,j , θ

(1)T ,j )

I Max-angular-SLNR transmit beamforming(incoherent addition of leakage energy)

fj [n] =f̃(L)j [n]∥∥∥̃f(L)j [n]

∥∥∥ , f̃j [n] =(σ2

n INt + L)−1

a(φ(1)T ,j , θ

(1)T ,j ),

L =∑

`∈S, 6̀=j

Ne∑i=1

gi,` a(φ(i)T ,`, θ

(i)T ,`)a(φ

(i)T ,`, θ

(i)T ,`)

H

I Greedy multi-user scheduling based on incoherent SINR estimate

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Algebraic Approach – RX Beamforming

I Maximum eigenmode transmission (MET)

gu [n, k ] = u(1)u [n, k ],

hu [n, k ] = σ(1)u [n, k ]v(1)

u [n, k ],

Hu [n, k ] = Uu [n, k ]Σu [n, k ]Vu [n, k ]H

I Maximum expected achievable rate combining (MERC) 2

⇒ Blind residual interference cancellation based on quantized CSI

2S. Schwarz and M. Rupp, Maximum expected achievable rate combining for limited feedback

block-diagonalization, ICASSP 2015

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Algebraic Approach – CSI Feedback

I Decompose the channel into a sum of plane-waves using an over-completebasis of quantized directions Φq ×Θq

{gi,u [k ], φ

(i)T ,u [k ], θ

(i)T ,u [k ]

}= argmin

gi ,(φi ,θi )

∥∥∥∥∥∥hu [n, k ]−Ne∑i=1

gi aP(φi , θi )

∥∥∥∥∥∥2

,

subject to:

gi ∈ C, (φi , θi ) ∈ Φq ×Θq

I Iterative solution using orthogonal matching pursuit (OMP) – perfectreconstruction with at most Nt coefficients

I Coherent channel decomposition

I Frequency-selective feedback on CSI pilot positions

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Algebraic Approach – TX Processing

I Frequency-selective greedy multi-user scheduling 3

⇒ Constant schedule in-between CSI feedback positions in time and frequency

I Zero-forcing (ZF) beamforming based on quantized CSI

⇒ increasing CSI mismatch over time is neglected

3S. Schwarz and M. Rupp, Evaluation of distributed multi-user MIMO-OFDM with limited feedback, IEEE

Transactions on Wireless Communications, vol. 13, no. 11, 2014

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Contents

System Model


Simulations

Conclusion

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Simulation Setup

120°

550m

I One base station serving ten UEs at 2.1 GHz

I UEs traveling at approx. 50 km/h (100 Hz Doppler)

I UPA of dimension 10× 10 at TX

I UPA of dimension {1× 1, 2× 2, 4× 4} at RX

I Microscopic fading only at SNR = 20 dB

I CSI feedback every mTs = 10 TTI (delayless)

I Non-line of sight propagation (3GPP 3D model)

I Normalized TTI: τs fd , τs ∈ [0,mTs − 1]

I Feedback of Ne = 4 angular directions

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Angular CSI Feedback

Azimuth [°]

Ele

va

tio

n [

°]

Re

lati

ve

po

we

r

-80 -60 -40 -20

40

60

80

100

120

140 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

806040200 -80 -60 -40 -20Azimuth [°]

40

60

80

100

120

140

Ele

va

tio

n [

°]

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Re

lati

ve

po

we

r

806040200

Fig. 1: Angular CSI feedback of the geometric method (left) and the algebraic method (right).

I Geometric method: Ne peaks of signal-energy distribution

I Algebraic method: phase-coherent decomposition into Ne multipath-components

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Algebraic Beampatterns

-40

-20

0

20

40

60

-6030 60 90 120 150

-40

-20

0

20

40

60

-6030 60 90 120 150

20

0

-20

-40

-60

-80

-100

Pow

er [d

BW]

Fig. 2: Max gain transmit beamforming (left) and max SLNR transmit beamforming (right).

I Max gain: largest signal gain but potentially strong interference

I Max SLNR: decreased interference for the cost of reduced intended signal

⇒ Smart multi-user scheduling important in both cases

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Achievable Rate Comparison 1 × 1

0.90.80.70.60.50.40.30.20.10

50

40

30

20

10

0

normalized TTI

Ach

ieva

ble

rate

[bits

/s/H

z]

10x10_1x1_20dB_100Hz_NLOS

BD perfect CSI

BD Fs=1

BD Fs=12

max Gain Fs=72max SLNR Fs=72

BD Fs=72

single user perfect CSI

I Large achievable rate of BD (algebraic method) with accurate CSI

I More robust behavior of geometric beamforming⇔ angles change slowly

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0.90.80.70.60.50.40.30.20.10

50

40

30

20

10

0

normalized TTI

Ach

ieva

ble

rate

[bits

/s/H

z]


BD perfect CSI

BD-MET Fs=1

BD-MET Fs=12


BD-MET Fs=72

max SLNR Fs=72

max Gain Fs=72

I Similar behavior with multiple RX and interference-ignorant receivers

I Significant performance gain through RX-side interference-suppression

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0.90.80.70.60.50.40.30.20.10

50

40

30

20

10

0

normalized TTI

Ach

ieva

ble

rate

[bits

/s/H

z]


BD perfect CSI

BD-MERC Fs=1

BD-MET Fs=1

BD-MERC Fs=12

BD-MET Fs=12


BD-MET Fs=72

BD-MERC Fs=72max SLNR Fs=72

max Gain Fs=72

I Similar behavior with multiple RX and interference-ignorant receivers

I Significant performance gain through RX-side interference-suppression

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0.90.80.70.60.50.40.30.20.10

50

40

30

20

10

0

normalized TTI

Ach

ieva

ble

rate

[bits

/s/H

z]


single user perfect CSIBD-MET Fs=72

BD perfect CSI

BD-MET Fs=12

BD-MET Fs=1

BD-MERC Fs=72

BD-MERC Fs=12

BD-MERC Fs=1

max SLNR Fs=72max Gain Fs=72

I Sufficient spatial degrees of freedom to virtually suppress all residualinterference (even blindly)

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Contents

System Model


Simulations

Conclusion

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Summary and Outlook

I Geometric beamforming performs more robustly

I Algebraic approach has much higher spatial multiplexing potential/capabilities

I Hardware-complexity advantages of geometric approach

I Without RX-side interference-suppression geometric beamforming with imperfectCSI outperforms algebraic approach

I RX-side interference-suppression can safe the day (even blindly)

I Selection of appropriate method depending on CSI accuracy

I Combination of geometric TX beamforming (robustness) and algebraic RXbeamforming (interference-suppression)

I Hybrid combination of both methods

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Limited Feedback based Double-SidedFull-Dimension MIMO for Mobile Backhauling

Asilomar Conference on Signals, Systems and Computers

Stefan Schwarz and Markus RuppNovember 7, 2016


References I

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limited feedback based double-sided full-dimension mimo...

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