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Huawei Technologies Co., Ltd. All right reserved Slide 1 www.huawei.com TELFOR-2013 November 26-28, 2013 Tensor-Based Multiuser Detection and Intra-Cell Interference Mitigation in LTE PUCCH Vladimir Lyashev | Ivan Oseledets | Delai Zheng Huawei Technologies Russian Research Center, Moscow ([email protected])

Tensor-Based Multiuser Detection

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Since Release 8 Long-Term Evolution (LTE) by the 3rd Generation Partnership Project (3GPP), the uplink control channel called the physical uplink control channel (PUCCH) is specified. In this paper, we propose a new multi-user joint receiver processing for LTE PUCCH that counteracts the intra-cell interference (ICI). Using the fact that the received signal in PUCCH signaling follows a constrained tensor model, a multi-user receiver based on an iterative joint channel/code estimation and symbol detection is proposed. The interest in such a challenging setting relies on the overhead reduction synchronization errors defined by time offset and inaccuracies of timing align. Simulation results show remarkable performance gains of the proposed receiver compared to the conventional time-frequency decorrelator based receiver under the same conditions.

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Page 1: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 1

www.huawei.com

TELFOR-2013 November 26-28, 2013

Tensor-Based Multiuser Detection

and

Intra-Cell Interference Mitigation

in LTE PUCCH

Vladimir Lyashev | Ivan Oseledets | Delai Zheng

Huawei Technologies

Russian Research Center, Moscow ([email protected])

Page 2: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 2

Why does PUCCH important?

“PUCCH occupies too much bandwidth and

is used not in the most efficient way.” Field-test scenario:

• eRAN7, 10 MHz

• 60 connected UEs

• 30 UEs constantly downloading

large files (i.e. video streaming)

eNodB allocates

• 10RB for PUCCH,

• 3 UE per RB in average

“VoLTE dramatically

increases PUCCH usage.” • 8 million VoLTE users worldwide

• VoLTE will take off in 2015-2016 worldwide

• over 10-20 MHz spectrum - hundreds users

• 46% MBB providers required VoLTE during 1 year

AMR calls/1MHz

GSM 8

UMTS 12

HSPA 24

VoLTE 50

“40% Of YouTube Traffic Now Mobile,

Up From 25% In 2012, 6% In 2011.”

Page 3: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 3

LTE Uplink: resources

Page 4: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 4

PUCCH Allocation and SRS Signal

SRS bandwidth is multiplied

by 4RB: 4, 8, 12, …

Page 5: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 5

Intra-cell Interference in LTE PUCCH

In practice, time-alignment of the

signals at the eNodeB receiver is

not perfect.

Up to:

• 36UE per 1RB in Format 1x

• 12UE per 1RB in Format 2x

Separation by CAZAC sequence

Page 6: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 6

Timing Error: Main Reasons

limited resolution and measurement errors

propagation time change due to UE movement

oscillator drift

abrupt change of the multipath channel

misdetection of the Timing Advance

(Initial or Update) command

Page 7: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 7

Abrupt changes in channel delay profile T

A

co

mm

an

d

Timing

correction at

UE

Timing

correction at

UE

Timing

correction at

UE

Path birth-

death

Path birth-

death

abs(timing

error)

200ms-1s 200ms-1s 200ms-1s

can’t be compensated by TA commands alone!

Page 8: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 8

Field-test measurements: scenario

1

2

1

2

20 km/h speed

600 m length difference

720kHz (6RB) SRS signal

generation

// blue line

14.4 MHz measurement signal

// red line

Page 9: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 9

Field test measurements: results

Page 10: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 10

Mathematical Model

IPPTPPTPYH

j

Q

jqq

qqqq

H

jjjjj

H

jj XHXH 2

ceinterferencellintra

1losspower

NUE = 6, TAerror = 0 μs NUE = 6, TAerror = 1.56 μs

Desired user Interference SIR Desired user Interference SIR

23.7 0.11 23 dB 14.41 4.12 5.5 dB

12.52 0.67 22 dB 11.23 2.63 6.5 dB

13.37 0.95 11 dB 16.91 1.01 12 dB

9.83 0.65 12 dB 2.42 3.15 -1.2 dB

7.5 0.26 15 dB 7.97 2.71 4.5 dB

10.6 0.51 13 dB 5.11 4.21 0.8 dB

Measurement results

Me

asu

rem

ent #

CAZAC property for ideal sync.: 𝐏𝑗𝐻𝐏𝑞 =

1, 𝑗 = 𝑞;0, 𝑗 ≠ 𝑞.

Page 11: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 11

Mathematical Model and Its Approximation

),,(),(),(),,,(),,(),,(1

klnEkqTlqXlknqHlkqPklnYQ

q

B-rank channel approximation:

B

kSnqWlknqH1

),(),,(),,,(

Rank-2 model basically gives a very good fit to the

experimental channel H(q, n, l, k), usually of a fit of order 95%.

The rank-1 model also look promising, and can approximate

70% of the energy.

Page 12: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 12

Rank-1 (B=1) Model Approximation

signal) (receivedtensor 3Dforslice

),,(),(

th

l

l

klnYknY

sequence) (referencetensor 3Dforslice

),,(),(

th

l

l

klqPkqP

Mathematical Notation in

Slice Form

)()()( kSkTkT

Jo

int

Alg

ori

thm

AL

S-1

llll ETPWXY

Page 13: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 13

Joint Detection

Receive Signal

Simple Channel

Estimation & MRC

with equalizing

Set as initial guess

for ALS iterations ALS-1 iterations

Output CQI bits

W0 X0 X

Y

XMRC

T0= I12x12^

QPSK-symbols

demapping &

decoding

H0

Update T ^ Update W Update X

Iteration++

lTll YTIPWX ˆ lWl YIXW ˆ lllX YPXIW ˆ

Page 14: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 14

Joint Detection with Quality Control

Qual

ity

dec

odin

g

TErr = 0

FER in MRC: 128 / 12 000

FER in ALS-1: 225 / 12 000

MRC & ALS-1 have the same error frames: 114 / 12 000

ALS-1 males mistake (MRC not): 111 / 12 000

TErr = 3

FER in MRC: 407 / 12 000

FER in ALS-1: 180 / 12 000

MRC & ALS-1 have the same error frames: 107 / 12 000

ALS-1 males mistake (MRC not): 73 / 12 000

Pilots

Page 15: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 15

Simulation Parameters

Parameter Value

LTE PUCCH format format 2

Bandwidth 1.4 MHz

CQI 7 bits

Modulation type QPSK

Number of Rx antennas 4

Number of Tx antennas per user 1

Number of users 6

Cyclic shift (CS) interval for RS π / 3

Power of desired user (CS=0) 0 dB

Power for UE with CS=1,3,5 3 dB

Power for UE with CS=2,4 0 dB

Timing error (uniform distribution) -1.56 … 1.56 us

Propagation channel ETU70

Number of simulated sub-frames 20 000

Page 16: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 16

Convergence

with Quality Decoding Control without Quality Decoding Control

Page 17: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 17

Simulation Results

with Quality Decoding Control without Quality Decoding Control

Gap: 0.8 dB Gap: 0.4 dB

Page 18: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 18

Outlook

Non-Orthogonal Access

MU-MIMO and Massive-MIMO

Algorithm Diversity for Cloud RAN (cRAN)

Dimension Reduction in Non-Linear Signal

Processing

Page 19: Tensor-Based Multiuser Detection

Huawei Technologies Co., Ltd. All right reserved Slide 19

www.huawei.com

TELFOR-2013 November 26-28, 2013

Dr. Vladimir Lyashev, IEEE Member [ [email protected] ]

[ linkedin.com/in/lyashev/ ]