a low-complexity phase rotation estimation using …high-papr signal 30 august 2018 vtc2018-fall @...

ROEC東北大学電気通信研究機構

Research Organization of Electrical Communication

Tohoku University

Amnart Boonkajay Fumiyuki Adachi

Wireless Signal Processing Research Group

Research Organization of Electrical Communication (ROEC), Tohoku University

Acknowledgement:

This work is a part of “The research and development project for realization of the fifth-generation mobile communications system”

commissioned to Tohoku University by The Ministry of Internal Affairs and Communications (MIC), Japan.

A Low-Complexity Phase Rotation Estimation using

Fourth-power Constellation for Blind SLMIEEE 88th Vehicular Technology Conference (VTC2018-Fall)

30 August 2018, Chicago, USA

Presentation outline

Introduction

High-PAPR signal

Blind selected mapping (blind SLM)

Computational complexity problem

Objective

Modified blind SLM for SC uplink (STBC-TD and MU-MIMO)

Transmitter

Receiver

Modified phase rotation sequence estimation

Performance evaluation

Conclusion & Future works The following figures are used throughout this presentation:

freq.

Analog waveform

Frequency-domain components (subcarriers)

D3D1D0 D2

Time-domain

Frequency-domain

time

d3d1d0 d2

Time-domain signal vector (pulse)

30 August 2018 VTC2018-Fall @ Chaicago, USA

High-PAPR signal


[A] D. Falconer, IEEE Trans. Commun., vol.59, no.4, pp.1154-1162, Apr 2011.

[B] J. Joung et al., IEEE Commun. Surveys&Tutorials, vol.17, no.1, pp.315-333, 1Q 2015.

Peak-to-average power ratio (PAPR) : definition

1

0

2

2

)(1

})(max{PAPR

cN

tc

tsN

ts peak

average inp

ut

ou

tpu

t

0

0.5

1

1.5

0 0.5 1 1.5 2 2.5

|Vout|

(vo

lt)

|Vin| (volt)

High-power amplifier (HPA)

-2

-1

0

1

2

Re{

s(t)

exp

(j2f c

t)}

Time index, t-2

-1

0

1

2

Re{

s(t)

exp

(j2f c

t)}

Time index, t-2

-1

0

1

2

Problem due to high-PAPR signal

Waveform distortion due to non-linear amplification[A] Solutions: using high-spec amplifier or back-off energy efficiency (EE) degrades[B]

High-PAPR signal


[A] D. Falconer, IEEE Trans. Commun., vol.59, no.4, pp.1154-1162, Apr 2011.

[B] J. Joung et al., IEEE Commun. Surveys&Tutorials, vol.17, no.1, pp.315-333, 1Q 2015.

[C] S. Okuyama et al., in Proc. VTC 2010-Spring, Taipei, Taiwan, May 2010.[D] S. Kumagai et al., IEICE Trans. Commun., Vol. E97-B, No. 9, pp. 1967-1976, Sept. 2014.

Peak-to-average power ratio (PAPR) : definition

1

0

2

2

)(1

})(max{PAPR

cN

tc

tsN

ts peak

average

Problem due to high-PAPR signal

Waveform distortion due to non-linear amplification[A] Solutions: using high-spec amplifier or back-off energy efficiency (EE) degrades[B]

inp

ut

ou

tpu

t

0

0.5

1

1.5

0 0.5 1 1.5 2 2.5

|Vout|

(vo

lt)

|Vin| (volt)

High-power amplifier (HPA)

-2

-1

0

1

2

Re{

s(t)

exp

(j2f c

t)}

Time index, t-2

-1

0

1

2

Re{

s(t)

exp

(j2f c

t)}

Time index, t-2

-1

0

1

2

PAPR : single-carrier (SC) vs. OFDM waveforms

SC waveform has lower PAPR appropriate for uplink transmission

But … PAPR increases due to transmit processing e.g. high-level modulation[C], transmit filtering and precoding[D], especially when the SC waveform

is generated by means of DFT-precoded OFDM

DF

T

High-PAPR signal

SC transmit signal processing (equivalent to DFT-precoded OFDM)

time

d3d1d0 d2

Time-domain

block

freq.

D3D1D0 D2

Frequency

components

Fre

qu

en

cy

ma

pp

ing

freq.

D3D1D0 D2

Filtered signal

IFF

T



[E] R. W. Bauml et al., IEEE Electron. Lett., vol.32, no.22, pp.2056-2057, Oct. 1996.

* (complex-conjugate)

Side-information (sequence number)

FF

T

Eq

ua

lizer

IDF

T

time

time

Original

Phase rotation

sequence generator

※ Assuming SC uplink transmission, 3 sequences, 4 symbols

Data

0 = {0,0,0,0}

1 = {0,-120,-120,0}

2 = {120,-120,0,0}

Original

time

time

Codebook

Select a phase rotation sequence

giving the lowest PAPR[E]

User equipment

(UE)

Base station

(BS)

Spectrum efficiency (SE) degrades

BER degrades if there are errors on side

information detection



[E] R. W. Bauml et al., IEEE Electron. Lett., vol.32, no.22, pp.2056-2057, Oct. 1996.[F] A. Boonkajay et al., Proc. ICICS2015, Singapore, Dec. 2015.[G] A. D. S. Jayalath et al., IEEE Trans. Wireless Commun., vol.4, no.5, pp.2006-2013, Sept. 2005.


FF

T

Eq

ua

lizer

IDF

T

time

time

Original

Phase rotation

sequence estimator

※ Assuming SC uplink transmission, 3 sequences, 4 symbols

Data

0 = {0,0,0,0}

1 = {0,-120,-120,0}

2 = {120,-120,0,0}

Original

time

time

Codebook

Select a phase rotation sequence

giving the lowest PAPR[E]

User equipment

(UE)

Base station

(BS)

-2

-1

0

1

2

-2 -1 0 1 2

Real part

Imag

inar

y p

art

16QAM, 16-path Rayleigh fading

Avg. received Eb/N0 = 30 dB

: correct de-mapping

: incorrect de-mapping

Estimate the selected phase rotation

sequence by exploiting the difference in

received constellations[F]

Previous studies about blind SLM:

OFDM transmission – has been studying in [G] from many years ago(meanwhile, [G] did not consider discrete phase rotation sequence like us)

SC transmission – firstly introduced by applying phase rotation sequence to either frequency

components [F] or time-domain symbols [H]

Blind SLM for MIMO transmission (single-user STBC-TD and MU-MIMO) were introduced in [I,J]

[H] A. Boonkajay et al., Proc. VTC 2016-Fall, Montreal, Canada, Sept. 2016.[I] A. Boonkajay et al., Proc. IEEE VTS APWCS2017, Incheon, Korea, Aug. 2017.[J] A. Boonkajay et al., Proc. IEEE/CIC ICCC2017, Qingdao, China, Oct. 2017.



[F] A. Boonkajay et al., Proc. ICICS2015, Singapore, Dec. 2015.

[G] A. D. S. Jayalath et al., IEEE Trans. Wireless Commun., vol.4, no.5, pp.2006-2013,

Sept. 2005.

[K] A. Boonkajay et al., Proc. PIMRC2017, Montreal, Canada, Oct. 2017.


FF

T

Eq

ua

lizer

IDF

T

time

time

Original

Phase rotation

pattern estimator

Estimation algorithm :mod

1*

0~ 10

ˆ{ ( )} arg min min ( ) ( )cN

m mm M

n

n n d n

Φ

The m-th phase sequences in codebook

Received signal after IDFT

QAM mapping

The above phase rotation estimation can be done by 2 approaches …

Maximum likelihood (ML) – applying all M possible de-mapping

sequences to search a correct sequence[F,G] high complexity

2-step estimation – using Viterbi algorithm to search an initial estimated

phase sequence, then applying verification to obtain a correct sequence[K]

5 6 7 8 9

108

107

106

105

Tota

l co

mp

uta

tion

al

com

ple

xit

y

PAPR0.1% (dB)

109

Note PAPR0.1% of transmission w/o SLM

OFDM : 16QAM 11.3 dB

SC : 16QAM 8.8 dB

ML estimation

2-step estimation

104

M=16

M=2048

M=256

M=64

M=16

M=2048

M=256

M=64

SISO, 16QAM transmission,

Random polyphase {0, 120, 240}

SC

OFDM

5 6 7 8 9

108

107

106

105

Tota

l co

mp

uta

tion

al

com

ple

xit

y

PAPR0.1% (dB)

109

Note PAPR0.1% of transmission w/o SLM

OFDM : 16QAM 11.3 dB

SC : 16QAM 8.8 dB

ML estimation

2-step estimation

104

M=16

M=2048

M=256

M=64

M=16

M=2048

M=256

M=64

SISO, 16QAM transmission,

Random polyphase {0, 120, 240}

SC

OFDM





Sept. 2005.



FF

T

Eq

ua

lizer

IDF

T

time

time

Original

Phase rotation

pattern estimator

Estimation algorithm :mod

1*

0~ 10

ˆ{ ( )} arg min min ( ) ( )cN

m mm M

n

n n d n

Φ

The m-th phase sequences in codebook


QAM mapping

The above phase rotation estimation can be done by 2 approaches …

Maximum likelihood (ML) – applying all M possible de-mapping

sequences to search a correct sequence[F,G] high complexity

2-step estimation – using Viterbi algorithm to search an initial estimated

phase sequence, then applying verification to obtain a correct sequence[K]

But … the complexity reduction capability is obvious only when M

is large

A new ML phase rotation sequence estimation will be

introduced to deal with the above problem

Objective


[L] C. Siegl et al., Proc. SCC2010, Siegen, Germany, Jan. 2010.

Blind SLM structure : ※ Assuming SC uplink transmission, 3 sequences, 4 symbols


FF

T

Eq

ua

lizer

IDF

T

time

time

Original

Phase rotation

sequence estimator

Data

0 = {0,0,0,0}

1 = {0,135,135,0}

2 = {135,0,135,0}

Original

time

time

CodebookUser equipment

(UE)

Base station

(BS)

To introduce a low-complexity ML phase rotation sequence

estimation for blind SLM

Objective



To introduce a low-complexity ML phase rotation sequence

estimation for blind SLM

Blind SLM structure : ※ Assuming SC uplink transmission, 3 sequences, 4 symbols


FF

T

Eq

ua

lizer

IDF

T

time

time

Original

Phase rotation

sequence estimator

Data

0 = {0,0,0,0}

1 = {0,135,135,0}

2 = {135,0,135,0}

Original

time

time

CodebookUser equipment

(UE)

Base station

(BS)

1. Phase rotation is changed from a random

set of {0,120,-120} to {0,135}

• Increase the distance between correct

and incorrect de-mapping[L]

• Same PAPR as conventional SLM

2. Phase rotation sequence estimation is

done based on the fourth-power

constellation

• Number of candidates in minimum

distance searching reduces significantly-4

-2

0

2

4

-4 -2 0 2 4

Real part

Imag

inar

y p

art



Furthest distance

Modified blind SLM for SC uplink(Transmitter)


Data

mo

d

SLM algorithm

Phase

rotation

Tx. processing• Band-limiting filter

• STBC coding

• Eigenmode filtering

IFFT

IFFT

+CP

+CP

…

NUE antennasD

FT

……

User equipment (UE)

Info

. b

its

Base station (BS)

CP

CP

FFT

FFT

NBS antennas

Rx. processing• STBC decoding +

MMSE-FDE

• Multiuser MMSE

filtering

Phase rotationsequence estimation

De-mapping

*

Data

de

mo

d

… IDF

T

Estim

ate

d b

its

NUE waveforms

……



Data

mo

d

SLM algorithm

Phase

rotation


• STBC coding


IFFT

IFFT

+CP

+CP

…

NUE antennasD

FT

……

User equipment (UE)

Info

. b

its

Base station (BS)

CP

CP

FFT

FFT

NBS antennas


MMSE-FDE

• Multiuser MMSE

filtering


De-mapping

*

Data

de

mo

d

… IDF

T

Estim

ate

d b

its

NUE waveforms

……

SLM algorithm at transmitter : single-user STBC-TD

space{D0(k)} {D1(k)}

{D0(k)}

{D1(k)}

)}({ *

1 kD

)}({ *

0 kD

time

ST

BC

e

nco

der

Only complex-conjugate operations

PAPR of signals before and after STBC coding are the same[I]

Individual phase rotation pattern is selected for {Dj(k)} SLM achieves same performance as SISO

※ Assuming 2 Tx. antennas

Phase rotation selection criterion : 0 1~

ˆ ( ) arg min PAPR { ( ) ( )}m jm M

m j n d n

The m-th phase sequences in codebook Time-domain transmit

signal (before DFT)※ Individual selection for J blocks (j=0J1), hence the index is a function of j

[I] A. Boonkajay et al., Proc. IEEE VTS APWCS2017, Incheon, Korea, Aug. 2017.



Data

mo

d

SLM algorithm

Phase

rotation


• STBC coding


IFFT

IFFT

+CP

+CP

…

NUE antennasD

FT

……

User equipment (UE)

Info

. b

its

Base station (BS)

CP

CP

FFT

FFT

NBS antennas


MMSE-FDE

• Multiuser MMSE

filtering


De-mapping

*

Data

de

mo

d

… IDF

T

Estim

ate

d b

its

NUE waveforms

……

SLM algorithm at transmitter : MU-MIMO

[J] A. Boonkajay et al., Proc. IEEE/CIC ICCC2017, Qingdao, China, Oct. 2017.

[M] F. Adachi et al., IEEE Trans. Commun., vol.E100-B, no.8, pp.1190-1204, Aug. 2017.

UE

UE UE

,0~

,0 1 1~

ˆ ( ) arg min max PAPR { ( )}u n mm M n N

m u s n

)()()(2

)( 2/1 kkkT

Ek uuu

s

su DPVS

(NUE1)

Transmit signal(frequency domain)

(NUEG)

Unitary matrix

obtained by SVD[M]

(GG)

MMSE power alloc.

(G1)

Data

streams(frequency domain)

Transmit filtering

Tx. filtering

{S0(k)}{D0(k)}

{DG-1(k)}

{S1(k)}

{SNt-1(k)}(G1)

Data streams(frequency domain)

(NUE1)

Transmit signal(frequency domain)

PAPR of signals before and after Tx. filtering are

different due to matrix multiplication

Minimax criterion is used[J]

Phase rotation selection criterion :

Transmit waveform corresponding to the m-th phase sequences

※ Same phase rotation sequence for G streams (g=0G1) but different for each user,

hence the index is a function of u

※ This selection criterion is also used for OFDM downlink, both STBC-TD and MU-MIMO

Modified blind SLM for SC uplink(Receiver)


Data

mo

d

SLM algorithm

Phase

rotation


• STBC coding


IFFT

IFFT

+CP

+CP

…

NUE antennasD

FT

……

User equipment (UE)

Info

. b

its

Base station (BS)

CP

CP

FFT

FFT

NBS antennas


MMSE-FDE

• Multiuser MMSE

filtering


De-mapping

*

Data

de

mo

d

… IDF

T

Estim

ate

d b

its

NUE waveforms

……

ML phase rotation sequence estimation at receiver (conventional[F,G])

Applying after STBC decoding or multiuser MMSE filtering

The lowest Euclidean distance correct de-mapping

mod0~

1*

10

ˆ( ) arg min min ( ) ( )cN

m jm M

n

m j n d n

STBC-TD :

MU-MIMO :mod

11*

,1

0 0~0

ˆ( ) arg min min ( ) ( )cNG

m u gm M

g n

m u n d n

The m-th phase sequence in codebook


QAM mapping※ Equations are the same for both ML and

2-step estimations

-2

-1

0

1

2

-2 -1 0 1 2

Real part

Imag

inar

y p

art







Sept. 2005.



Data

mo

d

SLM algorithm

Phase

rotation


• STBC coding


IFFT

IFFT

+CP

+CP

…

NUE antennasD

FT

……

User equipment (UE)

Info

. b

its

Base station (BS)

CP

CP

FFT

FFT

NBS antennas


MMSE-FDE

• Multiuser MMSE

filtering


De-mapping

*

Data

de

mo

d

… IDF

T

Estim

ate

d b

its

NUE waveforms

……

ML phase rotation sequence estimation at receiver (conventional[F,G])

mod0~

1*

10


m jm M

n

m j n d n

STBC-TD :

MU-MIMO :mod

11*

,1

0 0~0


m u gm M

g n

m u n d n


QAM mapping※ Equations are the same for both ML and

2-step estimations

-2

-1

0

1

2

-2 -1 0 1 2

Real part

Imag

inar

y p

art







Sept. 2005.

Assuming 16QAM, this needs to be done

16 times per one symbol

***High complexity***




Modified blind SLM for SC uplink(ML phase sequence estimation)


-4

-2

0

2

4

-4 -2 0 2 4

Real part

Imag

inar

y p

art


Avg. received Eb/N0



Modifications on phase rotation sequence design and estimation※ No major changes on SLM algorithm, i.e. phase rotation selection at the transmitter

{0,120,-120}, (I+jQ)1

• Conventional blind

SLM[H]

• High complexity required

at Rx.

{0,120,-120}, (I+jQ)4

• No. of QAM mapping

points reduces

(16QAM: 164)

Pros: No. of QAM mapping

points reduce[L]

Cons: Noise enhancement-4

-2

0

2

4

-4 -2 0 2 4

Real part

Imag

inar

y p

art


Avg. received Eb/N0



[H] A. Boonkajay et al., Proc. VTC 2016-Fall, Montreal, Canada, Sept. 2016.


Fourth-order

operation

Modified blind SLM for SC uplink(ML phase sequence estimation)


-4

-2

0

2

4

-4 -2 0 2 4

Real part

Imag

inar

y p

art


Avg. received Eb/N0



Modifications on phase rotation sequence design and estimation※ No major changes on SLM algorithm, i.e. phase rotation selection at the transmitter

{0,120,-120}, (I+jQ)1

• Conventional blind

SLM[H]

• High complexity required

at Rx.

Fourth-order

operation

{0,120,-120}, (I+jQ)4

• No. of QAM mapping

points reduces

(16QAM: 164)

Pros: No. of QAM mapping

points reduce[L]

Cons: Noise enhancement-4

-2

0

2

4

-4 -2 0 2 4

Real part

Imag

inar

y p

art


Avg. received Eb/N0





Enlarging the distance b/w correct and

incorrect de-mappings

-4

-2

0

2

4

-4 -2 0 2 4Real part

Imag

inar

y p

art


Avg. received Eb/N0



{0,135}, (I+jQ)1

• Distance b/w correct and


becomes larger[L]

{0,135}, (I+jQ)4

• No. of QAM mapping points

reduces (16QAM: 164)

• Distance b/w correct and


becomes larger-4

-2

0

2

4

-4 -2 0 2 4

Real part

Imag

inar

y p

art


Avg. received Eb/N0



Fourth-order

operation



Data

mo

d

SLM algorithm

Phase

rotation


• STBC coding


IFFT

IFFT

+CP

+CP

…

NUE antennasD

FT

……

User equipment (UE)

Info

. b

its

Base station (BS)

CP

CP

FFT

FFT

NBS antennas


MMSE-FDE

• Multiuser MMSE

filtering


De-mapping

*

Data

de

mo

d

… IDF

T

Estim

ate

d b

its

NUE waveforms

……

Modified ML phase rotation sequence estimation at receiver

4mod

1 4*

~0 10


m jm M

n

m j n d n

STBC-TD :

MU-MIMO : 4mod

11 4*

,0 1~

0 0


m u gm M

g n

m u n d n



Fourth-order mapping-4

-2

0

2

4

-4 -2 0 2 4

Real part

Imag

inar

y p

art


Avg. received Eb/N0



Furthest

distance



Performance evaluation


Simulation parameters

Data transmission

Data modulation 16QAM, 64QAM

No. of subcarriers Nc=128

CP length Ng=16

SLM algorithm

Phase rotation type Random

No. of phase sequences M=1256

Phase rotation estimation method Maximum likelihood

Oversampling factor V=8

User equipmentChannel estimation Ideal

No. of UE antennas NUE=2

Base stationChannel estimation Ideal

No. of BS antennas NBS=4

Channel

Fading Frequency-selective block Rayleigh

Power delay profile Symbol-spaced, 16-path uniform

Maximum Doppler frequency fD 0

Transmit/receive filters pair for SC uplink[M]

STBC-TD : no filter/MMSE-FDE, MU-MIMO : eigenmode/MMSE

※ In MU-MIMO, we assume that U=2 users transmitting G=NUE=2 streams simultaneously

Performance indicators

PAPR0.1% PAPR value at the point that complementary cumulative distribution function (CCDF) equals 0.001

BER assuming no channel coding, no adaptive rank/modulation control (ARMC)

Computational complexity counting the number of real-valued additions

complexity of a real-valued multiplication operation is approximated to be 3 times of a real-valued addition operation[N]

[M] F. Adachi et al., IEEE Trans. Commun., vol.E100-B, no.8, pp.1190-1204, Aug. 2017.

[N] S. Arora and B. Barak, Computational Complexity: A Modern Approach, Cambridge,

2009.

Performance evaluation(PAPR vs complexity)




16QAM 64QAM

4 5 6 7 8 9 10 11 12

Com

pu

tati

on

al

com

ple

xit

y 107

106

105

104

108

103

PAPR0.1% (dB)

2-step estimation

Conv. blind SLM [K]

ML estimation

Conv. blind SLM [H]

Proposed {0,135}

SC uplink, Nc=128, Ng=16,

16QAM, NBS=4, NUE=2

STBC-TD

MU-MIMO

M=1

M=256

M=16

M=4

M=64

M=1

M=256

M=16

M=4

M=64

4 5 6 7 8 9 10 11 12

Com

pu

tati

on

al

com

ple

xit

y 107

106

105

104

108

103

PAPR0.1% (dB)

2-step estimation

Conv. blind SLM [K]

ML estimation

Conv. blind SLM [H]

Proposed {0,135}


64QAM, NBS=4, NUE=2

STBC-TD

MU-MIMO

M=1

M=256

M=16

M=4

M=64

M=1

M=256

M=16

M=4

M=64

※ Computational complexity table is available in the manuscript

PAPR reduces when M increases (move from right to left), but the complexity also increases (move from bottom to top)

Conventional blind SLM ({0,120,-120}, (I+jQ)1) with 2-step estimation : complexity reduction capability is obvious when M>64

Proposed ML estimation ({0,135}, (I+jQ)4) …

Same PAPR as conv. blind SLM

Less complexity than conv. ML and 2-step estimation (due to candidates reduction in minimum Euclidean distance searching)

4 5 6 7 8 9 10 11 12

Com

pu

tati

on

al

com

ple

xit

y 107

106

105

104

108

103

PAPR0.1% (dB)

2-step estimation

Conv. blind SLM [K]

ML estimation

Conv. blind SLM [H]

Proposed {0,135}


16QAM, NBS=4, NUE=2

STBC-TD

MU-MIMO

M=1

M=256

M=16

M=4

M=64

M=1

M=256

M=16

M=4

M=64

4 5 6 7 8 9 10 11 12

Com

pu

tati

on

al

com

ple

xit

y 107

106

105

104

108

103

PAPR0.1% (dB)

2-step estimation

Conv. blind SLM [K]

ML estimation

Conv. blind SLM [H]

Proposed {0,135}


64QAM, NBS=4, NUE=2

STBC-TD

MU-MIMO

M=1

M=256

M=16

M=4

M=64

M=1

M=256

M=16

M=4

M=64

Performance evaluation(PAPR vs complexity)




16QAM 64QAM

※ Computational complexity table is available in the manuscript

At a point that achieving PAPR reduction of 3 dB …

16QAM: Computational complexity is reduced to be 35% (38%) of conventional ML estimation for STBC-TD (MU-MIMO)

64QAM: Computational complexity is reduced to be 14% (16%) of conventional ML estimation for STBC-TD (MU-MIMO)

3 dB

3 dB3 dB

3 dB

Performance evaluation(BER, no channel coding)



16QAM 64QAM

Assuming M=64

No significant BER degradation when the average received power is sufficiently high (i.e. Eb/N0>6 dB)

-10 0 10 20

1

10-1

10-2

10-3

10-4


Rayleigh fading w/ 16-path uniform PDP,

16QAM, ideal CSI,

NBS=4, NUE=2

Average received Eb/N0 (dB)

No SLM

Aver

age

un

cod

edB

ER

Conv. blind SLM [H]

Proposed {0,135}

Blind SLM, M=64

ML estimation

10-5

STBC-TD

MU-MIMO

-10 0 10 20

1

10-1

10-2

10-3

10-4


Rayleigh fading

w/ 16-path uniform PDP,

64QAM, ideal CSI,

NBS=4, NUE=2

Average received Eb/N0 (dB)

No SLM

Aver

age

un

cod

edB

ER

Conv. blind SLM [H]

Proposed {0,135}

Blind SLM, M=64

ML estimation

10-5

STBC-TD

MU-MIMO

As a result, the use of modified blind SLM is more attractive due to its lower computational complexity at the receiver

Conclusion



A low-complexity ML phase rotation sequence estimation

based on fourth-power constellation was proposed

1. Proposed phase set and ML phase rotation sequence estimation ({0,135}, (I+jQ)4)

Phase rotations {0,135} increases the distance between received symbols with correct and incorrect de-mappings

The use of fourth-order constellation reduces the number of candidates in minimum Euclidean distance calculation, leading to computational complexity reduction

2. Performance evaluation by computer simulation

Computational complexity can be reduced to be 35% (14%) of the conventional ML estimation for 16QAM (64QAM), while achieving a PAPR reduction of 3 dB

No BER degradation when the average received Eb/N0 is sufficiently high

3. Future works

Proposed blind SLM ({0,135}, (I+jQ)4) with 2-step estimation[K]

- Same contribution as ML estimation is expected (i.e., candidates reduction in minimum Euclidean distance searching

ROEC東北大学電気通信研究機構

Research Organization of Electrical Communication

Tohoku University

Thank you for your attention

(Q & A)IEEE 88th Vehicular Technology Conference (VTC2018-Fall)

30 August 2018, Chicago, USA

Amnart Boonkajay Fumiyuki Adachi

Wireless Signal Processing Research Group

Research Organization of Electrical Communication (ROEC), Tohoku University

Acknowledgement:

This work is a part of “The research and development project for realization of the fifth-generation mobile communications system”

commissioned to Tohoku University by The Ministry of Internal Affairs and Communications (MIC), Japan.

Appendix


Next-gen. communication: 5G[A,B]


Enhanced

mobile broadband

(eMBB)

Massive

machine-type

commun. (mMTC)

Ultra-reliable

low latency

commun. (URLLC)

100 Mbps

whenever needed

>10 Gbps

peak data rates

10000x

more traffic

ultra

low cost

10-100x

more devices

10 years

on battery

ultra

reliability

<1 ms

latency

Possible techniques to be used in 5G

(form PHY and MAC perspective)

eMBB

Spatial diversity/multiplexing

Massive MIMO beamforming

Distributed antenna system

Millimeter wave communication

5G NR/LTE dual connectivity

mMTC

NOMA, SCMA

D2D, M2M communication

Guard-band utilization (NB-IoT)

Low-power, large coverage transmission

Integration with wireless power transfer

URLLC

Flexible/scalable subframe structure

New channel coding

LDPC

Polar coding

[A] Nokia, 5G new radio network – white paper, Apr. 2018.

[B] Qualcomm, Making 5G NR a Commercial Reality, Dec. 2017.

a low-complexity phase rotation estimation using …high-papr signal 30 august 2018 vtc2018-fall @...

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