4G Research Problem Seminar DUT

1.Introduction2.Physical layer

3.Literature Review of Channel Estimation in Wireless Communications4.Basedband System Model of MIMO-OFDM

5.Channel Estimation techniques6.Channel Estimation in LTE systems with high-speed mobile users

7.Channel Estimation in Multi-hop Communications8.Possible research problems and outcomes

Research Problems in 4G (LTE) Mobile Communications

Nguyen Le Hung

April 2010

Nguyen Le Hung Research Problems in 4G (LTE) Mobile Communications





1.1.Needs and trends1.2.Development of mobile communications1.3.Multiuser transmission techniques in 4G (LTE) systems

1.1.Needs and trends

! multimedia services: Voice, Video distribution, Real-time videoconferencing, Data,… for both business and residential customers:

! Explosive traffic growth

! Internet growth, VoIP, VideoIP, IPTV

! Cell phone popularity worldwide

! Ubiquitous communication for people and devices

! Emerging systems opening new applications

! Unified network: Single distributed network, multiple services, packet architecture

Extracted from Digital Communication lecture notes, McGill Uni.







1.2.Development of mobile communications

time

code

frequency

code

space

FDMA (1G)e.g., AMPS ~ 1980s

TDMA (2G)e.g., GSM ~ 1990s

SDMA (4G)e.g., LTE ~ 2010s

CDMA (3G)e.g., W-CDMA ~ 2000s

frequency

time

time

~ 1 Gbps (stationary),

~ 100 Mbps (mobile)

frequency

frequency

~ 14 Mbps (downlink),

~ 5.8 Mbps (uplink)~ 50 Kbps

A new signal dimension will be exploited in 5G ?







1.3.Multiuser transmission techniques in 4G (LTE) systems

Broadband communications

LTE (4G) system

Broadband communications(high data rate and reliability)

Diversity

Time Freq.SignalSpace

Multi-user

Space

Multipath channel Modeling

CSI feedback

Analog Digital

Vectorquantization

g

Quasi-static Time-variant

BEMs AR

LBG

Grassmannian

Random

Scheduling Precoding

Exhaustivesearch

Greed or iterativesearch

Linearmethods

Non-linearmethods

Codebook-based ones

MMSE BD DPC THP PU2RC

Randomuser selection

VP






2.1.OSI layers2.2.Basis elements of a digital communication system

2.1.OSI layers

7. Application

6. Presentation

5. Session

4. Transport

3. Network

2. Data link

1. Physical

7. Application

6. Presentation

5. Session

4. Transport

3. Network

2. Data link

1. PhysicalCDMA, OFDM, SC-FDMA







An example: MIMO-OFDM transmitter/receiver

Burst structure

Clk RF

Pilot OFDM symbols

Data OFDM symbols S/P IFFT Insert CP DAC RF

Clk

Osc

RF

LO

P/S

ci

MQAM

mapping

Information

bits, ui

Pilot insertionConv.

Encoder

i

S/P

MQAM

mappingS/P IFFT Insert CP DAC RFP/S

Burst-mode OFDM transmitter.

RF

RF

LO

ADC S/P

CFO

compensation

Clk

Osc

FFTCP

Burst-mode OFDM receiver

MIMO

demapper

RF ADC S/P

FFT

P/SSISO

decoder S/P -1

RF ADC S/P

FFT

Hard

decision

CP

Estimation of CIR/CFO

Soft

FFT

decisionCFR

CIR

CFO

mapper

0 2 4 6 810

−5

10−4

10−3

10−2

10−1

100

Signal−to−Noise Ratio (dB)

Bit

Err

or R

ate

(BE

R)

"Quasi−static" assumptionTime−variant channel modeling

user speed of 100km/h2x2 MIMO−OFDM (LTE downlink)


hung

Highlight






2.2.Basis elements of a digital communication system

Source

encoder

Channel

encoder

Digital

modulation

Channel

Source

decoder

Channel

decoder

Digital

demodulation

S

h

r = Sh + n

Pilot

S

Data

S

Data

S

Pilot

S

Data

S

Data

S

Pilot

S

h h h h’ h’ h’






3.Literature Review of Channel Estimation in Wireless Communications

Detection/decoding

in communications3 dB f

Rx signal

vector

Tx signal

matrix

CIR

vector

Rx noise

vector

Noncoherent Coherentwithout using CSI3-dB performance

loss

use CSI

require Channel Estimation (CE)

vector matrix vector

(CSI)

vector

r = Sh + nrequire Channel Estimation (CE)

with channel parameters as:

Deterministic unknowns Random variables

Fisher approaches:

LS ML

Bayesian approaches:

MMSE MAPLS, ML,… MMSE, MAP,…

Multipath fading channel (freq. selective) in multi-carrier transmissions (e.g.,OFDM)

Time-invariant (quasi-static) Time-variant (Time-selective)

Perfect

Synch.

Imperfect

Synch.

Channel Estimation (CE)

Blind Pilot Semi-blind

Joint CE and Synch.

Semi-blind

Perfect

Synch.Imperfect

Synch.

Channel Estimation (CE)

PilotJoint CE and Synch.

Semi-blindPilot

Pilot design to minimize:

MSE CRLB


MSE CRLB


MSE BCRLB

Turbo-based

Decision-direct.

MSE CRLB MSE CRLB MSE BCRLB






Time-varying multipath propagation

reflection and diffraction

Extracted from Digital Communication lecture notes, McGill Uni.






Doubly selective channels▶ The last decade has witnessed numerous intensive studies in employing

orthogonal frequency division multiplexing (OFDM) for broadbandcommunication systems to exploit its high spectral efficiency androbustness against multipath (frequency-selective) fading channels. In thecurrent literature, most of these studies have assumed frequency-selectivechannels to be time-invariant (i.e., quasi-static or block-fading) within atransmission burst. This channel assumption can be used in a wirelesssystem with stationary and/or low-speed users.

▶ In a wireless network with rapidly moving nodes (e.g., users in cars andtrains in 4G-LTE systems), the resulting time-variation (time-selectivity) ofthe channel impulse response (CIR) introduces a large number of channelparameters (much greater than that of quasi-static/block-fadingchannels). In addition, the time-variation of the channel leads to a loss ofsubcarrier orthogonality, resulting in inter-carrier interference (ICI) inOFDM receivers. Under such a scenario, the assumption of quasi-staticfading channels becomes inappropriate. As a result, time- andfrequency-selective (doubly selective) channels should be considered in thewireless system investigation and analysis.






4.1.Transmitted signal4.2.Doubly selective channel modeling using basis expansion model (BEM)4.3.Received signal4.4.Numerical results of time-variant channels under LTE system settings

4.1.Transmitted signal

Consider an uncoded MIMO-OFDM system using Nt transmit antennas andN-point fast Fourier transform (FFT). After inverse FFT (IFFT) and cyclicprefix (CP) insertion, the transmitted baseband signal of the mth OFDMsymbol at the uth transmit antenna can be written as

x(u)n,m =

1√N

N−1∑

k=0

X(u)k,m exp

(j2�kn

N

), (1)

where n ∈ {−Ng, ..., 0, ..., N − 1}, Ng denotes the CP length, X(u)k,m is the kth

data-modulated (or pilot) subcarrier in the mth OFDM symbol from the uthtransmit antenna.







4.2.Doubly selective channel modeling using basis expansion model (BEM)For the pair of the uth transmit antenna and the rth receive antenna, the lth(time-varying) channel tap gain that includes the effect of transmit-receive

filters and doubly selective propagation is denoted by ℎ(r,u)l,n,m where n and m

stand for the indices of the time-domain sample and OFDM symbol,respectively. With the aid of the BEMs, the lth time-variant channel tap gain

at the nth time instance in the mth OFDM symbol (after CP removal) can berepresented as

ℎ(r,u)l,n,m =

Q∑

q=1

bn+Ng+mNs,q c(r,u)q,l , l ∈ {0, ..., L− 1}, (2)

where Ns = N +Ng denotes the OFDM symbol length after CP insertion,n = 0, ..., N − 1, m = 0, ...,M − 1 and M is the number of both data andpilot OFDM symbols in a burst. The mobile user speed is assumed to beunchanged within a burst of M OFDM symbols. L denotes the channel length.bn+Ng+mNs,q stand for the qth basis function values of the used BEM. c

(r,u)q,l

are the BEM coefficients of the channel modeling. Q is the number of basisfunctions used in the basis expansion modeling.







4.2.Doubly selective channel modeling using BEM (cont.)

To exploit the benefits of BEMs in reducing the number of channel parametersto be estimated, one can consider pilot-aided estimation of BEM coefficients(instead of time-variant CIR). In particular, for the pair of the uth transmitantenna and the rth receive antenna, the lth time-variant channel tap gainscorresponding to the pilot OFDM symbol at the position mp in a burst can beexpressed in a vector form as follows

h(r,u)l,mp

= Bmpc(r,u)l , (3)

where h(r,u)l,mp

=[ℎ(r,u)l,0,mp

, ..., ℎ(r,u)l,N−1,mp

]T, Bmp =

[bmp,1, ...,bmp,Q

],

bmp,q =[bNg+mpNs,q , ..., bNs−1+mpNs,q

]Tand c

(r,u)l =

[c(r,u)1,l , ..., c

(r,u)Q,l

]T.







4.2.Doubly selective channel modeling using BEM (cont.)

For a group of P pilot OFDM symbols in a transmission burst, a vectorrepresentation of all related time-variant channel tap gains can be expressed by

h(r,u) = BLc

(r,u), (4)

where h(r,u) =

[[h(r,u)0

]T, ...,

[h(r,u)L−1

]T]T

,

h(r,u)l =

[[h(r,u)l,m1

]T, ...,

[h(r,u)l,mp

]T, ...,

[h(r,u)l,mP

]T ]T, BL = IL ⊗B,

B =[BT

m1, ...,BT

mp, ...,BT

mP

]Tand c(r,u) =

[[c(r,u)0

]T, ...,

[c(r,u)L−1

]T ]T.

Given h(r,u) and (4), one can obtain actual values of c(r,u) (for later evaluationof the considered BEM coefficient estimation) by

c(r,u) =(BH

L BL

)−1BH

L h(r,u).







4.3.Received signal

Over the aforementioned doubly-selective channels, after CP removal, the nthreceived sample in the mth OFDM symbol at the rth receive antenna can berepresented by

y(r)n,m =

Nt∑

u=1

L−1∑

l=0

ℎ(r,u)l,n,mx

(u)n−l,m + z

(r)n,m, (5)

where n = 0, ..., N − 1 and zn,m is the additive white Gaussian noise (AWGN)with variance No. T is the sampling period of the system.







Time-variant path gain under mobile user speed of 5 km/h in a LTE system

0 1 2 3 4 5 6 7 8 9 10 11 12 13 141

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

Time (in OFDM symbol duration)

Abs

olut

e va

lue

of a

mpl

itude

of o

ne p

ath

gain

hl Mobile user speed = 5 km/h,

fc = 2 GHz,

128−FFT, CP length = 10,fs = 1.92 MHz,

2 time slots in LTE are considered,Jakes model is considered.

pilot OFDM symbol for channel estimation







Time-variant path gain under mobile user speed of 50 km/h in a LTEsystem

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140.95

1

1.05

1.1

1.15


Abs

olut

e va

lue

of a

mpl

itude

of o

ne p

ath

gain

hl Mobile user speed = 50 km/h,

fc = 2 GHz,

128−FFT, CP length = 10,fs = 1.92 MHz,

2 time slots in LTE are considered,Jakes model is considered

Data OFDM symbol







Time-variant path gain under mobile user speed of 300 km/h in a LTEsystem

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140.8

0.9

1

1.1

1.2

1.3


Abs

olut

e va

lue

of a

mpl

itude

of o

ne fa

ding

gai

n h

l Mobile user speed = 300 km/h,fc = 2 Ghz, 128−FFT, CP length = 10, f

s = 1.92 Mhz,

2 time slots in LTE are considered,Jakes model is considered.

Data OFDM symbol







BEM-based fitting of time-variant path gain using 8 DPS basis functions

0 1 ms0

0.5

1

1.5

2

2.5

Time (one LTE frame of 140 OFDM symbols)

Abs

olut

e va

lue

of a

mpl

itude

of o

ne p

ath

gain

hl

Mobile user speed of 200 km/h,fc = 2GHz, f

s = 1.92 MHz, Jakes model is considered,

one LTE frame of 140 OFDM symbols is considered.

Time−varying path gain

BEM using 8 DPS basis functions







BEM-based fitting of time-variant path gain using 10 DPS basis functions

0 1 ms0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Time (one LTE frame of 140 OFDM symbols)

Abs

olut

e va

lue

of a

mpl

itude

of o

ne p

ath

gain Mobile user speed of 200 km/h,

fc = 2GHz, f

s = 1.92 MHz, Jakes model,

Time−varying path gain

BEM using 10 DPSbasis functions







A possible use of BEM in stock market analysis

0 50 100 150 200 2500

10

20

30

40

50

60

70

80

90

100

Time

VC

G S

tock

Pric

e

Actual pricesBEM−based approximation using 30 DPS basis functions







Normalized MSE of DPS-BEM-based fitting of time-variant channelsgenerated by Jakes model under different mobile speeds in a LTE system.

1 2 3 4 5 6 7 8 9 1010

−30

10−25

10−20

10−15

10−10

10−5

100

Number of used basis functions

MS

E o

f DP

S−

base

d ch

anne

l fitt

ing

1 km/h5 km/h10 km/h20 km/h30 km/h40 km/h50 km/h60 km/h70 km/h80 km/h90 km/h100 km/h






5.1.Bayesian approach5.2.Fisher approach

5.1.Bayesian approach: vector representation of received signalsIn Bayesian estimation, channel parameters are treated as random variables(with known statistics) to be estimated. The received samples corresponding toP pilot OFDM symbols can be represented in a vector form as follows:

y = Sc+ z, (6)

where y =[yTm1

, ...,yTmP

]T, ymp =

[[y(1)mp

]T, ...,

[y(Nr)mp

]T ]Tand

y(r)mp =

[y(r)0,mp

, ..., y(r)N−1,mp

]T. z =

[zTm1

, ..., zTmP

]T,

zmp =

[[z(1)mp

]T, ...,

[z(Nr)mp

]T]T

and z(r)mp =

[z(r)0,mp

, ..., z(r)N−1,mp

]T.

S =[STm1

, ...,STmP

]T, Smp =

[S(1)mp , ...,S

(Nt)mp

]T,

S(u)mp =

[s(u)0,mp

Bmp , ..., s(u)L−1,mp

Bmp

],

s(u)l,mp

= diag([

x(u)0−l,mp

, ...x(u)N−1−l,mp

]), c =

[[c(1)

]T, ...,

[c(Nr)

]T ]T

,

c(r) =

[[c(r,1)

]T, ...,

[c(r,Nt)

]T ]T.







5.1.Bayesian approach: MAP technique

Based on the Bayesian approach and the received samples (6) in the timedomain, one can estimate BEM coefficients by using the MAP estimationprinciple. In particular, the MAP-based estimates of BEM coefficients can bedetermined as follows

c = argmaxc

ln p(c∣y), (7)

where p(c∣y) = p(y∣c)p(c)p(y)

.Hence, the MAP estimates of BEM coefficients are given by

c = argmaxc

ln [p(y∣c)p(c)] , (8)

where p(y∣c) = 1�NP ∣Rz∣

exp(− [y − Sc]H R−1

z [y − Sc]), Rz = E

(zzH

),

p(c) = 1�QL∣Rc∣

exp(−cHR−1

c c)and Rc = E

(ccH

).







5.1.Bayesian approach: MAP technique (cont.)

After some straightforward manipulations, the MAP estimates of BEMcoefficients can be obtained by

c = argminc

fMAP (c), (9)

where fMAP (c) =1

No∥y − Sc∥2 + cHR−1

c c.

Setting the gradient vector of fMAP (c) with respect to cH to zero yields thefollowing MAP estimates of BEM coefficients

c =(SHS+R

−1c No

)−1

SHy. (10)







An example: MSE performance of MAP-based BEM coefficient estimationunder LTE system settings.

0 5 10 15 20 25 3010

−5

10−4

10−3

10−2

10−1

SNR(dB)

MS

E o

f BE

M c

oeffi

cien

t est

imat

es

a: CE−based MAPb: GCE−based MAPc: DPS−based MAPd: KL−based MAPe: DPS−based BCRB

Figure: Normalized MSE results of BEM coefficient estimates under mobile speed of40 km/h and the use of various BEMs.







5.2.Fisher approach: ML technique

Unlike Bayesian approach, Fisher estimation (e.g., ML) treats channelparameters as deterministic unknowns to be estimated. In particular, the MLestimates of BEM coefficients can be obtained by

c = argmaxc

ln p(y∣c), (11)

where p(y∣c) = 1�NP ∣Rz∣

exp(− [y − Sc]H R−1

z [y − Sc]),

Rz = E(zzH

)= NoI.

After some manipulations (similar to the aforementioned MAP formulations),the ML estimates of BEM coefficients can be determined by

c =(SHS)−1

SHy. (12)

It is noted that the MAP estimates of c is c =(SHS+R−1

c No

)−1SHy






6.1 Over SDMA/OFDMA downlink6.2 Over SC-FDMA uplink

6.1 Channel estimation over SDMA/OFDMA downlink in a LTE system

Subcarrier in OFDMA

Space Division Multiple Access

(SDMA)

Orthogonal Frequency Division Multiple Access

(OFDMA)

A low-complexity channel estimation algorithm operating under low SNRconditions is desirable !Low-overhead placement of pilot OFDM symbols is of practical importance !






6.1 Over SDMA/OFDMA downlink6.2 Over SC-FDMA uplink

6.2 Channel estimation over SC-FDMA uplink in a LTE system

N-point

DFTSubcarrier

Mapping

M-point

IDFT

Add CP/

PSDAC/RF

MQAM

mapping

Binary

bits

User K

N-point

DFTSubcarrier

Mapping

M-point

IDFT

Add CP/

PSDAC/RF

MQAM

mapping

Binary

bits

User 1

K=M/N

N-point

IDFTSubcarrier

De-mapping

M-point

DFT

Remove

CPRF/ADC

MQAM

De-mapping

Binary

bits

Base Station

A low-complexity channel estimation algorithm operating under low SNRconditions is desirable !






7.1. Single-hop communications7.2. Relay/User-cooperation (multi-hop) communications

7.1. Single-hop communications

Source

Destination






7.1. Single-hop communications7.2. Relay/User-cooperation (multi-hop) communications

7.2. Relay/User-cooperation (multi-hop) communications

Relay (AF/DF)

Destination

Source 1

Source 2

User

cooperation

The 1st hop

The 2nd hop

The 2nd hopThe 1st hop






8.1.Over SC-FDMA channels (LTE uplink)8.2.Over relay/user-cooperation channels (multi-hop communications)

8.1.Possible research problems and expected outcomes in DSCE overSC-FDMA channels

▶ In the current literature, the problem of doubly selective channelestimation (DSCE) over OFDM channels (e.g., LTE downlink) has beenaddressed in many existing papers while DSCE over SC-FDMA channels(e.g., LTE uplink) has received a little attention. As a result, DSCE overSC-FDMA channels would need more studies. In particular, one couldconsider pilot-aided DSCE using Fisher/Bayesian approach over SC-FDMAchannels.

▶ The target is to develop a low-complexity DSCE algorithm using alow-overhead pilot placement that can provide highly accurate channelestimates in low SNR regimes (e.g., practical coded transmissionconditions).

▶ The research results could be submitted to an IEEE conference.







8.2.Possible research problems and expected outcomes in DSCE formulti-hop systems

▶ Most of studies in relay/user-cooperation (multi-hop) systems haveassumed that perfect channel estimation has been established at receivers.Recently, a few papers have considered channel estimation in multi-hopsystems but the considered channels are usually assumed to betime-invariant (i.e., quasi-static or block-fading).

▶ As a result, time- and frequency-selective (doubly selective) channelestimation in multi-hop systems could be an interesting research problem.For instance, in relay systems, one can consider DSCE foramplify-and-forward (AF) and/or decode-and-forward (DF) modes.

▶ The research results could be submitted to an IEEE conference.

▶ Low-overhead (optimal) pilot design for DSCE in multi-hopcommunications would be of practical importance. The correspondingresearch results could be considered for a journal submission.







References

Zijian Tang, Rocco Claudio Cannizzaro, Geert Leus and Paolo Banelli,“Pilot-assisted time-varying channel estimation for OFDM systems”, IEEETrans. Signal Processing, pp. 2226-2238, vol. 55, no. 5, May 2007.

Yahong Rosa Zheng and Chengshan Xiao, “Simulation models withcorrect statistical properties for Rayleigh fading channels,” IEEE Trans.

Communi, vol. 51, no. 6, June 2003.

Robert Love, Ravi Kuchibhotla, Amitava Ghosh, Rapeepat Ratasuk,Weimin Xiao, Brian Classon, Yufei Blankenship, “Downlink controlchannel design for 3GPP LTE,” in Proc. Wireless Commu. Network Conf.,pp. 813-818, 2008.

Thomas Zemen, and Christoph F. Mecklenbrauker,“Time-variant channelestimation using discrete prolate spheroidal sequences,” IEEE Trans. on

Signal Processing, vol. 53, no. 9, pp. 3597-3607, Sept. 2005.







Q&A

▶ Detailed questions can be sent to [email protected]

▶ Thank you for attending this seminar.


Documents

4G Research Problem Seminar DUT