future technology lab., kt

Mismatch Canceller2007
Ballad
268.4788
2Future Technology Lab., KT
Compensation in Communication Receivers
- 100 Mbit/s for high mobility such as mobile access - 1 Gbit/s for low mobility such as nomadic/local wireless access.
IEEE 802.16e reference system: 2x2 downlink, 1x2 uplink
Target throughput for data-only system for baseline antenna configuration shall be NLT 2 x (802.16e)
Normalized peak data rate: 6.5 / 2.8 (bps/Hz) Average user throughput: 3 / 2 (bps/Hz) Cell edge user throughput: 1 / 0.7 (bps/Hz)
TGm
IMT-advanced Proposal
TGLO
Wireless Channel: - Large scale fading: Path loss, lognormal shadowing - Small scale fading: Rayleigh fading, Doppler shift
<Receiver>
Approaches Cell partitioning ICI coordination ICI randomization ICI cancellation Use of relay Cooperative diversity
33 XH
0 123
OFDM transmission
Approaches Interference cancellation, e.g., MAI, ICI channel estimation, equalization, synchronization Space/frequency/time Coding, Spatial multiplexing
°0
°90
Performance measure To prevent quality degradation caused by the impairments of the RF receivers Dynamic range, SNR Low power consumption, Single chip implementation
Approaches Linear Power Amplifier technology DC offset cancellation Image cancellation caused by the I/Q mismatch
Contents
• Impact on receiver performance
Goal of Radio Receiver Design Low-power consumption, Low cost, single-chip implementation
High Performance – dynamic range, sensitivity
Superheterodyne Receiver
Good image rejection, but high Q SAW devices are required.
Alternatives: low-IF/zero-IF Receiver
Image band problem
• Low cost, low power consumption, single chip implementation (small size).
• Software-defined Radio (SDR) – multi-channel reception
RF receiver architectures
Low-IF receiver • Image rejection without use of IR filter. • Hatley, Weaver image-reject architecture
Severe image-band problem due to I/Q mismatch.
Direct conversion receiver • No image band problem, but self-image problem exists.
DC offset, I/Q mismatch
DC Offset Problem in DCRs
Sources of DC offset Self-mixing of LO leakage, interferer and reflected LO leakage.
0 f 0 f
• Time-varying DC offset => DC offset must be estimated realtime.
• Analog circuit technique: capacitive coupling, analog DC remover (Notch filtering)
• Residual DC offset problem : coarse cancellation is not enough.
(Requirement: > 125 dB )
tLOωcos
)(
second, every at sampled signal, digitaldomain - timeThen, .)(Re2)(Let 2 Tetstr tfj RF
cπ=
. and
QIo
LPF
)(tr
)(tF
θπεπ +++−+= tfjgtfgtF cQcI
f
)( fR
LPF
f0
)()()()()( * 00 kXkAkXkBkY −+=)sin()1(cos θφεφ −++= agjag QI
φjaenx =)(
θ
∑ −
=
Impact of I/Q Mismatch on Zero-IF OFDM systems
( SER with 64-QAM when L=1 ) ( SER with 64-QAM when L=12 )
gain: gI
f
)( fRIF
IFets )(2)( +πβ
IF signal : tfjtfj IFIF efZefXfY ππ 22 )()()( −+=
Baseband : )()()( * 00 fQfSfX −+= αβ
over 60 ~ 70 dB (ε < 0.02 dB, θ < 0.1o)
• One can hardly obtain IRR of over 30 dB by circuit design and layout.
General requirement
Previous Works
Data-aided Methods for Zero-IF Receivers Use of DFT and test-tone injection [Churchill, 1981]- Off-line
Adaptive filtering [Cavers, 1993]-AWGN channel
Least Squares (LS) Approach [Sohn, 2002]– IEEE Comm. Letter
Maximum-likelihood (ML) Approach [Gil, 2005]– IEEE Trans. Veh. Technol.
Non-Data-Aided methods Methods for low-IF receivers
Symmetric adaptive decorrelation for low-IF [Li Yu, 1999]
Adaptive blind source separation [Valkama, 2001]
GilGil’’s method for lows method for low--IF [Gil, IF [Gil, 2007]2007]–– IEEEIEEE TSPTSP
Methods for zero-IF receivers Symmetric Adaptive Decorrelation (SAD) for zero-IF [Pun, 2001]
Gil’s methods for zero-IF – submitted to IEEE TVT & TSP
Existing Solutions F.E. Churchill, G. W. Ogar, and B. J. Thomson, “The correction of I and Q errors in a coherent processor,” IEEE Trans. Aerosp. Electron. Sys., vol. 17, pp. 131-137, Jan. 1981.
J. K. Cavers and M. W. Liao, “Adaptive compensation for imbalance and offset losses in direct conversion transceivers,” IEEE Trans. Veh. Technol., vol. 42, pp. 581-588, Nov. 1993.
I. H. Sohn, E. R. Jeong, and Y. H. Lee, “Data-Aided Approach to I/Q Mismatch and DC Offset Compensation in Communication Receivers,” IEEE Commun. Lett., vol. 6, pp. 547-549, Dec. 2002.
G. T. Gil, I. H. Sohn, J. K. Park, and Y. H. Lee, “Joint ML estimation of carrier frequency, channel, I/Q mismatch, and DC offset in communication receivers,” IEEE Trans. Veh. Technol., vol. 54, pp. 338-349, Jan. 2005.
L.Yu and W. M. Snelgrove, “A novel adaptive mismatch cancellation system for quadrature IF radio receivers,” IEEE Trans. Circuits and Sys. II, vol. 46, pp. 789-801, June 1999.
M. Valkama, M. Renfors, and V. Koivunen, “Advanced methods for I/Q imbalance compensation in communication receivers,” IEEE Trans. Signal Processing, vol. 49, pp. 2335-2344, Oct. 2001.
G. T. Gil, Y. D. Kim, and Y. H. Lee, “Non-data-aided approach to I/Q mismatch compensation in low-IF receivers,” IEEE Trans. Signal Processing, July, 2007.
G. T. Gil, ”A novel low-complexity I/Q mismatch compensation technique for zero-IF receivers,” IEEE Trans. Signal Processing, submitted.
Data-aided Methods
Use of DFT and test-tone injection [Churchill] Adaptive filtering [Cavers, 1993] Least Squares Estimation [Sohn] Maximum-likelihood Estimation [Gil]
)(trRF
A/D
A/D
j−
System model for LS & ML method for DCR Baseband receiver structure
Channel )(ta
Channel Estimation
Problem
• Estimate the parameters of I/Q mismatch, DC offset and frequency offset. • Indispensable for reconstruction of the ideal baseband signal. • The three elements slowly change and may be assumed constant for the duration
of a training sequence.
Frame structure 1−+LN
f
)( fR
CfCf−
)( fY
)()( )()( )( ** ooooo
LOC ωωω −=
})({)( t RFo
LOetwLPFtw ω−=
(see p.16)
vnj o
v: normalized freq. offset
desired signal image signal
Signal vector in a quasi-static FIR channel
( ) ( ) NoooooNn nj
oNn nj
)()()( nhnans ∗=
h0h1h2
x, x, x, x, s(0), s(1), s(2), s = A h
( ) Ah)()( 1:0 2 νπν Γ=−= Nn
njens } , , ,1{)( )1(22 −=Γ Njj eediag πνπνν
( ) Nooooooo d 1wwAhAhy +++Γ+Γ= **)()( αβνανβ
( ) 1wwAhAhy ooooooo d++++= ** αβαβ
'ˆˆ hh =u *^ 'ˆ hh α=L
ehh += α*ˆˆ uL
Define and ( ) L T
Least Squares method
ICC NH =which achieves its minimum when the matrix C satisfies
( )122 )( −= CCe HtrE σThe MSE is represented by
Writing CHC in detail as
, *
*
*

TTT
HHH
H
it is easily shown that the optimal preamble satisfies the three conditions given by
,IAA NH =
,* 0AA =H
01A =H
tLOωcos
LPF
LPF
ADC
ADC
DSPChannel
( ) ( ) )()()()( )( * ooooo dnwnxnwnxny ++++= αβ DC offset (1)
desired signal image signal
o e−++=
QIo jddd −=
f
)( fR
, ,)]1(,),1(),0([ where, 1×∈−= N o
T CNyyy why

−−−−
+−
+−−
(2)
L-1 symbols {a-1,...a-L+1} must be known prior to a0. => “precursor”
AWGNFreq. offset
(3)
I/Q mismatch DC offset Channel */ oo βαα = hg oβα )1( 2−=
ooww βα )1( 2−=* oo ddd α−=
Maximum-Likelihood estimation rule
• Maximize the likelihood function given below over candidates of g, d,α, ν
),,,( ναdgΛ
Γ−−−−=
p
ggyp
• First maximize the joint likelihood function with respect to for each possible
),|( Φgyp Φg
Φ
1φ
2φ
3φ
Joint ML estimation procedure
Λ=
Channel estimation 2* )(),,,( gA1yyg νανα Γ−−−−=Λ dd (9)
( ) ))((),,(ˆ *1 1yyAAAg dvvd HHH −−Γ= − αα
DC offset estimation ( )( ) 2*
CI1f )( )()( where,
**
))(()(
( )( ) 2** ˆ,ˆ
))(()(),,,( 1yyfyyCIg gg
ανανα −−−−−=Λ ==
H Ndd
vd (11)
Frequency offset estimation
*1
0
∑ −
= − −=
Two-step search procedure
• Coarse search (FFT-based grid search)
is not a convex function of v because N is a finite number.
)(νΛ
– Compute Λ(v) over a grid of v-values. Determine the location vM of the maximum. – FFT pruning factor (K)
Mνν
Λ=
• Fine search – By use of interpolation, local maximum nearest vM is found.
Frequency selective channel
Parameters
2 1 +×=== θε
MSE in fixed channel
proposed ML
LS method
MLE#1
• Proposed method outperforms the others when Eb/No > 6 dB.
• SNR gain over MLE#1 and LS method : about 2.5 dB at BER of 10-3.
Window size and channel length : N=16, L=4
Non-data-aided Methods for low-IF receivers
Symmetric adaptive decorrelation [Li Yu] Adaptive blind source separation [Valkama, 2001] A NDA method for low-IF receivers [Gil, IEEE TSP] A NDA method for zero-IF receivers [Gil, IEEE Twireless]
nTfj n
nTfj nn
nj non eqesx −+= ( )*22 νπνπ βαβ nj
no nj
non eseqz += −
*/ oo βαα =
desired signal
Adaptive weight- control mechanism
nTj IFe ω−
nTj IFe ω
nj non eqesx −+= ( )*22 νπνπ βαβ nj
no nj
non eseqz += −
• Can cancel the frequency-dependent I/Q mismatch
• An online NDA technique -> Known training sequence is not required.
• Convergence of the SAD algorithm is not verified.
Weak points
• It cannot achieve the optimum estimate because the weight-update equation was derived in somewhat heuristic manner.
• Low convergence rate which is inherent in LMS adaptation with a small fixed stepsize parameter.
I/Q mismatch compensation structure
nx
* nz
Signal reconstruction :
Image reconstruction :
( ) νπβαα nj nonn eqxz 2**2** ||1 −=−
parameter.mismatch I/Q as toreferred is / where * oo βαα =
Basic idea
• {sn|n=0,1,...} and {qn * |n=0,1,...} have zero mean and mutually independent.
0][ ** =nnqsE ( orthogonality )
• To find the value of α that satisfies the orthogonality :
( ) ( ) ( ) ( )[ ] 0 ||1 1][ ****
Revised problem • Assume that {sn} and {qn
* } are asymptotically uncorrelated WSS. • Then {snqn} is ergodic in the mean, and thus
( ) ( )[ ] . largefor 0 1 1
I/Q mismatch estimate
*
n nn N
n nn zxczxb
.|| || and /1 because circle,unit theinside lies 21 * 121 ααααα ≤=
2α
I/Q mismatch estimation structure
Properties
• Upper bound:
ρ ρ/1
( ) ) 1 when (
4 1
]|ˆ[| 22
• Rayleigh fading channel (L=8, fDT = 0.01, uniform power profile)
Simulation Setup
Simulation parameters
• ν = 0.01
( AWGN channel ) ( Rayleigh fading channel )
Symmetric adaptive decorrelation [Pun] NDA block processing for zero-IF receivers [Gil, IEEE TSP] Normalized LMS Self-Image Canceller
Motivation
High complexity of online DA methods High complexity - increased complexity of channel equalization and frequency synchronization at the receiver.
Limited performance of SAD algorithm Error performance depending on the stepsize parameter – error floors, Slow convergence, stability issue
Statistical property of received signals Received signal in random channels can be modeled circular symmetric in general.
Signal model
Baseband signal:
Signal model in OFDM systems
Signal reconstruction structure
Derivation of the estimators
Cramer-Rao Lower Bound
Analytical MSE expression
Simulation model
Simulation parameters
• Three types of channels:
AWGN channel, Flat fading channel, Selective fading channel (L=8, fDT = 0.01,
uniform power profile)
• Two types of transmit signals:
complex white Gaussian signal, OFDM signal (64 subcarriers and CP length=12)
• I/Q mismatch values: ε = 0.1, θ = 10o
• ν = 0.01, SNR = 20 dB
Adaptive weight- control mechanism
Self-image canceller )(ˆ nu)(nyo
Normalized LMS Self-Image Canceller
LMS-ASIC algorithm
Instantaneous error square with w(n+1)
Stepsize that minimizes the error square
Revised weight-update equation (NLMS-ASIC)
Simulation Parameters
Parameters Normalized freq. offset = 0.01, phase offset = 0, SNR = 20 dB DC offset = 0.1 + j0.1
w(0) = 0
Target Performance: MSE < 10-4 until n = 25,000
Two transmit signal Complex Gaussian Signal OFDM signal: NFFT = 64, NCP = 16, QPSK encoded
Channel Types AWGN channel fading channel: fdT = 0.01, flat channel(L = 1), selective channel(L = 8)
A simple DC canceller was employed
Simulation Results
MSE with n for different values of |ε(0)|2 and step-size values
Comparison of the existing methods
LMS filtering, Cavers • • AWGN
LS estimation, Sohn • Low-complexity •
ML estimatoin, Gil • Theoretically optimum DA alg. • High complexity, O(N^2)
SAD, Li Yu/Pun • Noise canceller
•
BSS, Valkama, 2001 • Output SIR SIR • BSS
• Slow convergence
NDA block
processing, Gil
SIR
• square-root operation

Joint ML Estimation based method for DCR

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