30-07-2012, 12:19 PM
An Adaptive LMS Channel Estimation Method for LTE SC-FDMA Systems
[attachment=29481]
Abstract
3rd generation partnership project (3GPP) long term
evolution (LTE) uses single carrier-frequency division multiple
access (SC-FDMA) in uplink transmission and orthogonal
frequency division multiple access (OFDMA) scheme for the
downlink. One of the most important challenges for a transceiver
design is channel estimation (CE) and equalization. In this paper, a
training based least mean square (LMS) CE method is investigated
for a LTE SC-FDMA system. This method can avoid the illconditioned
least square (LS) problem. In addition, this CE
method uses adaptive estimator which is able to update parameters
of the estimator continuously. Simulation results show that the
LMS CE technique with 500 Hz Doppler frequency has around 3
dB better performances compared with 1.5k Hz Doppler
frequency.
Keywords
Channel estimation, least square, LMS, SC-FDMA.
I. INTRODUCTION
The wireless evolution has been stimulated by an explosive
growing demand for a wide variety of high quality of services in
voice, video, and data. This rigorous demand has made an
impact on current and future wireless applications, such as
digital audio/video broadcasting, wireless local area networks
(WLANs), worldwide interoperability for microwave access
(WiMAX), wireless fidelity (WiFi), cognitive radio (CR), and
3rd generation partnership project (3GPP) long term evolution
(LTE) [1], [2]. LTE uses uses single carrier-frequency division
multiple access (SC-FDMA) in uplink transmission and
orthogonal frequency division multiple access (OFDMA)
scheme for the downlink [1]. A highly efficient way to cope
with the frequency selectivity of wideband channel is OFDMA.
OFDMA is an effective technique for combating multipath
fading and for high bit rate transmission over mobile wireless
channels. Channel estimation (CE) has been successfully used to
improve the performance of the LTE OFDMA systems. It can
be employed for the purpose of detecting received signal,
improve signal-to-noise ratio (SNR), channel equalization,
cochannel interference (CCI) rejection, and improved the system
performance [3-5]. The training CE algorithm requires probe
sequences; the receiver can use this probe sequence to
reconstruct the transmitted waveform [6-8]. Training symbols
can be placed either at the beginning of each burst as a preamble
or regularly through the burst. Training sequences are
transmitted at certain positions of the SC-FDMA frequency time
pattern, in its place of data.
Several CE techniques have been proposed for LTE SCFDMA
systems. In [3], the least square (LS) CE has been
proposed to minimize the squared differences between the
received and estimated signal. The LS algorithm, which is
independent of the channel model, is commonly used in
equalization and filtering applications. But the statistics of
channels in real world change over time. Another limitation that
is encountered in the straight application of the LS estimator is
that the inversion of the large dimensional square matrix turns
out to be ill-conditioned. Two-dimensional based on Wiener
filtering pilot symbol aided CE has been proposed [4]. Although
it exhibits the best performance among the existing linear
algorithms in literature, it requires accurate knowledge of
second order channel statistics, which is not always feasible at a
mobile receiver. This estimator gives almost the same result as
1D estimator, but it requires higher complexity. To further
improve the accuracy of the estimator, Wiener filtering based
iterative CE has been investigated [4], [7]. However, this
scheme also requires high complexity and knowledge of channel
correlations [9-12].
Adaptive CE has been, and still is, an area of active research
topics, playing imperative roles in an ever growing number of
applications such as wireless communications where the channel
is rapidly time-varying. Signal processing techniques that use
recursively estimated, time varying models are normally called
adaptive. Different adaptive CE algorithms have been proposed
over the years for the purpose of updating the channel
coefficient. The least mean square (LMS) method, its
normalized version (NLMS), the affine projection algorithm
(APA), as well as the recursive least square (RLS) method are
well known examples of such CE algorithms. The well known
LMS/NLMS CE algorithms are attractive from a computational
complexity point of view but their convergence behavior for
highly correlated input signals is poor. The RLS CE method
resolves this trouble, but at the expense of increased complexity.
A very large number of fast RLS CE methods have been
developed over the years, but regrettably, it seems that the better
a fast RLS CE method is in terms of computational efficiency
and numerical stability [13-15]. In addition, the RLS algorithm
has the recursive inversion of an estimate of the autocorrelation
matrix of the input signal as its cornerstone, problems arise, if
the autocorrelation matrix is rank deficient.
International Journal of Engineering & Technology IJET-IJENS Vol: 10 No: 05 16
In this paper, we investigate the adaptive LMS CE method
in the LTE SC-FDMA systems. This CE method uses adaptive
estimator which is able to update parameters of the estimator
continuously, so that knowledge of channel and noise statistics
are not required. This LMS CE algorithm requires knowledge of
the received signal only. This can be done in a digital
communication system by periodically transmitting a training
sequence that is known to the receiver. Simulation results show
that the LMS CE scheme with 500 Hz Doppler frequency has 3
dB better performances compared with 1.5 kHz Doppler
frequency.
We use the following notations throughout this paper: bold
face lower letter is used to represent vector. Superscripts x* and
xT denote the conjugate and conjugate transpose of the complex
vector x respectively.
The remainder of the paper is organized as follows: section II
describes LTE SC-FDMA systems model. The adaptive LMS
CE method is presented in section III, and its performance is
analyzed in section IV. Finally, conclusions are made in section
[attachment=29481]
Abstract
3rd generation partnership project (3GPP) long term
evolution (LTE) uses single carrier-frequency division multiple
access (SC-FDMA) in uplink transmission and orthogonal
frequency division multiple access (OFDMA) scheme for the
downlink. One of the most important challenges for a transceiver
design is channel estimation (CE) and equalization. In this paper, a
training based least mean square (LMS) CE method is investigated
for a LTE SC-FDMA system. This method can avoid the illconditioned
least square (LS) problem. In addition, this CE
method uses adaptive estimator which is able to update parameters
of the estimator continuously. Simulation results show that the
LMS CE technique with 500 Hz Doppler frequency has around 3
dB better performances compared with 1.5k Hz Doppler
frequency.
Keywords
Channel estimation, least square, LMS, SC-FDMA.
I. INTRODUCTION
The wireless evolution has been stimulated by an explosive
growing demand for a wide variety of high quality of services in
voice, video, and data. This rigorous demand has made an
impact on current and future wireless applications, such as
digital audio/video broadcasting, wireless local area networks
(WLANs), worldwide interoperability for microwave access
(WiMAX), wireless fidelity (WiFi), cognitive radio (CR), and
3rd generation partnership project (3GPP) long term evolution
(LTE) [1], [2]. LTE uses uses single carrier-frequency division
multiple access (SC-FDMA) in uplink transmission and
orthogonal frequency division multiple access (OFDMA)
scheme for the downlink [1]. A highly efficient way to cope
with the frequency selectivity of wideband channel is OFDMA.
OFDMA is an effective technique for combating multipath
fading and for high bit rate transmission over mobile wireless
channels. Channel estimation (CE) has been successfully used to
improve the performance of the LTE OFDMA systems. It can
be employed for the purpose of detecting received signal,
improve signal-to-noise ratio (SNR), channel equalization,
cochannel interference (CCI) rejection, and improved the system
performance [3-5]. The training CE algorithm requires probe
sequences; the receiver can use this probe sequence to
reconstruct the transmitted waveform [6-8]. Training symbols
can be placed either at the beginning of each burst as a preamble
or regularly through the burst. Training sequences are
transmitted at certain positions of the SC-FDMA frequency time
pattern, in its place of data.
Several CE techniques have been proposed for LTE SCFDMA
systems. In [3], the least square (LS) CE has been
proposed to minimize the squared differences between the
received and estimated signal. The LS algorithm, which is
independent of the channel model, is commonly used in
equalization and filtering applications. But the statistics of
channels in real world change over time. Another limitation that
is encountered in the straight application of the LS estimator is
that the inversion of the large dimensional square matrix turns
out to be ill-conditioned. Two-dimensional based on Wiener
filtering pilot symbol aided CE has been proposed [4]. Although
it exhibits the best performance among the existing linear
algorithms in literature, it requires accurate knowledge of
second order channel statistics, which is not always feasible at a
mobile receiver. This estimator gives almost the same result as
1D estimator, but it requires higher complexity. To further
improve the accuracy of the estimator, Wiener filtering based
iterative CE has been investigated [4], [7]. However, this
scheme also requires high complexity and knowledge of channel
correlations [9-12].
Adaptive CE has been, and still is, an area of active research
topics, playing imperative roles in an ever growing number of
applications such as wireless communications where the channel
is rapidly time-varying. Signal processing techniques that use
recursively estimated, time varying models are normally called
adaptive. Different adaptive CE algorithms have been proposed
over the years for the purpose of updating the channel
coefficient. The least mean square (LMS) method, its
normalized version (NLMS), the affine projection algorithm
(APA), as well as the recursive least square (RLS) method are
well known examples of such CE algorithms. The well known
LMS/NLMS CE algorithms are attractive from a computational
complexity point of view but their convergence behavior for
highly correlated input signals is poor. The RLS CE method
resolves this trouble, but at the expense of increased complexity.
A very large number of fast RLS CE methods have been
developed over the years, but regrettably, it seems that the better
a fast RLS CE method is in terms of computational efficiency
and numerical stability [13-15]. In addition, the RLS algorithm
has the recursive inversion of an estimate of the autocorrelation
matrix of the input signal as its cornerstone, problems arise, if
the autocorrelation matrix is rank deficient.
International Journal of Engineering & Technology IJET-IJENS Vol: 10 No: 05 16
In this paper, we investigate the adaptive LMS CE method
in the LTE SC-FDMA systems. This CE method uses adaptive
estimator which is able to update parameters of the estimator
continuously, so that knowledge of channel and noise statistics
are not required. This LMS CE algorithm requires knowledge of
the received signal only. This can be done in a digital
communication system by periodically transmitting a training
sequence that is known to the receiver. Simulation results show
that the LMS CE scheme with 500 Hz Doppler frequency has 3
dB better performances compared with 1.5 kHz Doppler
frequency.
We use the following notations throughout this paper: bold
face lower letter is used to represent vector. Superscripts x* and
xT denote the conjugate and conjugate transpose of the complex
vector x respectively.
The remainder of the paper is organized as follows: section II
describes LTE SC-FDMA systems model. The adaptive LMS
CE method is presented in section III, and its performance is
analyzed in section IV. Finally, conclusions are made in section