International Journal of Digital Multimedia Broadcasting

Volume 2008 (2008), Article ID 509171, 7 pages

http://dx.doi.org/10.1155/2008/509171

## Block Iterative/Adaptive Frequency-Domain Channel Estimation for Cyclic-Prefixed Single-Carrier Broadband Wireless Systems

^{1}Channel Laboratory, Samsung Electronics Co. Ltd., Suwon 443-742, South Korea^{2}Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea

Received 27 April 2008; Revised 13 July 2008; Accepted 11 September 2008

Academic Editor: Fred Daneshgaran

Copyright © 2008 Jong-Seob Baek and Jong-Soo Seo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper presents a new block iterative/adaptive frequency-domain channel estimation scheme, in which a channel frequency response (CFR) is estimated iteratively by the proposed weighted element-wise block adaptive frequency-domain channel estimation (WEB-CE) scheme using the soft information obtained by a soft-input soft-output (SISO) decoder. In the WEB-CE, an equalizer coefficient is calculated by minimizing a weighted conditional squared-norm of the a posteriori error vector with respect to its correction term. Simulation results verify the superiority of the WEB-CE in a time-varying typical urban (TU) channel.

#### 1. Introduction

Cyclic-prefixed single-carrier frequency-domain equalization (SC-FDE) has received enormous attention in recent years because of its efficient implementation and low peak-to-average power ratio (PAPR) characteristics over broadband wireless channels. On the other hand, it is noticed that most works have been performed with the assumption that a channel frequency response (CFR) is perfectly known to the receiver [1, 2]. In practice, this assumption is not valid since a real channel is unknown and time-varying. This, it highlights the need for the precise estimation of CFR. In [3, 4], an adaptive channel estimation scheme using the hard information from the decision was presented. In [5], an iterative (nonadaptive) channel estimation scheme using the soft information obtained from a soft-input soft-output (SISO) decoder was presented. Recently, an iterative/adaptive scheme which performs a channel estimation iteratively by employing an adaptive algorithm using the soft information was proposed in [6]. This work concludes that the iterative/adaptive approach is adequate to support a good channel tracking performance over time-varying channels. However, the scheme has not been applied to the SC-FDE. In this correspondence, an iterative/adaptive channel estimation scheme for the SC-FDE is studied. Moreover, a new block-type channel estimation scheme is studied in order to provide a better channel tracking performance.

In this paper, we propose a block iterative/adaptive
CFR estimation scheme, in which a weighted element-wise block adaptive
frequency-domain channel estimation (WEB-CE) using the soft information
obtained from the SISO decoder is presented. It is found that the WEB-CE has a
flexibility over the element-wise block length as compared to an approximated
recursive least square (RLS)-CE of [6] by applying a weighted conditional least-square (LS)
criterion formulated with the *a posteriori error* vector [7]. Moreover, mean square error
(MSE) of the WEB-CE with respect to the element-wise block length is analyzed.
Simulation results show that the WEB-CE yields good performance in a typical
urban (TU) channel as the iteration number and element-wise block length
increase.

The paper is organized as follows. The next section describes the system model. In Section 3, the derivation procedure and property of the proposed CFR estimation scheme are discussed. Simulation results are discussed in Section 4, and conclusions are drawn in Section 5.

*Notation*

and denote an expectation and a transpose
operator, respectively. denotes complex conjugates of a symbol,
vector, and matrix. , ,
and denote the identity matrix of size ,
zero matrix of size ,
and diagonal matrix, respectively.

#### 2. SC-FDE System Model

##### 2.1. SC-FDE Transmitter

The transmitter of SC-FDE is shown in Figure 1. The blocks of data bits are encoded by the convolutional encoder. The code bits are interleaved by an interleaver , . QPSK mapper maps a pair of input bits to a symbol from the symbol alphabet . (In this paper, a constant-amplitude PSK constellation is assumed.) Then, a block of signals is structured with block length of and a cyclic-prefix (CP) is inserted between blocks to prevent an interblock interference (IBI) at block time . A training block (TB) that has block length of is inserted every data frame that consists of data blocks in order to initialize the channel coefficients and track the channel variations.

##### 2.2. SC-FDE Receiver

Suppose that an overall channel impulse response (CIR) containing the combined effects of transmit and receive filters, multipath fading and sampling is , where is the total length of the CIR, at a block time , and it is constant within one block. It is also supposed that the CP length is equal or larger than . At the receiver, discarding the CP, the time-domain received block can be expressed in a matrix form aswhere is an additive white noise vector, and is a circular matrix. It is assumed that , , and , where and denote the signal power and noise variance, respectively. Then, the discrete-time Fourier transform (DFT) matrix (for ) yields the frequency-domain block as follows:where , , , and , where represents the channel frequency response at the th frequency bin.

From (2), the frequency-domain estimate of the transmitted data block, , is performed by employing FDE at a block time . In this paper, a zero-forcing FDE (ZF-FDE) is considered. (Note that the minimum-mean-square error (MMSE) equalization can be used in place of the ZF equalization.) The received data at the th frequency bin is described asAfter the inverse DFT (IDFT) operation, an extrinsic log-likelihood ratio (LLR) of a coded bit is computed [8]. The extrinsic LLR value is de-interleaved and fed to an SISO decoder. Then, the SISO decoder outputs the a priori LLR, which is interleaved to compute the soft information, that is, a priori mean vector , in which is defined as [8]where denotes a symbol . Correspondingly, the DFT of the soft information such as the a priori mean vector is given byThe soft information is used to estimate in (3).

#### 3. Iterative/Adaptive CFR Estimation Scheme

##### 3.1. WEB-CE Using the Soft Information

The coefficient update algorithm of the WEB-CE at th frequency bin
is expressed as [7]where is the correction term of .
From (2), we define input vectors at a block time as follows:where is the element-wise block length of the
WEB-CE. In (7), it is noticed that may actually not be known to the receiver,
thus is alternatively defined as [6]where and ,
whose element has zero mean and
variance given byIn addition, it is assumed that is independent of .
Furthermore, we define *a priori error* vector and *a posteriori error* vector as follows [7]:From (7)–(10), *a posteriori
error* vector-based weighted conditional LS criterion is defined as
[6, 7]where a squared norm of is attenuated geometrically by diagonal matrix with the forgetting factor that satisfies .
Using (8) and (10), (11) can be rewritten asIn (12), using (8), the second
term is computed asUsing (9), can be expressed aswhere denotes the th row vector of .
In (12), using (8) and (10), the third term is given byFurthermore, assuming [6], in (15) can be expressed asThen, (15) can be rewritten
asThe computation of the fourth
term in (12) can be done similarly as that of the third term as
follows:Finally, substituting the
results of (13), (14), (17), and (18) into (12), and then minimizing with respect to , in (6) is computed:where *a priori soft error* vector is defined asFrom (19), is given byConsequently, substituting (21)
into (6), we can formulate WEB-CE algorithm as follows:The overall iterative receive
structure employing the WEB-CE is depicted in Figure 2.

##### 3.2. Properties of WEB-CE

(1) *Flexibility*: In (14), (21),
and (22), assuming ,
it is found that the WEB-CE is similar to the approximated RLS-CE (or least
mean square (LMS)-CE) of [6] defined aswhere ,
and .
As a result, we can see that the WEB-CE has flexibility over the block length as compared to the approximated RLS-CE.

(2) *Computational complexity*: The
computational complexity of the WEB-CE and the approximated RLS-CE are compared
with respect to a complex operator. We only focus on the required computation
number of the channel estimators, that is, (22) and (23). It is assumed that
the DFT operator requires complex multiplications (Mul.) and complex additions (Add.) per input symbols,
that is, and .
It is noticed in Figure 2 that the dotted DFT blocks would not be considered in
computing the computational complexity because these correspond to the register
(or buffer). In the WEB-CE, the following steps are necessary for each block
length of .

(i) in (22) requires complex multiplications and complex additions, respectively, when and are given. Considering the block length of , this step requires complex multiplications and complex additions.(ii)(22) requires complex multiplications and complex additions, respectively, where the term is a constant from (14). Therefore, this step requires complex multiplications and complex additions. The computational complexity of the approximated RLS-CE can be computed identically. As a result, the overall computational complexities of the WEB-CE and the approximated RLS-CE are shown in Table 1, which indicates that the computational load involved in the WEB-CE is linearly proportional to .

(3) *MSE analysis*: The MSE of the WEB-CE with respect to is analyzed in the Appendix. From (A.7), the
MSE is given by When and ,
the MSE (dB) curves versus and is depicted in Figure 3, where it is found
that the MSE decreases exponentially as increases for a given .

#### 4. Simulation Results

We evaluate the performance of SC-FDE employing WEB-CE in the slow time-varying channels [3]. In the simulation, a recursive systematic convolutional encoder with a generator and BCJR SISO decoding algorithm were employed. -random interleaver with a construction was used, where is a block size of the interleaver [8]. One block consists of 64 symbols (), where the symbol duration is . A normalized root mean square (rms) delay spread and radio channel memory of 3 (sample delay) are assumed for the TU channel as in Jake's model [7]. The CP length of 3 was set to eliminate the IBI. A data frame consists of data blocks and one TS block. In the simulations, the bit error rate (BER) was measured to evaluate the performance of the ZF-FDE with respect to of the WEB-CE. In addition, the MSE of channel estimate, that is, MSE between the perfect CFR and the estimated CFR by the WEB-CE, was also measured.

Figure 4 depicts the BER curve of the ZF-FDE employing the WEB-CE (), where the iteration number is 0 and 3. In this figure, the maximum Doppler shift was set to Hz and a parameter was chosen through a simulation in order to allow a good tracking for the WEB-CE. In order to provide a reference BER, a perfect channel condition was also considered. Here, the approximated RLS-CE could be regarded equivalently as the WEB-CE (). It is readily shown that the performance of the ZF-FDE is improved as the iteration number increases. In particular, it is noticed that the WEB-CE () yields a significant improvement of performance as compared to the WEB-CE (), which is caused by the gain obtained from (see Figure 3). It is also noticed that the error propagation due to the inaccurate past soft information [7] can be reduced as the iteration number increases.

Figure 5 depicts the corresponding MSE curve of Figure 4. It is shown that the WEB-CE () yields better MSE performance than the WEB-CE ().

Figures 6 and 7 depict the BER and MSE curves when Hz, where the simulation parameters are the same as in Figures 4 and 5. From these figures, we can also see that the WEB-CE yields superior performance as the iteration number and increase in the presence of a relatively fast fading. Furthermore, it is noticed from [9] that the TS blocks insertion, more often and using smaller , can help improve the BER performance as the Doppler shift increases.

Figure 8 depicts the performance comparison of the ZF-FDE with WEB-CE and the MMSE-FDE with WEB-CE , where it is assumed that a noise variance is known to the receiver. Figure 8 shows that the MMSE-FDE provides the lower BER performance than the ZF-FDE.

#### 5. Conclusions

In this paper,
we proposed the novel block iterative/adaptive frequency-domain channel
estimation named as the WEB-CE that utilizes the soft information in the
time-varying channel. In the WEB-CE, the correction term of the coefficient was
calculated by minimizing the weighted conditional *a posteriori error* vector-based LS criterion at each block iteration. It was found that the MSE of
the WEB-CE decreases as increases, which gives flexibility as compared
to the approximated RLS-CE. From the simulation results, it was shown that the
WEB-CE () would be a good choice for the channel
estimation scheme of the SC-FDE receiver in time-varying channels.

#### Appendix

#### MSE Analysis of the WEB-CE

In (22), assuming that the channel is stationary, is perfectly known to the receiver and the WEB-CE is on the steady state, we analyze the MSE defined aswhere is defined aswith , and . Premultiplying both sides of (A.2) by and using , (A.2) can be rewritten asSubstituting into (A.3), is expressed asCorrespondingly, the MSE is computed asHere, assuming for all in (1), and are given byFinally, substituting (A.6) into (A.5), the MSE of the WEB-CE is calculated as

#### Acknowledgments

This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute for Information Technology Advancement) (IITA-2008-(C1090-0801-0011)).

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