 Research Article
 Open Access
Block Transmissions over Doubly Selective Channels: Iterative Channel Estimation and Turbo Equalization
 Kun Fang^{1},
 Luca Rugini^{2}Email author and
 Geert Leus^{1}
https://doi.org/10.1155/2010/974652
© Kun Fang et al. 2010
 Received: 7 January 2010
 Accepted: 8 April 2010
 Published: 16 May 2010
Abstract
Modern wireless communication systems require high transmission rates, giving rise to frequency selectivity due to multipath propagation. In addition, highmobility terminals and scatterers induce Doppler shifts that introduce time selectivity. Therefore, advanced techniques are needed to accurately model the time and frequencyselective (i.e., doubly selective) channels and to counteract the related performance degradation. In this paper, we develop new receivers for orthogonal frequencydivision multiplexing (OFDM) systems and singlecarrier (SC) systems in doubly selective channels by embedding the channel estimation task within lowcomplexity block turbo equalizers. Linear minimum meansquared error (MMSE) pilotassisted channel estimators are presented, and the soft data estimates from the turbo equalizers are used to improve the quality of the channel estimates.
Keywords
 Pilot Symbol
 OFDM System
 Channel Estimator
 Selective Channel
 Basis Expansion Model
1. Introduction
Broadband wireless communication systems require high transmission rates, giving rise to frequency selectivity caused by multipath propagation, and consequently to intersymbol interference (ISI). In addition, recent wireless communication standards, such as WiMAX and LongTerm Evolution (LTE), also need to support high mobile speeds, leading to highmobility terminals and scatterers that introduce Doppler shifts and time selectivity, that is, intercarrier interference (ICI). Due to the concomitant presence of ISI and ICI, specialized techniques are necessary to counteract the related performance degradation. However, with a properly designed transceiver, time and frequencyselective (i.e., doubly selective) channels can even provide multiplicative delayDoppler diversity gains [1, 2].
LTE is a major 3GPP step in next generation wireless networks [3]. The LTE physical layer relies on a multipleaccess scheme based on orthogonal frequencydivision multiplexing (OFDM) in the downlink, and on singlecarrier frequencydivision multiple access (SCFDMA) in the uplink [3]. In both cases, the transmission scheme is blockwise, and a cyclic prefix (CP) is included in each data block in order to eliminate the ISI between consecutive data blocks. OFDM and singlecarrier (SC) block transmissions share some similarities: since an SC system can be viewed as a discrete Fourier transform (DFT) precoded OFDM system [4], performance and complexity are comparable, but part of the complexity (i.e., an inverse DFT) is moved from the transmitter to the receiver [4]. However, there are also some important differences: with respect to OFDM, SC has a lower peaktoaverage power ratio, and hence powerefficient terminals are suitable for the uplink [5]. However, both SC and OFDM systems suffer from doubly selective channels, and call for appropriate ICI mitigation methods.
A possible way to counteract a doubly selective channel is by means of iterative equalizers. The iterative approach, inspired by the turbo equalization principle [6, 7], exchanges soft information between the channel equalizer and the decoder, in an iterative fashion, and greatly improves the system performance. In the last fifteen years, many turbo equalizers have been proposed for timeinvariant frequencyselective channels (see [6–11], and the references therein). More recently, the turbo approach has been proposed also for doubly selective channel equalization, which is more challenging due to the time variation of the channel. For OFDM systems with doubly selective channels, lowcomplexity minimum meansquared error (MMSE) turbo equalizers have been proposed in [12, 13]. The turbo equalizers [12, 13], which are based on frequencydomain processing, estimate the data either serialwise, that is, each subcarrier is sequentially equalized [12], or blockwise, that is, all subcarriers are jointly equalized [13]. For SC transmissions over timeinvariant frequencyselective channels, timedomain turbo equalization (see [6, 7]) is traditionally more popular than frequencydomain turbo equalization [10]. However, recently, frequencydomain equalization has gained renewed interest, due to its reduced complexity for channels with significant delay spread [5]. For SC systems over doubly selective channels, a lowcomplexity iterative equalizer has been proposed in [14], which can be regarded as the timedomain counterpart of the iterative frequencydomain equalizer [15]. However, timedomain iterative equalizers are not suitable for channels with significant delay spread, since their complexity is quadratic in the channel length [14]. Besides, maximum likelihood and maximum a posteriori sequence estimators for SC and OFDM systems have been proposed in [16], which models the doubly selective channel using a basis expansion model (BEM).
In this paper, as a first contribution, we apply the block philosophy to design a lowcomplexity MMSE turbo equalizer for SC systems in doubly selective channels. To the best of our knowledge, all the turbo equalizers proposed so far for SC systems over doubly selective channels employ a serialwise data processing, that is, use a sliding window either in the time domain [14] or in the frequency domain [15, 17]. However, since the presence of the CP makes the transmission scheme blockwise, block equalization becomes a valid alternative. We design our block turbo equalizer for SC systems in the frequency domain, in the same spirit of the turbo equalizers designed for OFDM in [13]. An interesting feature of the proposed block equalizer is its reduced computational complexity, which scales only linearly with the block length. As a result, for doubly selective channels with significant multipath delay spread, our frequencydomain approach is less complex than timedomain equalizers like [14]. To keep the complexity low, some adhoc approximations are required, so that the proposed block turbo equalization algorithm for SC turns out to be different from that for OFDM [13]. In this paper, a performance comparison between the proposed algorithm and [13] is also given.
The ICI caused by Doppler spreading also makes the channel estimation problem more difficult. Pilot designs and pilotassisted channel estimation algorithms have been developed for SC over timevarying flatfading channels [18], for SC over doubly selective channels [19, 20], and for OFDM over doubly selective channels [21, 22]. All these papers, which employ a BEM for the channel, share the design principle that pilots and data are placed in such a way that they should remain orthogonal after transmission over the fading channel. Indeed, this criterion eliminates the datatopilot interference and hence it simplifies the channel estimation task. (The same criterion also eliminates the pilottodata interference, whose cancellation is therefore not necessary.) However, in doubly selective channels, orthogonal designs have two drawbacks. First, only approximateorthogonal designs are really possible, since a doubly selective channel cannot be perfectly diagonalized [23]. Second, a rate loss is introduced by the presence of zero symbols that are necessary to keep the almost orthogonality between data and pilots. On the other hand, nonorthogonal designs are also possible, such as the superimposed training approach developed in [24]. Actually, turboinspired iterative channel estimators can handle the datatopilot interference by means of reliabilitybased soft cancellation. In other words, the soft data estimates can be used to improve the quality of channel estimation, as shown by the adaptive iterative channel estimators [25, 26].
As a second contribution of this paper, we present iterative (turbolike) pilotassisted channel estimators for both OFDM and SC block transmissions. Differently from the turbobased channel estimators already proposed for SC transmissions over doubly selective channels [25, 26], the proposed turbolike channel estimators are nonadaptive and hence more suitable for block transmissions. For both OFDM and SC cases, the proposed iterative channel estimators firstly estimate the timedomain channel exploiting the BEM, and then transform the timedomain channel into the frequency domain for equalization purposes. This strategy is similar to that used for OFDM doubly selective channel estimation in [21, 22]. However, differently from the channel estimators of [21, 22], the proposed channel estimators exploit the reliability of the estimated data and can thus also work in the presence of nonorthogonal pilot designs.
To keep lowcomplexity channel estimation processing, we assume that the pilot symbols are located in the same domain where the data symbols are placed, that is, we assume frequencydomain pilots for OFDM systems, and timedomain pilots for SC systems. Although this choice is mainly dictated by computational complexity benefits, it is consistent with almostorthogonal pilot allocation strategies for doubly selective channels, which indeed suggest timedomain pilots for SC systems [19, 20], and frequencydomain pilots for OFDM systems [20].
The rest of this paper is organized as follows. In Section 2, we introduce the system model. Section 3 presents the proposed block turbo MMSE equalizer for SC systems. Section 4 deals with the design of iterative MMSE pilotassisted channel estimators, for both OFDM and SC systems. In Section 5, we evaluate and compare the performance of the proposed equalizer and of both channel estimators, by means of simulated results. Section 6 concludes the paper.
Notation 1.
We use upper (lower) boldface letters to denote matrices (column vectors). and , and represent transpose, complex conjugate transpose (Hermitian), and pseudoinverse, respectively. indicates the th entry of the matrix . We use the symbol and to denote the Hadamard (elementwise) product and Kronecker product between matrices, respectively. is a diagonal matrix with the vector on the diagonal. stands for the statistical expectation. The covariance matrix between and is defined as . Finally, and denote the allzero matrix and the identity matrix, respectively.
2. System Model
QPSK symbol alphabet.
 1  2  3  4 

 (0,0)  (1,0)  (0,1)  (1,1) 





As far as the time dispersion of the channel is concerned, we adopt the standard assumption that the maximum channel order is equal to the CP length, both denoted by , where . This way, there is no interference between successive blocks, and the equalizer can be designed separately for each block. As a consequence, we can omit the block index from our notation.
where , , , and , with the vector denoting the timedomain receiver window. Note that classical systems do not include windowing, that is, .
When the channel is time varying, is no longer circulant, and the frequencydomain channel matrix becomes a nondiagonal matrix, giving rise to ICI, where the ICI coupling is summarized by the nonzero offdiagonal elements of . However, with a proper window design, is cyclically banded, with the most significant elements around the main diagonal, and on the upperright and lowerleft corners [12]. In this paper, we employ the minimum band approximation error windowing developed in [27], where the window is obtained as a sum of complex exponentials. This choice permits the use of lowcomplexity equalization algorithms specially tailored to banded and cyclically banded matrices, as explained in [12, 28]. Observe that the receiver windowing in [27] only requires some statistical knowledge about the channel time variation, and this knowledge does not even have to be very exact.
where is the cyclically banded circulant matrix, which has ones on the main diagonal, on the super and subdiagonals, and on the upperright and lowerleft size corners, while the remaining entries are zeros. The matrix bandwidth parameter allows for a tradeoff between equalization complexity and performance, and it can be chosen according to some rules of thumb [12]. When windowing is included, is usually much smaller than the number of subcarriers .
It can be observed that the transmitted data block represents a timedomain signal in SC systems, while it represents a frequencydomain signal in OFDM systems. This clearly explains why SC systems are more prone to multipath effects, which mix the data due to the associated ISI, while OFDM systems suffer from Doppler effects, which mix the data due to the associated ICI. Our equalizer will be designed in the frequency domain, with the goal of mitigating the interference caused by the offdiagonal elements of .
3. LowComplexity Block Turbo Equalization
In order to derive frequencydomain block turbo equalizers for doubly selective channels, let us define as the th QPSK symbol of , and as the related bits. The mean and the variance of the symbol are denoted as and , respectively. Similarly, we have and . As far as the data symbols are concerned, the means and the variances are initialized with zeros and ones, respectively. But in every iteration of the turbo equalizer, they are updated using soft information from the estimated symbols. On the other hand, for each of the pilot symbols, the mean is set to the pilot symbol value, while the variance is zero, for all the iterations.
The a posteriori LLR of the current iteration then becomes the a priori LLR used in the next iteration. In the first iteration, no prior information is available, and therefore the a priori LLR is zero. The whole procedure described above can then be repeated, depending on the chosen number of iterations.
In the next subsection, we present a block turbo equalizer for SC systems. The proposed equalizer is derived using a similar approach as in [13], which develops three block turbo equalizers for OFDM systems.
3.1. Block Turbo Equalization for SC Systems
where . Similarly to our previous notation, we define as the th symbol of , and as the means and the variances of the frequencydomain symbols.
Given and as prior information, the equalizer exploits the means and the variances of the frequencydomain symbols. Since , we have , and . Since is diagonal, is in general circulant but not diagonal, that is, it can not be written as . However, as it will be explained later, dealing with a diagonal is crucial for complexity reasons. Therefore, to save complexity, we replace with its approximated version obtained by setting its offdiagonal elements to zero. Since the diagonal elements of are equal, is a scaled identity, with . A similar approximation is sometimes used also in timedomain equalizers [7, 8].
where is the th column of , , , and . It should be observed that, when is approximated as diagonal, the matrix is cyclically banded, and hence the computations in (6) can be performed using special algorithms designed for solving cyclically banded linear systems. In this work, we have used a cyclic band factorization obtained by a convenient modification of [28], in the same spirit of the fast Cholesky factorization of [29]. An alternative factorization algorithm could be derived using the divideandconquer method of [30]. Using the algorithms specifically tailored to cyclically banded matrices, the computational complexity per data block reduces to , which is linear in the block size . On the contrary, the complexity of a timedomain equalizer would be . Since the Doppler support is usually much lower than the maximum channel order , our frequencydomain MMSE equalizer is computationally cheaper than the corresponding timedomain MMSE equalizer.
On the other hand, without any approximation on , would not be cyclically banded, and therefore the complexity order of MMSE equalizers would be . Since the block size is by far greater than the Doppler support , the diagonal approximation is necessary for lowcomplexity MMSE equalizers. However, when moderate complexity is affordable, other approximations are possible. For instance, if is approximated as cyclically banded with bandwidth , the matrix would be cyclically banded too, but the computational complexity would increase to . Alternatively, a lowcomplexity weighted leastsquares (WLS) equalizer that avoids the approximation of could be employed, by neglecting the noise covariance matrix inside . However, since doubly selective channels lead to highly illconditioned matrices, WLS equalizers produce a very poor performance. Indeed, MMSE equalizers can be interpreted as regularized WLS equalizers.
where is the indicator function, defined as the th column of , is a diagonal matrix, and .
where and . In (8), we have approximated the matrices , , and by diagonal matrices, by setting their offdiagonal elements to zero. As it will be explained later, similarly to the diagonal approximation of , these approximations are necessary to maintain a low complexity. We now separately discuss the three approximations. First, the approximation of is similar to the approximation of , which has been discussed previously. However, now the obtained diagonal matrix is not a scaled identity. Second, since is cyclically banded, the offdiagonal elements of decay to zero very rapidly. Hence, we expect that the approximation on will not introduce a significant error. Third, the matrix represents the effect of a linear MMSE equalizer applied to the channel matrix . Since the MMSE equalizer highly mitigates the ICI, is already very close to a diagonal matrix. This last approximation also leads to , which justifies the equalizer unbiasedness .
where and .
It is easy to prove that the calculation of the extrinsic LLR in (9) has complexity . Therefore, the equalization complexity of (6) dominates over the extrinsic LLR calculation complexity of (9). Taking into account FFT operations, the overall computational complexity per iteration for each block of symbols is , which is independent of the channel length . On the other hand, the complexity of the timedomain equalizer of [14] is . Therefore, for multipath channels with a long impulse response, we obtain a significant complexity saving. A more detailed discussion (i.e., flops count) about the computational complexity of banded turbo equalizers can be found in [13].
We highlight that the three diagonal approximations introduced in (8) are fundamental in reducing the computational complexity. For instance, if the full matrix is used, the computation of in (8) involves full matrices, and therefore the computational complexity would be at least . In this case, the complexity of the extrinsic LLR calculation (9) would dominate. Clearly, the nonapproximated equalizer would be useful only when the block size is small, which is not feasible in long multipath channels due to the constraint . Therefore, if low computational complexity is important, there is no way to avoid diagonal approximations. We also point out that, among the different possible ways to approximate the two matrices and as diagonal, the only reasonable approach is setting their offdiagonal elements to zero. Indeed, as explained after (8), and are almost diagonal. However, for , there exist different ways to approximate it as diagonal. Neglecting the offdiagonal elements leads to , which could be replaced, for instance, by . Intuitively, the average approximation assigns to all the symbols the average reliability of all the symbols, whereas the maximum approximation assigns to all the symbols the reliability of the worst symbol estimate. In the simulation section, we compare the performance of both approximations.
4. Iterative Channel Estimation
The turbo equalizers presented in the previous section require the channelstate information (CSI) at the receiver. To acquire the CSI, we propose a modification of a pilotassisted channel estimator presented in [21] for OFDM. Specifically, we modify the iterative linear MMSE channel estimator of [21] in such a way that it can operate in a turbo fashion. Therefore, besides the pilot symbols, we also use the soft data estimates originating from the turbo equalizer and the decoder. Indeed, after the first iteration, the soft data symbol estimates can be used as auxiliary pilot symbols, in order to improve the quality of the subsequent channel estimates [31]. For both OFDM and SC systems, our channel estimators produce an estimate of the timedomain channel matrix , and then translate into the frequencydomain cyclically banded matrix estimate . The channel estimators are assumed to have perfect knowledge of the channel statistics, that is, the Doppler spectrum and the powerdelay profile. We highlight that the channel estimators considered in this paper are nonadaptive, that is, the CSI is newly estimated in each transmitted block, using both pilots and data. This way, severe time variation can be handled.
In pilotassisted transmissions, there exist various approaches to design the pilot pattern. We can distinguish between two broad categories: multiplexed training and superimposed training [24]. In the multiplexed training case, each element of the transmitted vector contains either a pilot symbol or a data symbol, while in the superimposed case both pilot and data symbols are located in the same positions, typically distributed over the whole transmitted vector. In this paper, we assume multiplexed training, which is also known as periodic training when the pilots are placed in the time domain, and as orthogonal training when the pilots are located in the frequency domain. In particular, we focus on the pilot placement schemes developed in [19, 20], which have been proved to be optimal in the MMSE sense under certain channel conditions. In these schemes, pilot symbols are interleaved with the data symbols to form the transmitted signal vector. For OFDM systems, we employ the frequencydomain Kronecker delta (FDKD) pilot structure [20], while, its dual scheme [19], identified as timedomain Kronecker delta (TDKD), is adopted for SC systems. In both cases, the pilot symbols are grouped into equidistant clusters, each having the same length. Within each cluster, a unique nonzero pilot symbol is located in the middle of the cluster, while null pilot symbols are placed on both sides. Therefore, the FDKD scheme coincides with equispaced pilot tones with guard frequency bands, while the TDKD scheme uses periodic training with guard time intervals.
respectively, with size and , respectively.
where is an matrix that has orthonormal basis functions as columns, and is a vector that collects all the BEM coefficients of all the channel taps.
In order to derive our MMSE channel estimator, the following assumptions are made.
Assumption.
where denotes the variance of the th channel tap, is the normalized time correlation, and stands for the Kronecker delta function.
Assumption.
respectively.
Assumption.
Assumption.
where , with and .
Assumption.
4.1. Iterative Channel Estimation for OFDM Systems
For OFDM systems, the pilot and data symbols are interleaved in the frequency domain. Since the frequencydomain channel matrix is cyclically banded only approximately, the received samples used for channel estimation are always contaminated by ICI, independently of the length of the null guard bands inserted. To be precise, the frequencydomain channel matrix is (with high probability) a full matrix, and hence the power of the pilot symbols is spread over all the received samples. While a timedomain receiver window can reduce the ICI to get a better equalization performance, it is still unclear whether the same window can improve the channel estimation quality or not. Thus, to estimate the timedomain channel matrix , we use the frequencydomain received signal without applying the timedomain receiver window.
where , , and represents the first columns of the matrix .
where is an matrix consisting of the rows of with indices from to . It can be observed that the pilot symbols, as well as the soft data estimates , are used to estimate the CSI, which could help to achieve a better performance than [21], which uses the pilot symbols only. The second term in (22) reflects the uncertainty of the soft data estimates and can be regarded as interference, whose covariance can be taken into account into the channel estimator.
where , , and are column vectors of size , , and .
After the estimation of the BEM coefficients in , the timedomain channel vector can be reconstructed by (13) as , whose elements form the estimated timedomain channel matrix .
In [21], it has been shown that the BEMbased linear MMSE channel estimator can achieve a better performance by using a larger number of observation samples, that is, when all elements of are included in the observation vector . Obviously, the same behavior is expected in our case: Indeed, our channel estimator additionally includes the reliability of the turboequalized data symbols, and hence additional benefit should be obtained by including more data locations into the observation window. However, the main complexity of our channel estimator comes from the matrix inverse in (25), which requires the observation vector length to be small. Thus, the smoothing parameter allows for a tradeoff between channel estimation complexity and performance.
4.2. Iterative Channel Estimation for SC Systems
Unlike OFDM systems, where the pilot symbols are inserted in the frequency domain, and the frequencydomain channel matrix is cyclically banded only approximately, in SC systems the pilots are positioned in the time domain, and the timedomain channel matrix is banded, due to the FIR channel assumption. Therefore, using sufficiently long guard intervals, the ISI between pilots and data is completely eliminated [19], thereby simplifying the channel estimation procedure.
where , , with and , and and are defined similarly to .
where and are similarly defined as in (25), and . It is easy to understand that, also in this case, a better performance is achieved by including a larger number of observation samples [21], that is, by increasing the smoothing parameter , at the price of increased complexity.
5. Simulation Results
In this section, the proposed algorithms are examined and compared by simulations. We consider a block transmission system with block length . A rate convolutional code with generator polynomials (in octal notation) and codeword length of is used. We employ random interleaving. The maximum channel delay spread and the CP length are equal to . The channel is assumed to be Rayleigh distributed with uniform powerdelay profile , , and with Jakes' Doppler spectrum [32, 33]. We consider a highmobility case where the normalized Doppler frequency is with the absolute Doppler frequency shift and the symbol period. It can be interpreted as with the subcarrier spacing in OFDM systems. The timedomain receiver window of [27], as well as the cyclically banded equalizers, are designed for a matrix bandwidth parameter , unless otherwise stated. We use the generalized complexexponential (GCE) BEM to model the timevarying channel at the receiver [21]. Note that the (critically sampled) complexexponential (CE) BEM would produce a cyclically banded channel matrix estimate, where the number of BEM parameters coincides with the number of estimated diagonals. On the contrary, the GCEBEM produces a full channel matrix estimate: Hence, this choice permits to increase the equalizer bandwidth , so that the number of equalizer diagonals can exceed the number of BEM parameters . The channel decoder employs a linear approximation to the logMAP decoding algorithm.
6. Conclusions
We have proposed a lowcomplexity frequencydomain block MMSE turbo equalizer for SC systems in doubly selective channels. We have exploited the cyclically banded structure of the frequencydomain channel matrix, as well as receiver windowing that enforces the cyclically banded structure, to limit the computational complexity, which is linear in the block length. For both OFDM and SC systems, we have developed two iterative MMSE pilotassisted channel estimators, where the soft data estimates from the turbo equalizers are used to improve the quality of the channel estimates. Combined with error correction coding, both OFDM and SC systems can effectively exploit the delayDoppler diversity provided by doubly selective channels.
Declarations
Acknowledgments
The authors would like to thank one of the reviewers, who provided insightful comments and interesting suggestions. This research was supported in part by NWOSTW under the VIDI program (DTC.6577) and VICI program (DTC.5893). This paper was presented in part at the International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, April 2008, and International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, April 2009.
Authors’ Affiliations
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