Kalman interpolation filter for channel estimation of LTE downlink in highmobility environments
 Xuewu Dai^{1},
 Wuxiong Zhang^{2, 3, 4},
 Jing Xu^{3, 4},
 John E Mitchell^{1}Email author and
 Yang Yang^{3, 4}
DOI: 10.1186/168714992012232
© Dai et al.; licensee Springer. 2012
Received: 23 August 2011
Accepted: 11 June 2012
Published: 25 July 2012
Abstract
The estimation of fastfading LTE downlink channels in highspeed applications of LTE advanced is investigated in this article. In order to adequately track the fast timevarying channel response, an adaptive channel estimation and interpolation algorithm is essential. In this article, the multipath fastfading channel is modelled as a tappeddelay, discrete, finite impulse response filter, and the timecorrelation of the channel taps is modelled as an autoregressive (AR) process. Using this AR timecorrelation, we develop an extended Kalman filter to jointly estimate the complexvalued channel frequency response and the AR parameters from the transmission of known pilot symbols. Furthermore, the channel estimates at the known pilot symbols are interpolated to the unknown data symbols by using the estimated timecorrelation. This article integrates both channel estimation at pilot symbols and interpolation at data symbol into the proposed Kalman interpolation filter. The bit error rate performance of our new channel estimation scheme is demonstrated via simulation examples for LTE and fastfading channels in highspeed applications.
Keywords
LTE advanced Channel estimation Extended Kalman filter PilotaidedinterpolationIntroduction
Channel estimation plays an important role in communication systems and, particularly, in the 3GPP LongTerm Evolution (LTE) which aims at continuing the competitiveness of the 3G Universal Mobile Telecommunications System technology. Orthogonal frequencydivision Multiple Access (OFDM) is considered as one of the key technologies for the 3GPP LTE to improve the communication quality and capacity of mobile communication system. As the support of high mobility is required in 3GPP LTE systems, the signals at the OFDM receivers are likely to encounter a multipath, fast timevarying channel environment [1]. Thus, good channel estimation and equalization at the receiver is demanded before the coherent demodulation of the OFDM symbols. In mobile communication, since the radio channel is modelled by some dominant spare paths and is represented by path taps, the channel estimation is to estimate and track the channel taps adaptively and efficiently.
In wideband mobile communications, the pilotbased signal correction scheme has been proven a feasible method for OFDM systems. The 3GPP LTE standard employs a Pilot SymbolAided Modulation (PSAM) scheme but does not specify the methods for estimating the channel from the received pilot and data signals. In the 3GPP LTE downlink, pilot symbols, known by both the sender and receiver, are sparsely inserted into the streams of data symbols at prespecified locations. Hence, the receiver is able to estimate the whole channel response for each OFDM symbol given the observations at the pilot locations. Pilotsymbolaided channel estimation has been studied [2–4] and the common channel estimation techniques are based on least squares (LS) or linear minimum mean square error (LMMSE) estimation [5]. Note that most pilotsymbolaided channel estimators, including those mentioned above, work in the frequency domain. LS estimation is the simpler algorithm of the two as it does not use channel correlation information. The LMMSE algorithm makes use of the correlation between subcarriers and channel statistic information to find an optimal estimate in the sense of the minimum mean square error.
In the literature, based on these two basic estimators, various methods are proposed to improve the performance of the channel estimation. As the LS and LMMSE estimators only give the channel estimate at the pilot symbol, most current work on pilotaided channel estimation considers interpolation filters where channel estimates at known pilot symbols are interpolated to give channel estimates at the unknown data symbols. Since the 3GPP LTE downlink pilot symbols are inserted in a comb pattern in both the time and the frequency domain, the interpolation is a 2D operation. Although some 2D interpolation filters have been proposed [6], presently, interpolation with two cascaded orthogonal 1D filters is preferred in 3GPP LTE. This is because the separation of filtering in time and frequency domains by using two 1D interpolation filters is a good tradeoff between complexity and performance. Various 1D interpolation filters have been investigated. Examples are linear interpolation, polynomial interpolation [7], DFTbased interpolation [8], moving window [9] and iterative Wiener filter [10].
From a system point of view, the channel estimation is a state estimation problem, in which the channel is regarded as a dynamic system and the path taps to be estimated are the state of the channel. It is known that Kalman filter (KF) provides the minimum mean square error estimate of the state variables of a linear dynamic system subject to additive Gaussian observation noise [11]. By considering the radio channel as a dynamic process with the path taps as its states, the KF has shown its suitability for channel estimation in the time domain [1]. In the frequency domain, Kalmanbased channel estimator in OFDM communication has also been studied [1, 12, 13]. For example, in [1, 12], a modified KF is proposed for OFDM channel estimation where the timevarying channel is modelled as an autoregressive(AR) process and the parameters of the AR process are assumed real and within the range [0.98, 1] for slowfading channels. However, in the highmobility environment, these parameters are relative large (e.g. in the 200 km/h environment, they are complex values with magnitudes varying in [0, 1.5]) representing a fastfading channels.
The difference between the KF in [12] and the one proposed in this article is that the former estimated the parameters of AR by a gradientbased recursive method separately, rather by the linear KF. Whereas, we derive an extended Kalman filter (EKF) for jointly estimating the channel response and the parameters of the AR model simultaneously. In addition, the parameters of the AR model are assumed timeinvariant and known in priori by solving YuleWalker equation in [1]. The authors of [13] only considered the combtype pilot patterns in which some subcarriers are full of pilot symbols without unknown data. As a result, the KF in [13] requires continuous stream of pilot symbols and is not suitable for 3GPP LTE, as the 3GPP LTE employs a scattered pattern where the pilot symbols are distributed sparsely among the data streams.
Although the KFbased channel estimation for LTE uplink has been reported recently [1], there has been no KFbased joint estimation of both timevarying channel taps and the timecorrelation coefficients of 3GPP LTE downlink in frequencytime domain. This article focuses on the major challenge of scattered pilotaided channel estimation and interpolation for a timevarying multipath fastfading channel in 3GPP LTE downlink. An AR process is used to model the timevarying channel. Both the taps of the multipath and the timecorrelation coefficients are jointly estimated by treating the channel as a nonlinear system. Then, a combined estimation and interpolation scheme is present under the EKF framework.
The main contribution of the proposed method is (1) both the timecorrelation coefficients and channel taps are estimated simultaneously in the framework of EKF; (2) no assumption on the upper/lower boundaries of the timecorrelation coefficients to achieve a good tracking of fastfading channel in highmobility scenario; (3) applicable to preamble pilot patterns, combtype pilot patterns and scattered pilot patterns.
This article is organized as follows: Section “System model” gives an overview of the LTE 3GPP downlink system and formulates its channel estimation problem. In Section “EKF for channel estimation”, an EKF is derived by using a firstorder Taylor approximation for the joint estimation of channel taps and timecorrelation coefficients at pilot symbols. Section “EKF for channel interpolation” describes the combined estimation and interpolation scheme and summarizes the proposed algorithm. Simulation results of the proposed Kalman interpolation filter are presented and its performance is demonstrated in Section “Simulation results and performance analysis”.
Notation and terms
Unless specified otherwise, an italic letter (e.g.T, h_{ k,n }) represents a scalar and its bold face lowercase letter represents its corresponding vector (e.g. ${\mathbf{h}}_{k}=\left[{h}_{k,1}{h}_{k,2},{h}_{k,{N}_{p}}\right]$). A bold face uppercase letter (e.g. A) represents a matrix. The subscriber k denotes the time index of an OFDM symbol, n denotes the index of subcarriers in the frequency domain, l denotes the l th path of the radio channel. $\leftx\right$ ($\leftA\right$) is the elementwise magnitude of a vector x (matrix A). I_{ N } is an N × N identity matrix. A_{ i,j } denotes the entry at the i th row and the k th column of A.
L denotes the total number of possible paths in a radio channel, referred to as channel length, N denotes the total number of subcarriers, N_{ p } the number of pilot subcarriers, ${g}_{k,l}$ the channel impulse response (CIR) of l th path at k th symbol, referred to as tap, g_{ k } the CIR vector at k th symbol time, ${\mathbf{g}}_{k}=\left[{g}_{k,1}\cdots {h}_{k,L}\right]$_{,}h_{ k,n } the channel frequency response (CFR) at k th symbol time and n th subcarrier, ${\overline{\mathbf{h}}}_{k}$ the CFR vector at all subcarriers at k th symbol time, ${\overline{\mathbf{h}}}_{k}=\left[{h}_{k,1}\cdots {h}_{k,N}\right]$, h_{ k } the CFR vector at N_{ p } pilot subcarriers at k th symbol time, ${\mathbf{h}}_{k}=\left[{h}_{k,1}\cdots {h}_{k,{N}_{p}}\right]$_{,}a_{ k } the timecorrelation coefficients of CFR at k th symbol time, x_{ k } the vector of transmitted OFDM symbols at pilot subcarriers at k th symbol time and y_{ k } the corresponding received OFDM symbol vector of x_{ k }.
System model
Pilot symbols in LTE downlink
Physical RBs parameters
Configuration  ${\mathbf{N}}_{\mathit{\text{sc}}}^{\mathit{\text{RB}}}$  ${\mathbf{N}}_{\mathit{\text{symb}}}^{\mathit{\text{DL}}}$  

Normal cyclic prefix  $\Delta f=15\phantom{\rule{0.12em}{0ex}}\text{kHz}$  12  7 
Extended cyclic  $\Delta f=15\phantom{\rule{0.12em}{0ex}}\text{kHz}$  6  
prefix  $\Delta f=7.5\phantom{\rule{0.12em}{0ex}}kHz$  24  3 
In order to successfully receive a data transmission, the receiver must estimate the CIR to mitigate the multipath interference. In packetoriented networks (like IEEE 802.11), a physical preamble is used to facilitate this purpose. In contrast to 802.11, LTE makes use of PSAM, where known reference symbols, referred to as pilot symbols, are inserted into the stream of data symbols, as shown in Figure 1. Generally, there are three kinds of timefrequency allocation pattern of pilot symbols, namely, entirely known OFDM symbols, pilot subcarriers and scattered pilots. 3GPP LTE adopts a scattered pattern involving the sparse insertion of known pilot symbols in a data symbol stream. For example, in the scenario of a single transmitting and a single receiving antenna, pilot symbols are transmitted at the first and the fifth OFDM symbols of each slot at the pilot subcarriers. In the frequency domain, reference signals are spread over every six subcarriers.
The effect of the channel response on the known pilot symbols can be computed directly by calculating the attenuation of each pilot symbol [5]. For the remaining unknown data symbols, interpolation has to be used to estimate the channel response among adjacent pilot symbols. A simple way of performing this interpolation is the linear approximation in both time and frequency. The concept of PSAM in OFDM systems allows the use of both the time and frequency correlation properties of the channel to improve the channel estimation. Therefore, an efficient channel estimation procedure may apply a complicated 2D timefrequency interpolation or a combination of two simple 1D interpolations [6] to provide an accurate estimation of the channel states for each OFDM symbol.
Channel model
 (1)
The system bandwidth is B = 1/T, where T is the duration of one timechip. The duration of one OFDM symbol is ${T}_{s}=NT+{T}_{\mathit{CP}}$, where T_{CP} is the duration of cyclic prefix (CP) for every OFDM symbol.
 (2)
The number of possible path is L and the maximum delay due to multipath is (L – 1)T.
 (3)
The length of CP is carefully designed to eliminate intersymbol interference between consecutive OFDM symbols. That is T_{CP} is longer than the than the channel’s maximum delay, $(L1)T<{T}_{\mathit{\text{CP}}}$.
 (4)
The Rayleighfading channel varies in consecutive OFDM symbols, but is assumed constant within one OFDM symbol.
where the l th path is represented by a tap with complex amplitude α_{ l }(t) at time instant t and a delay τ_{ l }. The impulse response of the physical channel consists of independent Rayleighfading impulses, uniformly distributed over the length of the CP.
Strictly speaking, g_{ k } is only an approximation of $g(t,\tau )$ at k th OFDM symbol ($kTs<t<(k+1)Ts$). When the multipath taps do not fall in the discrete sampling grid (i.e., ${\tau}_{l}\ne lT$), the discretetime CIR vector will be infinite in length. However, the pulse’s energy decays quickly outside the neighbourhood of the original pulse location [5, 14], it is still feasible to capture the impulses with a lengthL vector. In this study, we assume that the tails of the impulse response function are negligible beyond L samples, which is also the assumption made in OFDM to justify that no ISI occurs.
It has been shown that timevarying path taps in a fading channel can be modelled by an AR process [11, 15], which is applicable to general fading channels, and in particular to mobile communication. Examples include the firstorder AR model in [1, 11, 16] and the secondorder AR model [15]. Although the firstorder AR model is just an approximation to the actual statistics of the random radio propagation process, it is more realistic than those models assuming constant channel parameters (identity matrix) or using linear interpolation. Furthermore, the use of higherorder models will lead to higher computational costs, which may not be justified by the performance improvement. Compared to the higherorder model, a lowerorder model may reduce the overall computational complexity significantly with only a relatively small performance sacrifice. Here, we are concerned with the basic derivation of the proposed Kalman interpolator filter in LTE downlink. As shown in our following derivation, higherorder models can also be incorporated into the proposed scheme with only minor modifications. For the purpose of analysis, we restrict ourselves to a firstorder AR model for the timevarying channel.
where α_{ n } represents the time correlation of the channel response between k th and (k + 1)th OFDM symbols at the n th subcarrier. ${v}_{k,n}$ is a mutually independent zeromean Gaussian complex white noise representing the modelling error.
LTE OFDM reception and channel estimation
Here, ${w}_{k}\in {\u2102}^{{N}_{p}}$ is an additive white complex Gaussian noise with covariance matrix ${\sigma}_{w}^{2}{I}_{{N}_{p}}$ and ${h}_{k}\in {\u2102}^{{N}_{p}}$ is the CFR at pilot subcarriers at k th OFDM symbol.
where ${\mathbf{y}}_{k}/{\mathbf{h}}_{k}$ is an elementwise division with elements y_{ k,n }/h_{ k,n }.
It is worth noting that, as the pilot symbols in LTE downlink are inserted into the data symbols sparsely in a frequencytime scatter pattern, the channel response at data symbols are typically interpolated from the channel estimates at pilot symbols. As shown in literature, if the OFDM symbol is short compared with the coherence time of the channel, the time correlation between the channel attenuation of consecutive OFDM symbols is high. There is also a substantial frequency correlation between the channel attenuation of adjacent subcarriers. For a better channel estimation at data symbols, both of these time and frequency correlation properties of the fading channel can be exploited by the channel estimator.
Generally, as illustrated in Figure 1, the whole process of such a pilotaided channel estimation includes three steps: (1) Estimation at pilot symbols, where, h_{ k }, the channel responses at N_{ p } pilot subcarriers at k th OFDM symbol are calculated with the common LS estimator or LMMSE estimator; (2) Timedomain interpolation, where the channel responses h_{k+1} at (k + 1)th OFDM symbol at pilot subcarriers are estimated from h_{ k } by tracking the parameters of each path. (3) Frequencydomain interpolation, where the channel responses at all N subcarriers are estimated by interpolating or smoothing these estimates {h_{ k, }h_{k+ 1},…} at pilots subcarriers. This article integrates the first two steps into one framework called the Kalman interpolator filter.
EKF for channel estimation
In this section, we are interested in deriving a minimum variance estimator/interpolator for the channel response {h_{ k, }h_{k+ 1},…} at pilot subcarriers from the observation of sparse pilot symbols. We present a combined estimation and interpolation scheme, where the time correlation among consecutive OFDM symbols is taken into account to estimate the CFR at the known pilot symbols and then to interpolate to estimate the CFR at the unknown data symbols at the pilot subcarriers. The proposed scheme is based on the idea of Kalman filtering to improve the accuracy of the estimation and interpolation. More specifically, recalling the LTE reception model ${y}_{k}={X}_{k}{h}_{k}+{w}_{k}$ in (9), the task for the Kalman interpolator filter can be stated as:
Given the matrix X_{k} of known transmitted pilot symbols and received signal y_{ k } at k th OFDM symbol, to obtain minimum variance estimates of the timevarying multipath CFR h_{ k } and interpolate h_{ k } to the followed six $\left\{k+1,k+2,\dots ,k+6\right\}$ data symbols at the pilot subcarriers until the next pilot symbol (k + 7th OFDM symbol) is received.
Augmented state space model
where h_{ k } is the state variable to be estimated, ${A}_{k}\in {\u2102}^{{N}_{p}\times {N}_{p}}$ is the unknown state transition matrix consisting of the time correlation coefficients α_{ n } of channel response. Both v_{ k } and w_{ k } are mutually independent, zeromean, Gaussian complex white noises, with covariance ${Q}_{v}\triangleq {\sigma}_{w}^{2}{\mathit{I}}_{{N}_{p}}$ and ${Q}_{w}\triangleq {\sigma}_{w}^{2}{\mathit{I}}_{{N}_{p}}$, respectively. It is assumed that v_{ k } and w_{ k }are independent of the state variable h_{ k }. Note that, in this state space model of the CFR, the state transition matrix A_{ k } is unknown and to be estimated together with the state variable h_{ k }. Therefore, it is a problem of joint state and parameter estimation. The purpose is to estimate both the channel response h_{ k } and channel’s timecorrelation matrix A_{ k } from the received pilot symbols y_{ k }.
EKF
Since the state transition function f(z_{ n }) in the augmented state model (16) is a nonlinear function and an EKF has to be used to estimate the augmented states. The development of the EKF basically consists of two procedures: linearizing the augmented model (16) and applying the standard KF to the linearized model.
 1.Prediction (before receiving a OFDM symbol):${\widehat{z}}_{kk1}=f\left({z}_{k1}\right)=\left[\begin{array}{c}\hfill {\widehat{a}}_{k1}\hfill \\ \hfill {\widehat{A}}_{k1}{\widehat{h}}_{k1}\hfill \end{array}\right]$(19)$\begin{array}{ll}{P}_{kk1}& ={F}_{k1}{P}_{k1}{F}_{k1}^{H}+{Q}_{u}\\ =\left[\begin{array}{cc}\hfill {P}_{a,kk1}\hfill & \hfill {P}_{ah,kk1}\hfill \\ \hfill {P}_{ah,kk1}^{H}\hfill & \hfill {P}_{h,kk1}\hfill \end{array}\right]\end{array}$(20)
 2.Correction (once the reception of the OFDM symbol has completed):$\begin{array}{ll}{\mathbf{K}}_{k}\hfill & ={\mathbf{P}}_{kk1}\left[\begin{array}{c}\hfill 0\hfill \\ \hfill {\mathbf{X}}_{k}^{H}\hfill \end{array}\right]{\left(\left[0\phantom{\rule{0.24em}{0ex}}{\mathbf{X}}_{k}\right]{\mathbf{P}}_{kk1}{\left[0\phantom{\rule{0.24em}{0ex}}{\mathbf{X}}_{k}\right]}^{H}+{\mathbf{Q}}_{w}\right)}^{1}\hfill \\ =\left[\begin{array}{c}\hfill {\mathbf{P}}_{ah,kk1}\hfill \\ \hfill {\mathbf{P}}_{h,kk1}\hfill \end{array}\right]{\mathbf{X}}_{k}^{H}{\left({\mathbf{X}}_{k}{\mathbf{P}}_{h,kk1}{\mathbf{X}}_{k}^{H}+{\mathbf{Q}}_{w}\right)}^{1}\hfill \end{array}$(22)$\begin{array}{ll}{\widehat{\mathbf{z}}}_{k}\hfill & ={\widehat{\mathbf{z}}}_{kk1}+{\mathbf{K}}_{k}\left({\mathbf{y}}_{k}\left[0\phantom{\rule{0.24em}{0ex}}{\mathbf{X}}_{k}\right]{\widehat{\mathbf{z}}}_{kk1}\right)\hfill \\ ={\widehat{\mathbf{z}}}_{kk1}+{\mathbf{K}}_{k}\left({\mathbf{y}}_{k}{\mathbf{X}}_{k}{\widehat{\mathbf{h}}}_{\mathit{k}k1}\right)\hfill \end{array}$(23)${P}_{k}={P}_{kk1}{K}_{k}\left[0\phantom{\rule{0.36em}{0ex}}{X}_{k}\right]{P}_{kk1}$(24)
Here, K_{ k } is the Kalman gain of the EKF. The EKF makes use of a firstorder Taylor approximation of the state transition and thus does not approach the true minimum variance estimate when the linearization error is nonnegligible. Nevertheless, the resulting EKF is a practical approximation to the minimum variance estimator when the state equation is nonlinear, and will be shown to provide a good performance in timevarying channel estimation. Furthermore, the EKF has successfully been applied to the problem of joint channel state and parameter estimation in [11, 16], and thus it seems reasonable to apply EKF to the timevarying channel estimation.
Remark
In terms of computation complexity, it can be seen that prediction of state error covariance ${\mathbf{P}}_{k+}{}_{1k}$ and the update of K_{ k } consumes the major amount of computation. Fortunately, in general, crosspath coupling is confined within a small neighbourhood, and thus the offdiagonal elements of A_{ k } representing the coupling between multiple paths are small and may be neglected. As shown in the AR model (8) of timevarying channel, the channel’s timecorrelation matrix A_{ k } can be modelled as a diagonal matrix. If both X_{ k } and A_{ k } are diagonal matrices, the number of complex multiplications and additions is be reduced to a great extent. More specifically, the number of multiplication and division operations in Equations (19)–(24) is 25N_{ p }.
EKF for channel interpolation
In this section, the proposed EKF is further extended to interpolate the CFR estimate to unknown data symbols and the whole estimation and interpolation process of the proposed EKF is summarized.
The estimator is trained during these periods when a pilot symbol is received. Then it switches to an interpolation mode, in which a decisiondirected method is applied to estimate the channel response until the next pilot symbol is received. During the training period, the transmitted symbols X_{ k } are known to the estimator, while in the data symbols periods, the transmitted data symbols are estimated as ${\widehat{\mathbf{X}}}_{k}$by the decoder and the EKF is fed by the ${\widehat{\mathbf{X}}}_{k}$to replace the unknown transmitted symbols X_{k.} Indeed, the channel estimator is fed with one pilot symbol and six estimates of the data symbols in one LTE slot. The proposed Kalman interpolator filter method yields an adaptive algorithm and can be implemented recursively.
At each iteration, the equalizer and the decoder compute an estimate ${\widehat{\mathbf{X}}}_{k}$of the transmitted data symbols on the basis of the previous, a priori channel estimate ${\widehat{h}}_{kk1}$. In the iteration of the OFDM data symbol, ${\widehat{\mathbf{X}}}_{k}$is also fed to the EKF to calculate a posteriori channel estimate ${\widehat{h}}_{kk}$ and a priori channel estimate ${\widehat{h}}_{k+1k}$. By exchanging their estimates, both EKF and equalizer are able to improve their performance iteratively. This is particularly useful at these iterations of unknown data symbols.
Initialization by LS estimation
Although a KF is able to convergence under any reasonable initial value of the state variable z_{ k }, a good initial condition will reduce the duration of convergence. Generally, if the initial value of the state variable is set to the neighbourhood of the true value, a faster convergence can be obtained. Since the state variable z_{ k } consists of two independent components, a_{ k } and h_{ k }, their initial values are chosen separately.
For initializing the channel’s timecorrelation coefficients a_{ k }, we use an identity matrix ($A\left({a}_{0}\right)={I}_{{N}_{A}}$) assuming the channel response at next OFDM symbol is the same as the current OFDM symbol. Although an identity matrix represents a timeinvariant channel, an identity matrix would be the best choice of the channel’s initial condition, given we have no a priori knowledge about the channel.
where ${\widehat{h}}_{0,LS}\in {\u2102}^{{N}_{p}}$ is the initial CFR estimate. A more complicated LMMSE estimator using the channel’s frequency correlation may be applied to obtain a more accurate initial estimate of the CFR. It should be pointed out that the EKF is initialized until the first group of pilot symbol is received.
Trained estimation
After the state variable is initialized, the EKF works iteratively either in the training mode or in the interpolation mode. During the pilot symbols, the EKF switches to the training mode, where the known pilot symbol forms the matrix X_{ k }. As the observation y_{ k } is obtained by the DFT at the end of an OFDM symbol duration, the a posteriori CFR ${\widehat{h}}_{kk}$ is first estimated from y_{ k } by using update equations (22)–(24). Then the a priori estimate ${\widehat{h}}_{k+1k}$is calculated by Equations (19)–(21) for next OFDM symbol.
Decisiondirected interpolation
During periods where the pilot symbol is not available, the EKF switches to decisiondirected interpolation mode to continue adaptation. For these data symbols, as the transmitted symbol X_{ k } is unknown, X_{ k } is replaced by the decoder’s decision of ${\widehat{X}}_{k}$that is supposed to be nearest to X_{ k }. In the decisiondirected mode, the prediction and correction processes are the same as the training mode, except X_{ k } is replaced by ${\widehat{X}}_{k}$,.
It is worth noting that, as y_{ k } is only available at the end of the current symbol duration, the correction process has to be carried out at the end of the symbol duration. Thus, the equalizer uses the a priori channel estimate ${\widehat{h}}_{kk+1}$to refine the currently received OFDM symbol, rather than uses the a posteriori CFR ${\widehat{h}}_{kk}$.
Selection of the covariance matrices
In most applications of Kalman filtering, it is difficult to measure the variance of noises. In practice, the covariance matrices are a priori approximated by applying the best available knowledge and tuned empirically in the application. As shown in the state space model (12), the channel measurement y_{ k } is subject to the noise w_{ k }, the additive white complex Gaussian noise in the wireless channel. Since the transmission power and signaltonoise ratio (SNR) are usually available in a communication system, the elements of the variance matrix ${Q}_{w}$can be calculated by ${\sigma}_{w}^{2}=\frac{{P}_{\mathit{tx}}}{{10}^{S\phantom{\rule{0.12em}{0ex}}N\phantom{\rule{0.12em}{0ex}}R/10}}$, where P_{ tx } is the transmission power measured in Watts and SNR is in dB. Presuming a small process variance and linearization errors in (18), the values of ${\sigma}_{v}^{2}$and ${\sigma}_{\u03f5}^{2}$in ${Q}_{v}$are empirically selected from {0.1, 0.01, 0.001} according to the SNRs. At low SNRs, the channel estimate is less accurate due to large observation noise and thus a larger value is used for ${Q}_{u}$. At higher SNRs, a better channel estimation is expected and a smaller value is used for ${Q}_{u}$.
Summary
We now summarize the proposed method for channel estimation in LTE downlink:
Step 1. Initialize ${[{a}_{0}^{T}\phantom{\rule{0.36em}{0ex}}{h}_{0}^{T}]}^{T}$ when the first pilot symbol is received, make the first a priori prediction ${[{a}_{10}^{T}\phantom{\rule{0.36em}{0ex}}{h}_{10}^{T}]}^{T}$for next OFDM symbol and set k = 1; When a new OFDM symbol (k th symbol) has been received, repeat the following steps 2–6.
Step 2. Calculate y_{ k } by using DFT
Step 3. Estimate ${\widehat{x}}_{k}$ by equalizing y_{ k } with previous a posteriori${h}_{kk1}$;
Step 4. If y_{ k } is pilot symbol, set X_{ k } by the known pilot symbol x_{ k },else set X_{ k } by the estimated data symbol ${\widehat{x}}_{k}$,
Step 5. Correct a posteriori state estimation ${[{a}_{kk}^{T}\phantom{\rule{0.24em}{0ex}}{h}_{kk}^{T}]}^{T}$ from y_{k by} (22)–(24).
Step 6. Timeinterpolation: Predict a priori state estimate ${[{a}_{k1k}^{T}\phantom{\rule{0.36em}{0ex}}{h}_{k+1k}^{T}]}^{T}$by (19)–(21) for next symbol.
Step 7. Frequencyinterpolation: The CFR at data subcarriers for next symbol is interpolated using a DFTbased interpolation [8].
Step 8. k = k + 1, wait for next symbol and goes back to step 2.
It can be seen that, the proposed KFbased channel estimation scheme is a combination of the estimator (for pilot symbols) and the interpolator (for data symbols). When the pilot symbol is available at k th iteration, a direct observation of the channel state is obtained and the EKF works at the training mode giving the optimal estimate of CFR in the sense of minimum variance. In the followed six {k + 1, k + 2,…,k + 6} data symbols, the EKF interpolates the CFR in decisiondirected model until the next pilot symbol (k + 7th OFDM symbol) is received.
Simulation results and performance analysis
The simplified rural area channel model
Tap number  delay (μs)  Average path gains (dB) 

1  0  −2.748 
2  0.1302  −4.413 
3  0.2604  −11.052 
4  0.3906  −18.500 
5  0.5208  −18.276 
LTE downlink simulation parameters
Parameters  Values 

Bandwidth  5 MHz 
Total number of RBs  25 
Number of total subcarriers  300 
Number of pilot subcarriers in Pilot OFDM symbols  100 
Subcarrier spacing  15 kHz 
CP length  4.69 μs 
Slot duration  0.5 ms 
Sample Rate  7.68 MHz 
FFT size  512 
Modulation  QPSK 
Velocity  {50, 200} km/h 
As expected, A0 gives the best performance among all of the three methods, since it has the perfect CFR. The BER performance of A0 can be regarded as the BER’s lower bound. Obviously, the LS method has the poorest BER performance in all these three scenarios and the LMMSE is able to improve the BER performance. It can be seen that BERs of the proposed Kalman interpolation filter fall between the LMMSE’s performances and the performances of perfect channel, although the EKF shows a slightly higher BER than LMMSE in low SNRs (i.e. 0 and 5 dB). It is worth noting that the EKF is always better than the LS method. This is to be expected since the concept behind the observation equation in the proposed EKF method is the same as the LS method, where it assumes the CFRs at adjacent pilot subcarriers are independent. Nevertheless, compared to the LS estimation, the proposed Kalman interpolation filter shows a significant improvement. This is particularly obvious at high SNRs and highspeed environment. As seen in Figure 7, when using the proposed EKF instead of the LS estimator, a gain in SNR up to 8 dB can be obtained for certain BERs (e.g. 0.002) at highspeed application. The average SNR gain is about 3–5 dB.
Conclusions
This article focuses on channel estimation and interpolation for a timevarying multipath fading channel in 3GPP LTE downlink. The timevarying radio channel is modelled as an AR process represented in state space form and an EKF is developed for the purpose of both channel estimation at pilot symbols and interpolation at data symbols. The timevarying channel estimation is a joint state and parameter estimation problem, where both the channel taps and AR parameters need to be estimated simultaneously to achieve an accurate channel estimate. We convert the state model into an augmented system and a corresponding EKF is proposed. Furthermore, the interpolation channel estimate at data symbols are also integrated into the EKF and the proposed Kalman interpolation filter shows a good performance of estimating a timevarying channel in the 3GPP LTE downlink.
Appendix
Abbreviations
 3GPP:

The 3rd Generation Partnership Project (3GPP)
 AR:

autoregressive
 CIR:

channel impulse response
 DFT:

discrete Fourier transform
 EKF:

extended Kalman filter
 LMMSE:

linear minimum mean square error
 LS:

least square
 LTE:

longterm evolution
 OFDM:

orthogonal frequencydivision multiplexing
 PSAM:

pilot symbolaided modulation
 QPSK:

quadrature phaseshift keying
 SNR:

signaltonoise ratio.
Declarations
Acknowledgement
This study was supported by the EPSRC UKChina Science Bridges: R&D on 4 G Wireless Mobile Communications under grant EP/G042713/1.
Authors’ Affiliations
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