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 [24] 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. ). 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 lth path of the radio channel. () 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 ith row and the kth 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, the channel impulse response (CIR) of lth path at kth symbol, referred to as tap, g_{k} the CIR vector at kth symbol time, _{,}h_{k,n} the channel frequency response (CFR) at kth symbol time and nth subcarrier, the CFR vector at all subcarriers at kth symbol time, , h_{k} the CFR vector at N_{p} pilot subcarriers at kth symbol time, _{,}a_{k} the timecorrelation coefficients of CFR at kth symbol time, x_{k} the vector of transmitted OFDM symbols at pilot subcarriers at kth symbol time and y_{k} the corresponding received OFDM symbol vector of x_{k}.
System model
Figure 1 describes the LTE downlink baseband system used in this article. Here, we only consider baseband processing and omit all analogue components, higher layer protocols and application processing. The baseband processor receives the digitized signal as complex samples from the analoguetodigital convertors and posts the decoded data stream to the higher layer protocol and the application processor.
Figure 1. LTE downlink frame structure and the timefrequency allocation of pilot symbols (one transmitting antenna scenario).
Pilot symbols in LTE downlink
As depicted in Figure 1, a radio frame of the LTE downlink has duration of 10 ms and consists of ten subframes each of 1 ms. Each subframes has two 0.5ms time slots with each slot consisting of OFDM symbols (the values of for various configurations are given in Table 1). The transmitted downlink signal is represented as a timefrequency resource grid. Each small box within the grid represents a single subcarrier for one symbol period and is referred to as a resource element. Note that in MIMO applications, there is a resource element mapping graph for each transmitting antenna. A resource block (RB) is defined as consisting of consecutive subcarriers for one slot ( OFDM symbols). An RB is the smallest unit of bandwidthtime resource allocation assigned by the base station scheduler, and the specification for the parameters of one RB is shown in Table 1.
Table 1. Physical RBs parameters
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
In this article, we consider an LTE downlink system with N subcarriers over a Rayleighfading channel. For the purpose of analysis, the following notation and assumptions are taken in this article.
(1) The system bandwidth is B = 1/T, where T is the duration of one timechip. The duration of one OFDM symbol is , 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, .
(4) The Rayleighfading channel varies in consecutive OFDM symbols, but is assumed constant within one OFDM symbol.
The timevarying multipath channel can be represented in the continuous timedomain function by a collection of paths
where the lth 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.
In the OFDM implementation of the 3GPP LTE, the transmitted and received signals are sampled for D/A and A/D conversion with an interval of chip duration T, the CIR (1) in the continuous timedomain is converted into an equivalent discrete channel model with sampling interval T. We define as representing the complex magnitude of the lth path with delay lT during the kth OFDM symbol. The equivalent discrete model of the radio channel (1) is therefore
Hence, the discrete CIR model can be represented by a lengthL CIR vector g_{k}
Strictly speaking, g_{k} is only an approximation of at kth OFDM symbol (). When the multipath taps do not fall in the discrete sampling grid (i.e., ), 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.
In the frequencydomain, the frequency response of the CIR g_{k} at kth OFDM symbol is
where , denoting the CFR of nth subcarrier at kth OFDM symbol time is converted from the timedomain CIR via the discrete Fourier transform (DFT)
The relationship between the CIR in timedomain and CFR in frequency domain can be described in matrix notation
where is the first L columns of the N × N DFT matrices F. And F is denoted by
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.
It is easy to verify that the channel coefficients of the timevarying CFR can be modelled by the following dynamic AR process [1,11,12]:
where α_{n} represents the time correlation of the channel response between kth and (k + 1)th OFDM symbols at the nth subcarrier. is a mutually independent zeromean Gaussian complex white noise representing the modelling error.
LTE OFDM reception and channel estimation
In order to estimate the CFR as defined in (4), N_{p} pilot symbols are inserted sparsely among N subcarriers at kth OFDM symbol duration following the comb pattern shown in Figure 1. Let denote the transmitted pilot vector of N_{p} known pilot symbols at the kth OFDM symbol, denotes the vector of the received pilot symbols. After CP removal, the received pilot symbols can be expressed as
where is an N_{p} × N_{p} diagonal matrix with transmitted pilot symbols x_{k} as its diagonal elements,
Here, is an additive white complex Gaussian noise with covariance matrix and is the CFR at pilot subcarriers at kth OFDM symbol.
The goal of channel estimation is to estimate the whole CFR for all data carriers from h_{k} at these N_{p} pilot symbols with as high accuracy as possible. This is an optimization problem described as
where 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 kth 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 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 kth OFDM symbol, to obtain minimum variance estimates of the timevarying multipath CFR h_{k} and interpolate h_{k} to the followed six data symbols at the pilot subcarriers until the next pilot symbol (k + 7th OFDM symbol) is received.
Augmented state space model
Considering a timevarying channel described in Equation (8), the CFR at pilot subcarriers can be described as a state space model
where h_{k} is the state variable to be estimated, 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 and , 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}.
Considering that A_{k} is a spares matrix in most cases, without loss of generality, we assume A_{k} has N_{A} unknown entries to be estimated and let a vector a_{k} denote all the N_{A} unknown entries as follows:
is an N_{A} × 1 column vector formed by stacking all unknown entries of the matrix A_{k} in a rowwise order. The timecorrelation parameters are now represented by a_{k} which is the vector to be estimated. For the purpose of clarification, A_{k} is represented by A(a_{k}) explicitly in the following. Assuming a random walk model for the parameter a_{k}, then Equation (12) becomes
where ϵ_{k} denotes the process noise of a_{k} and is an independent, zeromean Gaussian noise with covariance . In order to jointly estimate the state and parameters, a new augmented state z_{k} is defined as
and the channel state space model Equation (14) turns into an augmented system
where with covariance matrix and f(z_{k}) is the nonlinear state transition function
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.
The linearization procedure is included in the Appendix where the derivation of the EKF algorithm for a general matrix A(a_{k}) is demonstrated. The basic concept is to form the Taylor approximation of the nonlinear transition function. The resulting linear state space model approximating the AR model (12) is
Applying the standard KF to the model (18) is straightforward. The resulting EKF algorithm for the joint estimation of CFR h_{k} and CFR’s time correlation coefficients a_{k} works in an iterative prediction–correction cycle. The prediction projects forward (in time) the current estimate and error covariance P_{k} at kth OFDM symbol to obtain the a priori estimates and for the next (k + 1)th OFDM symbol. The correction adjusts the projected estimates and to obtain an improved a posteriori estimate by using an actual measurement of received symbol y_{k+1} at (k + 1)th OFDM symbol. Here, the subscript corresponds to onestep a priori prediction, corresponds to a posteriori correction and is denoted by k for the purpose of short notation. More specifically, the filtering algorithm is presented as follows:
1. Prediction (before receiving a OFDM symbol):
where
and is the covariance of noises .
2. Correction (once the reception of the OFDM symbol has completed):
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 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.
Figure 2 illustrates the block diagram of the baseband channel model and the proposed Kalman interpolation filter for LTE downlink channel equalizer. The EKF works in an iterative prediction–correction manner and, in the application of LTE downlink channel estimation, each iteration corresponds to the duration of an OFDM symbol. However, due to the fact that the known pilot symbols are inserted sparsely into the unknown data symbols, the coefficient matrix X_{k} is not always available at each iteration. Like most adaptive algorithms, two working modes, namely, training mode and decisiondirected interpolation mode, are adopted in the proposed Kalman interpolation filter to address this issue.
Figure 2. Channel estimation and interpolation at pilot subcarriers.
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 by the decoder and the EKF is fed by the 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 of the transmitted data symbols on the basis of the previous, a priori channel estimate . In the iteration of the OFDM data symbol, is also fed to the EKF to calculate a posteriori channel estimate and a priori channel estimate . 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 () 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.
For CFR h_{k}, we shall use the conventional version of an LS estimation to get the initial value. When the first group of pilot symbols is received, the LS method is performed as follows:
where 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 is first estimated from y_{k} by using update equations (22)–(24). Then the a priori estimate 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 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 ,.
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 to refine the currently received OFDM symbol, rather than uses the a posteriori CFR .
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 can be calculated by , 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 and in 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 . At higher SNRs, a better channel estimation is expected and a smaller value is used for .
Summary
We now summarize the proposed method for channel estimation in LTE downlink:
Step 1. Initialize when the first pilot symbol is received, make the first a priori prediction for next OFDM symbol and set k = 1; When a new OFDM symbol (kth symbol) has been received, repeat the following steps 2–6.
Step 2. Calculate y_{k} by using DFT
Step 3. Estimate by equalizing y_{k} with previous a posteriori;
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 ,
Step 5. Correct a posteriori state estimation from y_{k by} (22)–(24).
Step 6. Timeinterpolation: Predict a priori state estimate 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 kth 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
In this section, simulation is performed to validate the performance of the proposed Kalman interpolation filter for LTE downlink systems. A simplified rural area model defined by 3GPP [17] is adopted to configure the Rayleigh channel with additive white Gaussian noise and the parameters are listed in Table 2. The LTE downlink simulation parameters are listed in Table 3. The total number of subcarriers is 512 with 300 of them used for data/pilot transmission and a quadrature phaseshift keying (QPSK) modulation employed. For simplicity, the raw bits randomly generated are not coded with turbo coding schemes. In the single input single output scenario, 100 of the 300 subcarriers are used for carrying pilot symbols during the pilot OFDM symbol time period. Three speeds of user equipment are simulated, namely, 50 200 and 300 km/h. For each speed, the simulation is repeated 10 times (10 runs) in order to obtain reliable statistics and each simulation run simulates the transmitting/receiving four LTE downlink subframes containing 56 OFDM symbols.
Figure 3 illustrates a plot of the channel surface for the urban channel model at a moving speed of 200 km/h, where the Doppler frequency is around 480 Hz. This plot shows the timevarying and frequencyselective nature of the channel gain and provides an image of the true values of the CFR. Studying the channel surface indicates that fluctuations in the frequency are clearly visible, but relatively smoothly varying in time, implying an AR process would be able to represent the channel’s time correlation. The channel surface also suggests that a linear interpolation may not be good for such a nonlinear CFR.
Figure 3. Channel surface of an LTE radio channel at a moving speed of 200 km/h.
The simulations are carried out at different noise levels with the SNR varying from 0 to 40 dB at a step size of 5 dB. Figure 4 shows an example of the CFR estimation errors at the 100 pilot subcarriers in one simulation run (with 4 subframes containing 56 OFDM symbols) at SNR = 20 dB and moving speed 20 km/h. Figure 4a depicts the CFR surface estimation error given by the proposed EKF scheme and Figure 4b depicts the estimation errors of the LS scheme, where the improvement of CFR estimation in the proposed scheme can be seen clearly and the mean square error (MSE) of the EKF is 0.066 and that of the LS is 0.09. The smaller CFR estimation error demonstrates the proposed EKF’s ability to filter the noises in observation and to track the timevarying channel parameters. Particularly, towards the edge of the LTE downlink subframe (i.e. at the 14th, 28th, 42th, 56th OFDM symbols), a larger estimation error occurs in the LS estimation which can be seen clearly in Figure 4b. This is caused by the extrapolations errors in the LS scheme as no pilot symbols are inserted at the edge of each subframe. However, it is worth noting that the proposed EKF’s estimation errors may have peaks at some data symbols, due to the fact that the incorrect data symbol decision is fed back to the EKF in the decisiondirected mode. If a large deviation occurs and thus makes the received OFDM data symbol far from its original QAM constellation position but nearer to another constellation position, the quantization procedure will result in a wrong decision of the data symbol. When the incorrect data symbol decision is fed back to the EKF, it works as an incorrect ‘observation’ resulting in the EKF giving an abrupt change in state estimation. As a result, a sudden jump appears in the CFR estimates and may result in error propagation, making more errors in the following data symbol decision. If these decision errors are infrequent enough, the effects of these errors decay away and the decisiondirected equalizer’s performance remains similar to that of the training mode.
Figure 4. CFR estimation errors (a) for the proposed EKF scheme and (b) for the LS scheme.
Figure 5 shows the average CFR estimation MSEs of the LS and EKF schemes at different SNRs. It can be seen that the EKF achieves a smaller estimation errors and gives a better CFR estimation.
Figure 5. Average mean square estimation errors at various SNRs (200 km/h). Blue solid line with triangle: the average MSE of channel estimates by LS method. Red solid line with star: the average MSE of channel estimates by the proposed EKF method.
The BER performances are plotted in Figures 6, 7 and 8. Figure 6 is for lowspeed environment (50 km/h), Figures 7 and 8 are for highspeed environment (200 and 300 km/h), respectively. In the BER comparisons, the popular (LMMSE) algorithm [5] is also employed. Note that since the proposed EKF method is based on the LS estimation, we explicitly denote it by ‘EKF with LS’ in the legends of these figures. As a performance benchmark, the BER performances of a perfect channel estimation algorithm (denoted by A0 in Figures 6, 7 and 8) are also depicted, where the perfect channel estimation refers to the actual CFR being known by the receiver in advance.
Figure 6. BER performance comparison at 50 km/h (A0 denotes a perfect channel estimation algorithm where the actual CFR is known to the receiver). Blue solid line with triangle: the BER performance of LS method. Red solid line with star: the BER performance of the proposed EKF method. Blue solid line with circle: the BER performance of LMMSE method. Blue solid line with square: the BER performance of the perfect channel estimation method. The perfect channel estimation means that the actual CFR is known to the receiver.
Figure 7. BER performance comparison at 200 km/h (A0 denotes a perfect channel estimation algorithm where the actual CFR is known to the receiver). Blue solid line with triangle: the BER performance of LS method.Red solid line with star: the BER performance of the proposed EKF method. Blue solid line with circle: the BER performance of the LMMSE method. Blue solid line with square: the BER performance of the perfect channel estimation method. The perfect channel estimation means that the actual CFR is known to the receiver.
Figure 8. BER performance comparison at 300 km/h (A0 denotes a perfect channel estimation algorithm where the actual CFR is known to the receiver).
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
Applying the firstorder Taylor approximation to the nonlinear state transition function f(z_{n}) around in Equation (17), the state equation (16) becomes
where is the linearization error and
Here, we assume that A(a_{k}) is independent of h_{n}. Recalling the definitions of a_{k} and A(a_{k}), it is easy to verify that is an N_{p} × N_{A} blockdiagonal matrix H_{n}
where denotes the Kronecker product and the operator removes these knowncolumns of . The known column is the th column when the ithrowjthcolumn entry of A is known. Hence, substituting into Equation (26), we have
And the linear state space model approximating the AR model (12) is
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.
Competing interests
The authors declare that they have no competing interests.
Acknowledgement
This study was supported by the EPSRC UKChina Science Bridges: R&D on 4 G Wireless Mobile Communications under grant EP/G042713/1.
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