Channel estimation with co-channel pilots suppression

Abstract

A user equipment device operating in a cellular communications system includes a neighbor cell searcher configured to receive a first reference signal transmitted from a base station of a first cell of the cellular communications system and determine first channel information associated with the first cell, and receive a second reference signal transmitted from a base station of a second cell of the cellular communications system and determine second channel information associated with the second cell. A channel estimator is configured to generate correlations between times and frequencies associated with reception of the first reference signal and the second reference signal, and generate a channel estimate corresponding to the first reference signal based on the first channel information, the second channel information, and the correlations.

Claims

1. A user equipment device operating in a cellular communications system, the user equipment device comprising: a neighbor cell searcher configured to receive a first reference signal transmitted from a base station in a first cell of the cellular communications system and determine first channel information for the first reference signal, wherein the determined first channel information includes a first gain, a first spreading sequence, and a first channel response of the first reference signal, and receive a second reference signal transmitted from a base station in a second cell of the cellular communications system and determine second channel information for the second reference signal, wherein the determined second channel information includes a second gain, a second spreading sequence, and a second channel response of the second reference signal, wherein the first reference signal and the second reference signal include a known pattern of bits, and wherein the first cell is different from the second cell; and a channel estimator configured to generate a model of the first reference signal and the second reference signal, wherein generating the model includes calculating a vector of a combination of the first reference signal and the second reference signal using a sum of (i) a first term incorporating the first gain, the first spreading sequence, and the first channel response and (ii) a second term incorporating the second gain, the second spreading sequence, and the second channel response, calculate a signal filter using the model of the first reference signal and the second reference signal, and generate a channel estimate corresponding to the first reference signal using the signal filter and the model of the first reference signal and the second reference signal.

2. The user equipment device of claim 1 wherein the first spreading sequence and the second spreading sequence are different.

3. The user equipment device of claim 1, wherein the neighbor cell searcher is configured to i) associate the first reference signal with first subcarriers, and ii) associate the second reference signal with second subcarriers.

4. The user equipment of claim 1, further comprising a pre-estimator configured to generate an output indicating at least one of a signal to noise ratio (SNR) of the first reference signal, a Doppler frequency of the first reference signal, and an estimated channel profile associated with the first cell, the channel estimator being configured to generate the channel estimate further based on the output.

5. The user equipment of claim 1, wherein the channel estimator is further configured to generate time-frequency bins associated with transmission of the first reference signal and the second reference signal.

6. The user equipment of claim 1, wherein the channel estimator is configured to generate the channel estimate by generating a linear minimum mean square error estimate.

7. The user equipment of claim 1, wherein the channel estimator is configured to calculate the signal filter by generating time and frequency correlation matrices.

8. A method of operating a user equipment device in a cellular communications system, the method comprising: receiving a first reference signal transmitted from a base station in a first cell of the cellular communications system and determining first channel information for the first reference signal, wherein the determined first channel information includes a first gain, a first spreading sequence, and a first channel response of the first reference signal; receiving a second reference signal transmitted from a base station in a second cell of the cellular communications system and determining second channel information for the second reference signal, wherein the determined second channel information includes a second gain, a second spreading sequence, and a second channel response of the second reference signal, wherein the first reference signal and the second reference signal include a known pattern of bits, and wherein the first cell is different from the second cell; generating a model of the first reference signal and the second reference signal, wherein generating the model includes calculating a vector of a combination of the first reference signal and the second reference signal using a sum of (i) a first term incorporating the first gain, the first spreading sequence, and the first channel response and (ii) a second term incorporating the second gain, the second spreading sequence, and the second channel response, calculating a signal filter using the model of the first reference signal and the second reference signal; and generating a channel estimate corresponding to the first reference signal using the signal filter and the model of the first reference signal and the second reference signal.

9. The method of claim 8, wherein the first spreading sequence and the second spreading sequence are different.

10. The method of claim 8, wherein the determining the first channel information includes associating the first reference signal with first subcarriers, and the determining the second channel information includes associating the second reference signal with second subcarriers.

11. The method of claim 8, further comprising: generating an output indicating at least one of a signal to noise ratio (SNR) of the first reference signal, a Doppler frequency of the first reference signal, and an estimated channel profile associated with the first cell, wherein generating the channel estimate includes generating the channel estimate further based on the output.

12. The method of claim 8, further comprising generating time-frequency bins associated with transmission of the first reference signal and the second reference signal.

13. The method of claim 8, wherein the generating the channel estimate includes generating the channel estimate by generating a linear minimum mean square error estimate.

14. The method of claim 8, wherein calculating the signal filter includes calculating the signal filter by generating time and frequency correlation matrices.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The subject matter of the present disclosure will be more fully understood by reference to the following detailed description together with the accompanying drawings in which:

(2) FIG. 1 is a schematic illustration of a set of cells of a cellular communications system and user equipment moving through the cells, constructed and operative in accordance with an embodiment;

(3) FIG. 2 is a block diagram illustrating a channel estimator, useful in the communications system of FIG. 1;

(4) FIG. 3 is a schematic illustration useful in understanding an estimation of f.sub.Doppler, forming part of the channel estimator of FIG. 2;

(5) FIG. 4 is a schematic illustration useful in understanding an estimation of a channel profile, forming part of the channel estimator of FIG. 2; and

(6) FIG. 5 is a block diagram illustration of the operations of a filter calculator forming part of the channel estimator of FIG. 2.

(7) It is noted that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. Furthermore, where appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

(8) Reference is now made to FIG. 1, which illustrates an example of a cellular communications system that includes two cells, cell 12A and cell 12B, which are each served by a respective base station 11. User equipment devices 14 may be situated at various locations in cells 12A and 12B, and may move within or between the cells. A different pilot signal is transmitted within each cell 12.

(9) Cells may have a single transmitter or multiple transmitters. For the sake of clarity, the following explanation will assume a single transmitter per cell. An extension to multiple transmitters may be found at the end of the disclosure.

(10) As seen in FIG. 1, device 14B receives a single reference signal since it is well within cell 12A. The channel estimation for device 14B may be relatively accurate since it has little noise interfering with the estimation.

(11) On the other hand, device 14A is at the edge of cells 12A and 12B and thus may receive reference signals from both cell 12A and cell 12B, as indicated by the dashed lines. As device 14A moves into cell 12B, device 14A may receive the reference signal of cell 12B and may attempt to determine the channel qualities of cell 12B. Unfortunately, since device 14A is still at the edge of cell 12B, there may be significant interference from the reference signal transmitted by the transmitter in cell 14A.

(12) Conventionally, the channel estimator of the user equipment models this interference as noise. However, in accordance with an embodiment and as shown in FIG. 1, user equipment devices 14 may comprise a neighbor incorporating channel estimator 30 which may utilize neighbor information, already determined by a neighbor cell searcher 32 forming part of most conventional user equipment devices.

(13) Neighbor cell searcher 32 may facilitate mobility from one cell to another by determining the pilot patterns of neighboring transmitters, to prepare for switching to the next cell, whichever it may be. Neighbor cell searcher 32 may periodically search for signals from neighboring cells, may analyze their respective reference signals to determine which cells are most suitable for communication and, for selected signals, may determine their respective power levels and their scrambling or spreading sequences. In some instances, searcher 32 may associate each of the reference signals with the respective subcarriers on which the reference signals were transmitted. A suitable neighbor cell searcher that is compliant with emerging LTE and LTE advance standards is described in the book by Erik Dahlman et al., 3G Evolution HSPA and LTE for Mobile Broadband, 2nd edition, pages 421-425, for example.

(14) In accordance with an embodiment, neighbor incorporating channel estimator 30 may incorporate various neighbor information into the channel estimation. For example, interfering signals, such as pilot signals that are received from neighboring transmitters, may be modeled rather than treated as white noise. By modeling interfering, neighboring signals, channel estimator 30 may improve its suppression of co-channel (i.e. neighbor) reference signals to thereby produce an improved, channel estimation. The neighbor information may include, for example, the power, the spreading sequences, the scrambling sequences, or other suitable information that distinguishes the reference signals of the different cells.

(15) Reference is now made to FIG. 2, which is a block diagram illustrating channel estimator 30. In accordance with an embodiment, channel estimator 30 receives inputs from neighbor cell searcher 32 and a pre-estimator 36, as shown. Channel estimator 30 may comprise, for example, an MMSE (minimum mean square error) filter calculator 40, to estimate the appropriate filters from the information it receives from searcher 32 and pre-estimator 36, and an MMSE filter 42 that employs the filter calculated by MMSE filter calculator 40 to filter incoming reference signals to determine the channel estimate ĥ.sub.0.

(16) Pre-estimator 36 may determine a signal to noise ratio (SNR) of the current reference signal (i.e. the reference signal of the currently active cell), a Doppler frequency f.sub.doppler of the current reference signal and an estimated channel profile.

(17) MMSE filter calculator 40 may be designed, for instance, for cellular OFDMA (orthogonal frequency division multiple access) systems, such as LTE, in which the reference signals of the cells are synchronized up to a cyclic prefix length (i.e. only their prefixes differ), and each cell uses the same frequencies. Each cell i has its own reference signal with its own, unique spreading or scrambling sequence s.sub.i, and different cells may have different channel gains g.sub.i.

(18) Moreover, in OFDMA, each user equipment device is assigned a plurality of “sub-carrier” frequencies upon which to transmit. At each transmission time (known as an “OFDM symbol”), the devices transmit on their currently assigned frequency, typically changing to another frequency for the next transmission time. A “sub-frame” may typically include multiple transmissions, such as 14 transmissions. Thus, each frame may comprise a block of multiple frequencies by multiple time stamps and the devices are assigned a plurality of time-frequency (T/F) “bins”. To generate a received signal, the devices may order the received data in the form of a T/F matrix.

(19) The reference signals of some of the cells may be assigned to transmit during the same time-frequency bins. However, as discussed hereinabove, each reference signal has its own spreading or scrambling sequences s.sub.i.

(20) Each cell i transmits its reference signal, defined by its spreading/scrambling sequence s.sub.i. The path through which the transmitted signal travels to reach the user equipment device affects the received signal, both by changing its power (or gain g.sub.i) and by distorting the signal, where the distortion is known as the channel h.sub.i. Thus, the signal reaching the user equipment from cell i is g.sub.i.Math.S.sub.i.Math.h.sub.i where:

(21) g.sub.i is the gain of cell i;

(22) S.sub.i is a diagonal matrix with the spreading or scrambling sequences s.sub.i for the reference signal from cell i (each transmitted symbol of the reference signal is assumed to have a value of the diagonal S.sub.i); and

(23) h.sub.i is the channel response of the transmission (Tx) antenna of cell i to the reception (Rx) antenna of the user equipment device at the T/F bins assigned to the reference signals (h.sub.i is typically normalized to unity power).

(24) In accordance with an embodiment, channel estimator 30 may be designed to model all of the reference signals arriving to it, thus:

(25) $\begin{array}{cc}\stackrel{\_}{y}=\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}{g}_i.Math.S_i.Math.{\stackrel{\_}{h}}_i+\stackrel{\_}{n}& \mathrm{Equation}1\end{array}$

(26) where: y is a vector of the received reference signal data;

(27) n is any remaining noise at the T/F bins assigned to the reference signal; and

(28) NumCells is the number of cells that are assumed in the multi-cell model where, without loss of generality, the 0.sup.th cell is the cell having the desired transmitter transmitting the reference signal of interest. In general, only the neighboring cells will be included in the model. Typically, these are the neighboring cells which neighbor cell searcher 32 has deemed to be significant and thus, are expected to have interfering reference signals.

(29) It is noted that equation 1 models the reference signals of neighboring cells rather than considering them sources of noise. The noise element n merely models additional noise sources whose properties are unknown.

(30) The standard LMMSE (Linear Mean Square Error) solution to an equation of the style of equation 1 is:
{circumflex over (h)}.sub.0=W.sup.+.Math.y
W=R.sub.yy.sup.−1.Math.R.sub.yh
R.sub.yy=E{y.Math.y.sup.+}
R.sub.yh=E{y.Math.g.sub.0.Math.h.sub.0.sup.+}   Equation 2

(31) Where, if X is a general term for a matrix, X.sup.−1 is the inverse of matrix X and X.sup.+ is the conjugate transpose of matrix X;

(32) ĥ.sub.0 is the desired output, the estimate for the channel from the 0.sup.th cell, the cell of interest;

(33) R.sub.yy is an autocorrelation matrix of the received reference signal data;

(34) R.sub.yh is a cross-correlation matrix of received signal with channel; and

(35) W is the signal filter matrix for MMSE filter 42.

(36) It is noted that, in equation 2, the reference signals from the other cells (cells 1−NumCells-1) implicitly affect the correlation matrices R.sub.yy and R.sub.yh. The present disclosure describes an example solution for estimating R.sub.yy and R.sub.yh and, from them, for estimating the channel estimate ĥ.sub.0.

(37) As can be seen in the following equations 3-5, autocorrelation matrix R.sub.yy may be defined from the gains g.sub.i of each cell, the sequence matrices S.sub.i, an autocorrelation matrix R.sub.hh of the channel estimate (which will be determined later), and a noise variance σ.sub.n. The matrix R.sub.yh may be defined from the gain, g.sub.0, of the cell of interest, the sequence matrix S.sub.0 of the cell of interest and the channel autocorrelation matrix R.sub.hh.

(38) $\begin{array}{cc}{R}_{\mathrm{yy}}=E\left\{\stackrel{\_}{y}.Math.{\stackrel{\_}{y}}^{+}|g_i,i=0,1,2,\mathrm{.Math.},\mathrm{NumCells}-1\right\}=\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}{p}_i.Math.S_i.Math.R_{\mathrm{hh}}\left(i\right).Math.S_i^{+}+\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}\underset{\underset{j\ne i}{j=0}}{\overset{\mathrm{NumCells}-1}{.Math.}}{g}_i.Math.g_j.Math.S_i.Math.E\left\{{\stackrel{\_}{h}}_i.Math.{\stackrel{\_}{h}}_j^{+}\right\}.Math.S_j^{+}+{\sigma }_n^2.Math.I& \mathrm{Equation}3\end{array}$

(39) Equation 3 may be simplified by assuming that the channels from different cells are “uncorrelated” (i.e. not related to one another). Another simplification assumes that the channels from all the transmitters have the same general correlation matrix R.sub.hh rather than individual channel correlation matrices R.sub.hh(i). Thus:
E{h.sub.i.Math.h.sub.j.sup.+}=R.sub.hh.Math.δ.sub.i-j
R.sub.hh=R.sub.hh(i)=E{h.sub.i.Math.ĥ.sub.i.sup.+}, i=0, 0.1, 2, . . . , NumCells−1   Equation 4

(40) These assumptions may be particularly suitable when all the modeled reference signals are transmitted from the same geographical site but may be utilized when the signals are from neighboring cells.

(41) With the assumptions of equation 4, equation 3 may be rewritten as:

(42) $\begin{array}{cc}{R}_{\mathrm{yy}}=\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}{p}_i.Math.S_i.Math.R_{\mathrm{hh}}.Math.S_i^{+}+{\sigma }_n^2.Math.I{R}_{\mathrm{yh}}=E\left\{y.Math.g_0.Math.h_0^{+}|g_i,i=0,1,2,\mathrm{.Math.},\mathrm{NumCells}-1\right\}=p_0.Math.S_0.Math.R_{\mathrm{hh}}{p}_i=g_i^2& \mathrm{Equation}5\end{array}$

(43) where p.sub.i is the power of the signal from cell i.

(44) Gain (Power) and Noise Variance Estimation

(45) In order to determine the correlation matrices R.sub.yy and R.sub.yh of equation 5, filter calculator 40 may first estimate the power p.sub.i of each cell from the spreading/scrambling sequences s.sub.i (in the form of the matrix s.sub.i). For example, a Least Squares based approach may be utilized. It gives, as a by product, also an estimate of the noise variance Υ.sub.n.

(46) Repeating equation 5 for R.sub.yy.

(47) $\begin{array}{cc}{R}_{\mathrm{yy}}=\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}{p}_i.Math.S_i.Math.R_{\mathrm{hh}}.Math.S_i^{+}+{\sigma }_n^2.Math.I=\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}{p}_i.Math.B_i+{\sigma }_n^2.Math.I{B}_i=S_i.Math.R_{\mathrm{hh}}.Math.S_i^{+}& \mathrm{Equation}6\end{array}$

(48) Assuming that the channels vary slowly, channel autocorrelation R.sub.hh also varies slowly, and thus, the channel autocorrelation R.sub.hh from the previous estimation may be utilized to determine B.sub.i. Equation 6 may be rewritten using only the 1.sup.st column of each matrix in equation 6.

(49) $\begin{array}{cc}{\widehat{\stackrel{\_}{r}}}_{\mathrm{yy}}=\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}{p}_i.Math.b_i+{\sigma }_n^2.Math.\stackrel{\_}{e}=\left[\begin{array}{ccccc}{\stackrel{\_}{b}}_0& {\stackrel{\_}{b}}_1& .Math.& {\stackrel{\_}{b}}_{\mathrm{NumCells}-1}& \stackrel{\_}{e}\end{array}\right].Math.\left[\begin{array}{c}{p}_0\\ p_1\\ .Math.\\ p_{\mathrm{numCells}-1}\\ {\sigma }_n^2\end{array}\right]=A.Math.\stackrel{\_}{\theta }& \mathrm{Equation}7\end{array}$

(50) where

(51) $\begin{array}{cc}\stackrel{\_}{e}=\left[\begin{array}{c}1\\ 0\\ .Math.\\ 0\end{array}\right],A=\left[\begin{array}{ccccc}{\stackrel{\_}{b}}_0& {\stackrel{\_}{b}}_1& .Math.& {\stackrel{\_}{b}}_{\mathrm{NumCells}-1}& \stackrel{\_}{e}\end{array}\right],\stackrel{\_}{\theta }=\left[\begin{array}{c}{p}_0\\ p_1\\ .Math.\\ p_{\mathrm{NumCells}-1}\\ {\sigma }_n^2\end{array}\right]& \mathrm{Equation}8\end{array}$

(52) The least squares solution for that case is:
{circumflex over (θ)}.sub.LS=(A.sup.+.Math.A).sup.−1.Math.A.sup.+.Math.{circumflex over (r)}.sub.yy  Equation 9

(53) where, as per equation 8, {circumflex over (θ)}.sub.LS is a vector of the estimated powers p.sub.i and the noise variance σ.sub.n.sup.2 and the {circumflex over (r)}.sub.yy from the previous estimation is utilized. It is noted that equation 9 utilizes an empirical value for the 1.sup.st column vector r.sub.yy. For example, a vector may be some combination of the 1.sup.st column of the previous estimate of R.sub.yy and the current empirical estimation.

(54) Given the per-cell gains g.sub.i (or power p.sub.i) and the per-symbol noise variance σ.sub.n.sup.2, generated from equation 9, channel correlation matrix R.sub.hh matrix may be estimated, as described hereinbelow.

(55) Assuming that the channel and the received reference signal can be modeled as stationary processes (i.e. their statistics do not change over time), the (i,j)th element of R.sub.hh may be denoted by:
[R.sub.hh].sub.ij=E{h(t.sub.i,f.sub.i).Math.h*(t.sub.j,f.sub.j)}=r.sub.h(t.sub.i−t.sub.j,f.sub.i−f.sub.j)   Equation 10

(56) where (t.sub.i, f.sub.i) are the time offset and frequency offset of the (i,j)th element of R.sub.hh, respectively. The assumption that the paths of the reference signals are independent leads to the separability of the time and frequency correlations, r.sub.h.sup.t(Δt) and r.sub.h.sup.f(Δf), respectively, as follows:
r.sub.h(Δt,Δf)=r.sub.h.sup.t(Δt).Math.r.sub.h.sup.f(Δf)   Equation 11

(57) A consequence of the channel gain parameterization is that:
r.sub.h.sup.t(0)=1
r.sub.h.sup.f(0)=1  Equation 12

(58) Channel Time Correlation Estimation

(59) One approach to determine the time offset is to determine a time correlation of fading channels, known as Rayleigh fading. The “Jakes Model” (described in the Wikipedia article on “Rayleigh fading” of Nov. 9, 2008) models time correlation as a Bessel function J.sub.0 of the 1.sup.st kind. Filter calculator 40 may calculate the time correlation r.sub.h.sup.t(Δt) as follows:

(60) $\begin{array}{cc}{r}_h^t\left(\Delta t\right)=J_0\left(2.Math.\pi .Math.f_{\mathrm{Doppler}}.Math.\Delta t\right)=\underset{k=0}{\overset{\infty }{.Math.}}{\left(-1\right)}^k.Math.\frac{{\left(0.25.Math.{\left(2.Math.\pi .Math.f_{\mathrm{Doppler}}.Math.\Delta t\right)}^2\right)}^k}{{\left(k!\right)}^2}& \mathrm{Equation}13\end{array}$

(61) where the only parameter required to effect this calculation is f.sub.Doppler, and f.sub.Doppler may be generated by pre-estimator 36. Pre-estimator 36 may generate a matrix of values of r.sub.h.sup.t(Δt), for different values of Δt, for example, of 0, 1, 2 . . . seconds.

(62) Reference is briefly made to FIG. 3, which illustrates one suitable approach that may be used by pre-estimator 36 for estimating f.sub.Doppler. The process may include the following steps: 1. Estimate the spectrum of the reference signal in the time domain. The result is shown in FIG. 3. 2. Define Fdnegative and Fdpositive as the frequencies that have 90% of the power in the negative and positive regions of the spectrum. 3. Estimate f.sub.Doppler to be the frequency that is the maximum of the values of Fdnegative and Fdpositive:
f.sub.Doppler=max(∥Fdnegative∥,Fdpostive)  Equation 14

(63) Channel Frequency Correlation Estimation

(64) One approach for the frequency correlation is motivated by a discrete multipath model for the channel. The model results in the following expression:

(65) $\begin{array}{cc}{r}^f\left(\Delta f\right)=r^f\left(\Delta k\frac{f_s}{N}\right)=E\left\{H\left(k_1\right).Math.H^{*}\left(k_1+\Delta k\right)\right\}=E\left\{\underset{l=0}{\overset{N_{\mathrm{Paths}}-1}{.Math.}}{h}_l.Math.e^{-\frac{j2\pi k_1{\tau }_l}{{\mathrm{NT}}_s}}.Math.\underset{m=0}{\overset{N_{\mathrm{Paths}}-1}{.Math.}}{h}_m^{*}.Math.e^{\frac{j2\pi \left(k_1+\Delta k\right){\tau }_m}{{\mathrm{NT}}_s}}\right\}\underset{\Downarrow }{E}\left\{h_l{h}_m^{*}\right\}=E\left\{{.Math.h_l.Math.}^2\right\}{\delta }_{l-m}={\sigma }_l^2{\delta }_{l-m}=\underset{l=0}{\overset{N_{\mathrm{Paths}}-1}{.Math.}}{\sigma }_l^2.Math.e^{-\frac{j2\mathrm{\pi \Delta }k{\tau }_l}{{\mathrm{NT}}_s}}=\underset{l=0}{\overset{N_{\mathrm{Paths}}-1}{.Math.}}{\sigma }_l^2.Math.e^{-j2\mathrm{\pi \Delta }f{\tau }_l}& \mathrm{Equation}15\end{array}$

(66) Where:

(67) τ.sub.1 is the 1'th path delay spread;

(68) σ.sub.l.sup.2 is the 1'th path power;

(69) Δk is a multiplier of

(70) $\frac{f_s}{N};$
and

(71) 0$T=\frac{1}{f_s}$

(72) For example, one solution for determining the frequency correlation may be based on a time tracking operation in the time domain. The resultant equation is:

(73) $\begin{array}{cc}{\hat{r}}^f\left(\Delta f\right)=\underset{l=0}{\overset{N_{\mathrm{TT}}-1}{.Math.}}{\hat{\sigma }}_l^2.Math.e^{-j2\mathrm{\pi \Delta }f{\tau }_l}& \mathrm{Equation}16\end{array}$

(74) Reference is briefly made to FIG. 4, which illustrates one suitable approach that may be used by pre-estimator 36 for estimating the path delays and path powers, which, together, form the channel profile. The process may include the following steps: 1. Create a vector y(n) of the reference signal (RS) from one OFDM symbol n, over time (shown in box 50) 2. Perform sqr(abs(IFFT)) (i.e. perform an IFFT (inverse fast fourier transform), take the absolute value of each component and then square them). This produces a multiplicity of spikes 52, one per path 1. The amplitude of each spike 52 is its power σ.sub.l.sup.2 and its distance from zero is its delay τ.sub.1. 3. Average the results for multiple OFDM symbols n to produce the power and delay values for equation 16.

(75) Equation 13 gives the time correlation r.sub.h.sup.t(Δt) and equation 16 provides the frequency correlation r.sub.h.sup.f(Δf). Filter calculator 40 may insert the time correlation and the frequency correlations into equation 10 to generate a time, frequency matrix R.sub.hh.

(76) Based on the matrix R.sub.hh, the estimated powers p.sub.i and the known sequences s.sub.i, filter calculator 40 may construct the R.sub.yy and R.sub.yh matrices, as follows:

(77) $\begin{array}{cc}\begin{array}{c}{R}_{\mathrm{yy}}=\underset{i=0}{\overset{\mathrm{NumCells}-1}{.Math.}}{p}_i.Math.S_i.Math.R_{\mathrm{hh}}.Math.S_i^{+}+{\sigma }_n^2.Math.I\\ R_{\mathrm{yh}}=p_0.Math.S_0.Math.R_{\mathrm{hh}}\end{array}& \mathrm{Equation}17\end{array}$

(78) As can be seen from the above description, autocorrelation matrix R.sub.yy and correlation matrix R.sub.yh may incorporate models of neighboring reference signals (i.e. equation 16 sums the models of reference signals from the current cell (i=0) and from the remaining cells (i=1, NumCells-1)).

(79) Filter calculator 40 may estimate the filter as W=R.sub.yy.sup.−1.Math.R.sub.yh and may provide filter matrix W to filter 42. Filter 42 may then utilize matrix W as described in equation 2 to generate the estimated channel h.sub.0 from the received symbols y. The equation is repeated hereinbelow:
{circumflex over (h)}.sub.0=W.sup.+.Math.y  Equation 18

(80) Thus, signal filter W and channel estimate ĥ.sub.0 also incorporate the models of the neighboring reference signals.

(81) FIG. 5, to which reference is now made, summarizes the operations of filter calculator 40, as follows: 1. Estimate the power transmitted by each cell i along with the noise variance; 2. Estimate the time and frequency correlation matrices; 3. Generate the R.sub.hh channel correlation matrix from the time and frequency correlation matrices; 4. Use the estimated powers p.sub.i and the known sequences s.sub.i to construct the R.sub.yy and R.sub.yh correlation matrices; and 5. Calculate the signal filter to be W=R.sub.yy.sup.−1.Math.R.sub.yh

(82) It is noted that the present invention may provide an improved channel estimation in the presence of interfering, possibly co-channel, reference signals and may be particularly useful in OFDMA based cellular telecommunication systems. Interference caused by the neighboring reference signals is modeled, rather than considered as white noise.

(83) It is noted that the equations provided herein are applicable to cells having a single transmitter, as described hereinabove, or to cells having more than one transmitter. For the latter, the transmitters participating in the calculation are numbered from 1 to NumCells, irrespective of how many transmitters are in each cell. The calculations and assumptions described herein do not change.

(84) It is further noted that equation 1 may be solved using other types of estimators. For example, an MMSE (Minimum Mean Square Error) estimator, which is not necessarily linear, may be considered.

(85) Embodiments of the present invention may include apparatus for performing the operations herein. This apparatus may be especially constructed for desired purposes, for example, a dedicated processor may be provided in a suitable user equipment device that communicates on a cellular telecommunications network, or other suitable network such as WiMax and the like. In other embodiments, the apparatus may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, which may be any type of media suitable for storing electronic instructions and capable of being coupled to a computer system bus.

(86) It is noted that none of the embodiments of the present invention are described with reference to any particular programming language. A variety of suitable programming languages may be used to implement the teachings this disclosure.

(87) While certain features of the disclosure have been illustrated and described herein, various modifications, substitutions, changes, and equivalents may occur to those of ordinary skill in the art without departing from the scope of the invention as claimed hereinbelow.