Abstract:
There is disclosed a technique for generating an improved estimate of the frequency error in a received signal, and more particularly the application of such a technique in the equalisation circuitry of a wireless network element.

Description:
FIELD OF THE INVENTION  
         [0001]    The present invention relates to correction of the frequency shift in a received signal of a communication system, and particularly but not exclusively in a wireless communication system.  
         BACKGROUND TO THE INVENTION  
         [0002]    In known wireless communication systems, an equaliser is used in the receiver in order to equalise the received signal prior to further processing in the receiver. However in a mobile communication system, a mobile may be moving quickly or have a carrier offset, such that the signal received in the receiver has a frequency shift. This frequency shift needs to be removed in the receiver for the received signal to be properly processed. More particularly, the frequency shift should be removed prior to equalisation of the received signal.  
           [0003]    Existing wireless communication systems utilise techniques for removing this frequency shift. In one example, a Kalman filter based recursive estimator is used to remove the frequency shift and equalise the received signal.  
           [0004]    The problem is to be able to receive a signal from a fast moving mobile or from a mobile where there is a carrier offset. The received signal has a frequency shift, which should be corrected.  
           [0005]    The frequency error is usually estimated from the samples of the received samples. This estimate is typically worse when the channel quality is poor and gets better as the channel quality improves. When there is no frequency shift and the channel conditions are near the sensitivity level of a receiver, a frequency correction algorithm should not degrade the performance of the receiver. The problem of frequency error correction has become a potential problem in the case of EDGE wireless systems in particular, and as such improvements are desired.  
           [0006]    A Maximum-Likelihood method for frequency estimation is defined in M. Luise and R. Reggiannini,”  Carrier Frequency Recovery  in All-Digital Modems for  Burst - Mode Transmissions , IEEE Trans. On Comm., February/March/April 1995. Also Australian Patent No. AU 664626 describes a system where the Doppler correction is based to change in TOA (Time of arrival).  
           [0007]    It is an aim of the present invention to provide an improved technique for estimating and removing the frequency shift in a received signal in a communication system such as a wireless communication system.  
         SUMMARY OF THE INVENTION  
         [0008]    According to the present invention there is provided a method of estimating the frequency error in a received signal, comprising: a) Receiving the signal at time t; b) Removing from said signal an estimate of the frequency error in said signal, thereby generating a frequency corrected received signal at time t; c) Equalising said received signal, wherein the equalising step introduces a delay of n samples, such that an equalised output is generated at time t−n; d) Generating an estimate of a first component of the frequency error in the received signal at time t−n based on the equalised output; e) Recalculating the first and second components in dependence on the frequency corrected received signal at time t; f) In dependence on the recalculated first and second values, estimating values of the first and second components of the frequency error for times t+n and t+1 respectively; and g) Wherein the first component of the estimate frequency error for time t+n is used to generate a frequency corrected signal for a signal received at time t+n.  
           [0009]    The step of generating an estimate of the first component and recalculating the first component (steps d and e) may use the acquired decision vector:  
           β={circumflex over (φ)}( t|t− 1)−α( t|t−   n )  
           {circumflex over (φ)}( t|t )={circumflex over (φ)}( t|t− 1)+(δ w   2   P   −1 ( t )+x H ( t ) h*h   T   x ( t )) −1   x   H ( t ) h *( y   r ( t )−(1 +jβt ) h   T   x ( t ))  
           [0010]    The step of estimating a value of the first component (step h) may use the predictions:  
         α        (       t   +   n        t     )       =       ϕ   ⋒          (     t      t     )                   ϕ   ⋒          (       t   +   1        t     )       =       ϕ   ⋒          (     t      t     )                 P        (     t   +   1     )       =         (         P     -   1            (   t   )       +       1     δ   w   2              x   H          (   t   )            h   *          h   T          x        (   t   )           )       -   1       +     E        (            u        (   t   )            2     )                               
 
           [0011]    The step of removing from said signal an estimate of the frequency error in the received signal at time t+n may use the prediction:  
             y   r ( t+n )= y ( t+n ) e   −jα(t+n|t)(t+n)    
           [0012]    The method may further comprise generating an estimate of a second component of the error in the received signal based on an equalised output at time t−1, wherein the step of recalculating further includes recalculating the second component, and the step of the second component is estimated for time t+1 in dependence on said recalculated value.  
           [0013]    The step of generating an estimate of the first component and recalculating the first component (steps d and e) may use the acquired decision vector:  
           {circumflex over (φ)}( t|i )={circumflex over (φ)}( t|t− 1)+(δ w   2   P   −1 ( t )+ x   H ( t ) h*h   T   x ( t )) −1   x   H ( t ) h* ( y   r ( t )− h   T   x ( t ))  
           [0014]    The step of generating an estimate of the first component and recalculating the first component (steps d and e) may use the acquired decision vector:  
           P     -   1            (     t   +   1     )       =         P     -   1            (   t   )       +       1     δ   w   2              x   H          (   t   )            h   *          h   T          x        (   t   )                       ϕ   ⋒          (     t      t     )       =         ϕ   ⋒          (     t        t   -   1       )       +       P        (     t   +   1     )            δ   w   2            x   H          (   t   )              h   *          (         y   r          (   t   )       -       (     1   +     j                 β                 t       )          h   T          x        (   t   )           )                     when                   u        (   t   )         =   0.                         
 
           [0015]    The step of removing an estimate of the frequency error in a received signal may comprise rotating the received signal with a prediction of said error.  
           [0016]    According to a further aspect the present invention further provides equalisation circuitry comprising: rotation means for rotating a received signal at time t with a prediction of the frequency error in the received signal, thereby generating a frequency corrected received signal at time t; an equaliser having a delay of n samples and for equalising the frequency corrected received signal at time t, and for generating an equalised received signal at time t+n; a frequency error calculator for receiving the equalised received signal at time t+n and the corrected received signal at time t, and for generating a prediction of the frequency error in a received signal received at time t+n, wherein the frequency error calculator generates an estimate of s first component of the frequency error in the received signal at time t−n based on the equaliser output, recalculates this first component in dependence on the frequency corrected received signal at time t, and in dependence on such recalculated value estimates a value of the first component for frequency error at time t+n, wherein this estimate is used by the rotation means to generate a frequency corrected signal for a signal received at time t+n.  
           [0017]    The frequency error calculator further generates an estimate of a second component of the frequency error in the received signal based on an output of he equaliser at time t−1, and wherein said second component is recalculated and a in dependence thereon a value of the second component of the frequency error for time t+1 is estimated.  
           [0018]    An element of a mobile communication system may include such equalisation circuitry. In particular, a base transceiver station may include such circuitry, or implement the defined method. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0019]    [0019]FIG. 1 illustrates a preferred embodiment of the present invention. 
     
    
     DESCRIPTION OF PREFERRED EMBODIMENTS  
       [0020]    Referring to FIG. 1, an example implementation of the present invention will be described with reference to a particular, non-limiting example. The invention is described here-n with reference to an example of a receiver in a wireless communication system, and more particularly ar EDGE system. However the invention is more generally applicable, and may be utilised in any communication system in which there is a need to remove a frequency shift in an equaliser.  
         [0021]    [0021]FIG. 1 illustrates a block diagram of a frequency equaliser in accordance with a preferred embodiment of the present invention, for an EDGE application. The block diagram includes a pre-filter  10 , a mixer  16 , an equaliser  20 , and a frequency error estimator  18 .  
         [0022]    A received sample to be equalised at time t is provided on line  12  to the input of the pre-filter  10 . The received, filtered sample is then provided on line  14  at the output of the pre-filter  10 , as represented by sample y(t).  
         [0023]    The received, filtered sample at time t, y(t), on line  14  is provided as a first input to the mixer  16 . The second input to the mixer is provided, as discussed further hereinafter, by the output of the frequency error estimator  18  on line  24 . The generation of the signal on line  24  by the frequency error estimator  18  is discussed further hereinbelow. As is discussed further hereinbelow, for a received sample at time t on line  14 , the sample on line  24  represents the frequency error in the received sample at time (t−n), where n represents a processing delay in the equaliser. This sample is labelled y error (t−n) in FIG. 1.  
         [0024]    The output of the mixer on line  22  forms the sole input to the equaliser  20  and a first input to the frequency error estimator  18 . As will be further discussed hereinbelow, the output of the mixer on line  22 , y r (t), represents an estimate of the received signal with any frequency error removed. However as will be apparent from the preceding paragraph, this estimate is a best estimate of the frequency error in the signal at time t based on an error estimation which was performed at time (t−n).  
         [0025]    The equaliser  20 , in accordance with conventional techniques, generates two output signals. A first output signal on line  28  represents the actual decisions made by the equaliser, and forms the output of the equaliser block. This output typically contains soft information of the received data, which soft information contains the decision about the value of the signal and in addition some quality information about the certainty of the decisions (Max-Log-MAP). This operation of the equaliser will be familiar to one skilled in the art.  
         [0026]    In some cases, such as in a Viterbi decoder, the output on line  28  is only made available when a whole received block has been processed. In such a case a second output of the equaliser, as represented by line  26 , is provided. This second output on line  25  provides ‘tentative’ decisions for the frequency error estimator block  18 .  
         [0027]    Thus the second output signal of the equaliser, on line  26 , forms a second input to the frequency error estimator block  18 . It should be noted, however, that the example of FIG. 1 assumed an implementation such as a Viterbi decoder. If however the equaliser did not have a second output for feeding back values, then the input to the frequency error estimator  18  may be taken directly from the equaliser output  28 .  
         [0028]    The frequency error estimator generates an output on line  24  in accordance with the present invention, as further described hereinafter, in dependence on the inputs on lines  22  and  26 .  
         [0029]    The received signal on line  12 , and consequently the filtered, received signal on line  14 , contains a frequency shift. This received frequency shift needs to be corrected for the correct processing of the received signal.  
         [0030]    As mentioned hereinabove, the second input of the mixer on line  24 , y error (t−n), represents the best estimate of the frequency error in the received sample y(t), ard as such is the frequency error which is to be removed from the received, filtered sample at the first input of the mixer  16 .  
         [0031]    The output of the mixer  16  on line  22  represents an adjusted version of the received sample. More particularly the signal on line  22  represents the received, filtered signal on line  14 , with the estimated frequency shift removed there from.  
         [0032]    This corrected sample y r (t) on line  22  is provided to the equaliser  20 . The equaliser  20  equalises the samples on line  22  In a known manner, to provide an equaliser output on line  28  for further processing elsewhere in the receiver if which the equaliser block of FIG. 1 forms a part.  
         [0033]    The second output of the equaliser  25  are decisions drawn as separate decisions from the actual decisions on the first output  28  of the equaliser.  
         [0034]    Depending on the actual decisions at the first output  28 , the decisions on the output  26  may actually be the same, for example if the actual decisions on line  28  are Max-Log MAP decisions. Thus the decision output gives the values of the actually transmitted data, which is the function of the equaliser block.  
         [0035]    The frequency error estimator then processes the signals received on the respective lines  22  and  26  to generate the estimate of the frequency offset in the received signal for input to the mixer on line  24 .  
         [0036]    It should be noted that the equaliser block of FIG. 1 would function to equalise the received signals without any frequency error estimator. However, the quality of the decisions generated by such an equaliser block would be inferior.  
         [0037]    In accordance with the present invention, the frequency error estimator  18  is adapted in order to provide improved estimates on line  24  for use in removing the frequency error from the received signal.  
         [0038]    At time t the frequency error estimator receives the corrected received signal at time t, y r (t), and the decisions about the received signal at time (t−n) from the equaliser. The frequency error estimator then uses those values to provide an estimate of the frequency error in the received signal at time (t+n). Thus the error calculated by the frequency error at time t, is used for processing the received signal received at time (t+n). Thus, in FIG. 1, there is shown that the error signal applied to the mixer  16  at the time t corresponds to an error calculated at time (t−n).  
         [0039]    It should be noted that the equaliser block has a processing delay such that it provides the estimates relating to the samples received at a time t after a delay n. n represents the number of samples in the processing delay. Thus the frequency error estimates performed by the frequency error estimator are estimates for the future. Therefore the actual output of the equaliser  20  at the time at which the received sample y(t) is received is x(t−n).  
         [0040]    Thus at a time t, relying purely on the completed processing of the equaliser  20 , only the frequency error estimate at time t−n is available.  
         [0041]    However, the frequency error estimator operates better if as many samples as are possibly available are used in the error estimating step. For this reason, in accordance with the present invention, the frequency error estimator additionally keeps track of all for all samples up to time t−1. As such, and referring to FIG. 1, it can be seen that the frequency error estimator receives the actual output of the equaliser  20  x(t−n), as well as the most recent ‘intermediate’ result, x(t−1).  
         [0042]    The frequency error estimator operates on these two values to produce two corresponding estimates of the frequency error in the signal received at time t.  
         [0043]    The two frequency error estimates are generated using equations as defined in the mathematical analysis given hereinbelow.  
         [0044]    Thus, the frequency error estimate generated by the frequency error estimator has two components. The first component, α, is the most recent error estimate based on the results available at time t−n, and the second component, β, is based on all available sample results at time t−1. It should be clear that the second component includes part processed results, as it includes in its estimates all samples from t−n to t−1.  
         [0045]    As discussed further hereinbelow, the second component β is used to keep track of the best possible frequency error estimate, and the first component α is the information that is actually output by the frequency error estimator to the mixer to correct the received signal.  
         [0046]    Thus, in a first ‘prediction’ step, the frequency error estimator generates two predictions of the frequency error in the received signal: i.e. the first α and second β components. As a result, at a given time t two estimates of the frequency error are available.  
         [0047]    The frequency error estimator also receives the adjusted (or frequency corrected) sample at time t, y r (t). After receiving this sample, the frequency error estimator can recalculate the best possible frequency error estimate at time t. That is, the first α and second β components of the frequency error information are corrected.  
         [0048]    Thus, in a ‘correction’ step, corrected first α and second  62  error components are generated.  
         [0049]    After this correction step, the best error estimate for the time t is available. However, the samples related to that time have already been received and processed by the equaliser  20 , and used to generate the corrected components α,β as discussed above. As such, these corrected component values cannot be used for removing the frequency error at time t.  
         [0050]    The frequency error estimator therefore makes a further estimate of the frequency error estimate for each component. For the first component α, the estimate is made for a time t+n, and for the second component β the estimate is made for a time t+1. That is, estimates are made for the time at which the next results are available.  
         [0051]    The first component α(t+n) provides the output of the frequency error estimator, and as such provides the frequency error estimate to be removed from the received signal y(t+n). The second component β(t+1) is used for the optimality of the Kalman process within the Kalman filter, and is used only within the frequency error estimator, and not output therefrom. The use of this second component β ensures that the frequency error estimates always use the latest data estimates from the equaliser.  
         [0052]    The invention may be further understood with reference to the following mathematical analysis of the operation of the equaliser block shown in FIG. 1.  
         [0053]    The problem of frequency estimation can be written as the following well-known equation:  
           y ( t )= h   T   x ( t ) e   jφ   +v ( t )  
         [0054]    where h T x(t) is a convolution between data and channel impulse response, y is a received sample and v is a noise component. In the frequency estimation the angle φ is to be estimated.  
         [0055]    This problem is non-linear and therefore an optimal estimator is difficult to obtain.  
         [0056]    The above equation can be partitioned into linear and non-linear parts by setting:  
         φ=α+β, then  
           y ( t )= h   T   x ( t ) e   jφt   +v ( t )= h   T   x ( t ) e   j(α+β)t   +v ( t )= h   T   x ( t ) e   jαt (1+ jβt )+ v ( t )  /1/  
         [0057]    A linearisation step has been used assuming e jβt ≈(1+jβt). Assuming white noise both sides can be multpilied by e −jαt  and now a “partly linear” model can be written as  
           y ( t ) e   −jαt   −h   T   x ( t )= jβth   T   x ( t )+ w ( t )   
         [0058]    where φ=α+β,  
         [0059]    where the frequency error φ is to be estimated. As such the components α and β are to be estimated. This mathematical model defines the basis for the frequency error estimator according to the invention which is discussed above.  
         [0060]    It should be noted that an inverse model of the above equation may be written as:  
           h   T   x ( t )= y ( t ) e   jφt   ÷v ( t ),  
         [0061]    and could be used to derive the state model instead of the model derived herein below.  
         [0062]    The derivation of the state model, starting from equation (2), is now described below.  
         [0063]    This estimator is designed to operate in the case of an equalizes, which as discussed above means that there is a decision delay (n) related to the acquired decisions. In accordance with the preferred embodiment of the present invention, the frequency error prediction is carried out for an input sample which goes to an equalizer at time (t+n), and the correction step is performed using all available information at time (t−1).  
         [0064]    Therefore, as discussed above, there are two predictions: φ(t+n|t) and φ(t+1|t).  
         [0065]    Also for the correction step, the sample related to the decision at time t is based on the frequency error estimate at time (t−n).  
         [0066]    A state model of the frequency error can therefore be stated as:  
             {                   y        (   t   )                   -   j                     α        (     t        t   -   n       )          t         -       h   T          x        (   t   )           =       j                   β        (     t        t   -   1       )            th   T          x        (   t   )         +     w        (   t   )                         ϕ   ⋒          (       t   +   1        t     )       =         ϕ   ⋒          (     t      t     )       +     u        (   t   )                         ϕ   ⋒          (     t        t   -   1       )       =       α        (     t        t   -   n       )       +     β        (     t        t   -   1       )                 ,               /   3     /                               
 
         [0067]    where it can be noted that the last equation is additional to a normal Kalman equation. It is used to define the bias between the non-linear and linear parts of equation 1. Therefore its derivation is close to an Extended Kalman Filter approach.  
         [0068]    The term u(t) is a frequency noise term, which can be used to enhance the tracking capabilities of the frequency estimator.  
         [0069]    The frequency error estimator itself is now discussed.  
         [0070]    The frequency error estimator procedure is based on the predictor-corrector approach known from the Kalman filter. As the frequency error component is divided into linear and non-linear parts, an additional step is carried out to study the relation between those parts.  
         [0071]    First the relation between φ(t|t−1)=α(t|t−n)+β(t|t−1) is studied. It can be noted that the linear approximation e jβt ≈(1+jβis only valid when β is small and its quality gets worse as β gets larger. Therefore by taking the expected values of equation E(φ(t|t−1))=E(α(t|t−n)+β(t|t−1)), it can be noted that E(β(t|t−1)) is minimised when φ(t|t−n)=α(t|t−n) (at time t−n.)  
         [0072]    Using the information above a frequency error estimator (and corrector) can be derived. Note that two predictions of the frequency error are required. One may be used for rotating the samples to be used in the equalizer, and the other one may be used in the correction phase.  
         [0073]    The necessary steps to be performed can therefore be defined as follows:  
         [0074]    1. Find the frequency difference between different predictions and correct the frequency error estimate according the acquired decision vector.  
         β={circumflex over (φ)}(xt|t−1)−α( t|t−n )  
         {circumflex over (φ)}( t|t )={circumflex over (φ)}( t|t− 1)+(δ w   2   P   −1 ( t )+ x   H ( t ) h*h   T   x ( t )) −1   x   H ( t ) h* ( y   r ( t )−(1+ jβt ) h   T   x ( t ))  
         [0075]    2. Create new predictions:  
         α        (       t   +   n        t     )       =       ϕ   ⋒          (     t      t     )                   ϕ   ⋒          (       t   +   1        t     )       =       ϕ   ⋒          (     t      t     )                 P        (     t   +   1     )       =         (         P     -   1            (   t   )       +       1     δ   w   2              x   H          (   t   )            h   *          h   T          x        (   t   )           )       -   1       +     E        (            u        (   t   )            2     )                               
 
         [0076]    3. Rotate the received samples with the prediction:  
           y   r ( t+n )= y ( t+n ) e   −jα(t+n|t)(t+n)    
         [0077]    The y r  samples are then used in the equalizer.  
         [0078]    In the initial phase, estimates for P(t initial ) and φ(t initial ) are needed and the samples y r  are assumed to be rotated accordingly.  
         [0079]    The simplification of the above equations is now discussed.  
         [0080]    1. It can be noted that in case the u(t)=0, the variance of the frequency estimate for a time (t+n) can be used in the correction step and the calculation can be combined  
           P     -   1            (     t   +   1     )       =         P     -   1            (   t   )       +       1     δ   w   2              x   H          (   t   )            h   *          h   T          x        (   t   )                       ϕ   ⋒          (     t      t     )       =         ϕ   ⋒          (     t        t   -   1       )       +       P        (     t   +   1     )            δ   w   2            x   H          (   t   )              h   *          (         y   r          (   t   )       -       (     1   +     j                 β                 t       )          h   T          x        (   t   )           )                                 
 
         [0081]    2. If n is small, the β˜0 and the correction can be simplified  
         {circumflex over (φ)}( t|t )={circumflex over (φ)}( t|t− 1)+(δ w   2   P   −1 ( t )+ x   H ( t ) h*h   T   x ( t )) −1   x   H ( t ) h* ( y   r ( t )− h   T   x ( t ))