Abstract:
A base station is capable of performing a method for velocity estimation in a mobile communication system. In the velocity estimation method, a received signal is delayed by a plurality of different sample intervals. A candidate maximum Doppler frequency for each of the delayed received signals is estimated. A maximum Doppler frequency in a reliable period is selected among the candidate maximum Doppler frequencies.

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) AND CLAIM OF PRIORITY 
     The present application claims the benefit under 35 U.S.C. §119 to an application filed in the Korean Intellectual Property Office on Feb. 26, 2009 and assigned Serial No. 10-2009-0016164, the contents of which are herein incorporated by reference. 
     TECHNICAL FIELD OF THE INVENTION 
     The present invention relates generally to velocity estimation, and in particular, to an apparatus and method for efficiently estimating the velocity of a mobile station (MS) moving at low speed in a mobile communication system. 
     BACKGROUND OF THE INVENTION 
     Mobile communication systems need resource allocation based on accurate channel information in order to support large-capacity multimedia packet services with limited frequency/channel resources. As one of the important channel characteristics representing a channel state, users&#39; velocities are used for efficient management of radio resources. For example, if a mobile communication system is to determine whether to allocate a resource to an MS, it may use the velocity information of the MS to determine whether the MS will stay in its own cell for a long time. 
     Examples of the conventional MS velocity estimation method include a velocity estimation method based on a covariance function and a velocity estimation method based on an autocorrelation function of a received signal in the time domain. 
     The velocity estimation based on a covariance function and the velocity estimation method based on an autocorrelation function of a received signal measure the maximum Doppler frequency of a received signal to detect the velocity mapped to the maximum Doppler frequency. That is, the conventional velocity estimation methods estimate the velocity by predicting a change in the covariance function or the autocorrelation function depending on the Doppler spread. 
     If there is a sufficient measurement time, the above velocity estimation methods can estimate the accurate velocity of an MS regardless of high-speed, medium-speed or low-speed environments. However, time limitations are inevitable in order to measure the MS velocity that varies in real time. The velocity estimation error increases particularly in low-speed environments. Also, the noise influence increases particularly in low-speed environments, thus making it impossible to reliably identify a low-speed MS. 
       FIG. 1  illustrates a velocity estimation error depending on a Doppler frequency in the conventional art. 
     Referring to  FIG. 1 , the axis of abscissas represents a normalized Doppler frequency f d T s  and the axis of ordinates represents a means error value NMSE (Normalized Mean Square Error). Herein, f d  denotes the maximum Doppler frequency and T s  denotes a sampling period. 
     The graph of  FIG. 1  shows the mean error values estimated using every 1000 samples of a signal with a Signal-to-Noise Ratio (SNR) of 10 dB and a signal with an SNR of 20 dB in Rayleigh Fading environments. 
     It may be seen from the graph of  FIG. 1  that in high 20 dB SNR environments, the mean error is equal to or smaller than 10 −1  in a high-speed region of f d T s ≧0.05 and the mean error increases suddenly in a low-speed region of f d T s ≦0.05. In low 10 dB SNR environments, reliable estimation may be achieved only in a high-speed region of f d T s ≧0.1. 
     In a mobile communication system using a 2 GHz band, the velocity of an MS corresponding to f d T s =0.05 corresponds to 27 km/h in the case of applying a symbol rate of 1 k. Thus, in the case of applying the conventional method, the velocity of an MS in an f d T s ≦0.05 region may be inaccurately estimated even when an SNR is sufficiently high (e.g., about 20 dB). 
     In the conventional art, a large error in the estimation of the velocity of a low-speed MS may be caused by the following three reasons. The first reason is that the inverse of a Bessel function used to estimate the maximum Doppler frequency operates more sensitively in a low-speed region of f d T s ≦0.05. 
       FIG. 2  illustrates a Bessel function and a squared Bessel function according to the conventional art. 
     Referring to  FIG. 2 , a change in the estimated f d T s  according to a change in the measured mean error value is small in a high-speed f d T s ≧0.05 region ‘B’. Alternatively, a change in the estimated f d T s  according to a change in the measured mean error value increases greatly in a low-speed f d T s ≦0.05 region ‘A’. This phenomenon occurs prominently when an instantaneous change of the measured signal increases due to a reduced measurement time. 
     The second reason is that the influence of an over bias caused by an additive noise occurs more prominently in the low-speed region. That is, because the additive noise occurs uniformly by the sampling period and the SNR independently of the velocity of the MS, the influence of the additive noise increases in the low-speed region due to the sensitivity to a change of the measured value in the low-speed region. 
     The third region is that the measured value is distorted by the noise if the SNR value is unknown in the velocity estimation. 
       FIG. 3  illustrates estimation mean values for an SNR of 10 dB and an SNR of 20 dB according to the conventional art. 
     Referring to  FIG. 3 , the axis of abscissas represents a normalized Doppler frequency f d T s  and the axis of ordinates represents an estimated Doppler frequency. Herein, f d  denotes the maximum Doppler frequency and T s  denotes a sampling period. 
     It may be seen from the graph of  FIG. 3  that an over bias in a low-speed region occurs very seriously if there is an additive noise. 
     What is therefore required is an apparatus and method for errorlessly estimating the velocity of a low-speed MS in a mobile communication system. 
     SUMMARY OF THE INVENTION 
     To address the above-discussed deficiencies of the prior art, it is a primary aspect of the present invention to solve at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below. Accordingly, an aspect of the present invention is to provide an apparatus and method for velocity estimation in a mobile communication system. 
     Another aspect of the present invention is to provide an apparatus and method for accurately estimating the velocity of a low-speed MS in a mobile communication system. 
     According to an aspect of the present invention, a method for velocity estimation in a mobile communication system includes: delaying a received signal by a plurality of different sample intervals; estimating a candidate maximum Doppler frequency for each of the delayed received signals delayed by the different sample intervals; and selecting a maximum Doppler frequency in a reliable period among the candidate maximum Doppler frequencies. 
     According to another aspect of the present invention, an apparatus for velocity estimation in a mobile communication system includes: a plurality of delayers configured to delay a received signal by a plurality of different sample intervals; an estimator configured to estimate a candidate maximum Doppler frequency for each of the delayed received signals delayed by the different sample intervals; and a selector configured to select a maximum Doppler frequency in a reliable period among the candidate maximum Doppler frequencies. 
     Before undertaking the DETAILED DESCRIPTION OF THE INVENTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document: the terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation; the term “or,” is inclusive, meaning and/or; the phrases “associated with” and “associated therewith,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like; and the term “controller” means any device, system or part thereof that controls at least one operation, such a device may be implemented in hardware, firmware or software, or some combination of at least two of the same. It should be noted that the functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. Definitions for certain words and phrases are provided throughout this patent document, those of ordinary skill in the art should understand that in many, if not most instances, such definitions apply to prior, as well as future uses of such defined words and phrases. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects, features and advantages of certain exemplary embodiments of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which: 
         FIG. 1  illustrates a graph for a velocity estimation error depending on a Doppler frequency in the conventional art; 
         FIG. 2  illustrates a graph for a Bessel function and a squared Bessel function according to the conventional art; 
         FIG. 3  illustrates a graph for estimation mean values for an SNR of 10 dB and an SNR of 20 dB according to the conventional art; 
         FIG. 4  illustrates an apparatus for errorlessly estimating the velocity of an MS moving at low speed in a mobile communication system, according to an exemplary embodiment of the present invention; 
         FIG. 5  illustrates a process for errorlessly estimating the velocity of an MS moving at low speed in a mobile communication system, according to an exemplary embodiment of the present invention; 
         FIG. 6  illustrates a graph for velocity estimation experiment results based on autocorrelation according to the present invention; 
         FIG. 7  illustrates a graph for velocity estimation experiment results based on autocorrelation according to the present invention; 
         FIG. 8  illustrates a graph for velocity estimation experiment results based on autocorrelation according to the present invention; 
         FIG. 9  illustrates a graph for velocity estimation experiment results based on autocorrelation according to the present invention; 
         FIG. 10  illustrates a graph for velocity estimation experiment results based on covariance according to the present invention; 
         FIG. 11  illustrates a graph for velocity estimation experiment results based on covariance according to the present invention; 
         FIG. 12  illustrates a graph for velocity estimation experiment results based on covariance according to the present invention; and 
         FIG. 13  illustrates a graph for velocity estimation experiment results based on covariance according to the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIGS. 4 through 13 , discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged communication system. 
     Preferred embodiments of the present invention will be described herein below with reference to the accompanying drawings. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Also, the terms used herein are defined according to the functions of the present invention. Thus, the terms may vary depending on user&#39;s or operator&#39;s intentions or practices. Therefore, the terms used herein must be understood based on the descriptions made herein. 
     The present invention provides an apparatus and method for reducing an error in the estimation of the velocity of an MS moving at low speed in a mobile communication system. 
     The present invention provides a method for estimating the optimal MS velocity on the basis of autocorrelation or covariance by applying a plurality of different sample intervals. 
       FIG. 4  is a block diagram of an apparatus for errorlessly estimating the velocity of an MS moving at low speed in a mobile communication system, according to an exemplary embodiment of the present invention. 
     Referring to  FIG. 4 , the velocity estimation apparatus includes first to l th  delayers  400 _ 1  to  400   —   l , first to l th  autocorrelation/covariance units  410 _ 1  to  410   —   l , first to l th  maximum Doppler frequency estimators  420 _ 1  to  420   —   l , and a maximum Doppler frequency selector  430 . 
     Each of the first to i th  delayers  400 _ 1  to  400   —   l  delays a received signal according to each delay sample and outputs the resulting signal to each of the first to l th  autocorrelation/covariance units  410 _ 1  to  410   —   l.    
     For example, the first delayer  400 _ 1  delays the received signal by a 1-sample  interval and outputs the resulting signal to the first autocorrelation/covariance unit  410 _ 1  and the second delayer  400 _ 2 . The second delayer  400 _ 2  delays the output signal of the first delayer  400 _ 1  by a 1-sample interval and outputs the resulting signal to the third autocorrelation/covariance unit  410 _ 3  and the third delayer  400 _ 3 . Consequently, the second delayer  400 _ 2  delays the received signal by a 2-sample interval and outputs the resulting signal to the third autocorrelation/covariance unit  410 _ 3  and the third delayer  400 _ 3 . According to embodiments, the first to l th  delayers  400 _ 1  to  400   —   l  may delay the received signal by the respective delay values (1 sample, 2 samples, . . . , l samples). 
     Each of the first to l th  autocorrelation/covariance units  410 _ 1  to  410   —   l  calculates the maximum Doppler frequency on the basis of the covariance and the autocorrelation between the received signal C(i) and the delayed received signal C(i−1). 
     Herein, when measuring the covariance or the autocorrelation for the received signal, if delaying the measured signal by an l-sample interval (D=l), the maximum Doppler frequency may be expressed as Equations 1 and 2: 
     
       
         
           
             
               
                 
                   
                     
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     where {circumflex over (f)} d   ACF(D=l)  denotes the maximum Doppler frequency based on autocorrelation, l denotes the number of samples, T s  denotes a sampling period, γ denotes an SNR, B m  denotes a noise bandwidth, φ l  denotes the autocorrelation function value between the received signals spaced apart by the l th  sampling, J(•) denotes a Bessel function, and sinc(•) denotes a sinc function. 
     
       
         
           
             
               
                 
                   
                     
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     where {circumflex over (f)} d   COIV(D=l)  denotes the maximum Doppler frequency based on covariance, l denotes the number of samples, T s  denotes a sampling period, γ denotes an SNR, B m  denotes a noise bandwidth, a(i) denotes the envelope of a received signal, Var(•) denotes a variance function, J(•) denotes a Bessel function, and sinc(•) denotes a sinc function. 
     The influences of the noises on the maximum Doppler frequencies in Equations 1 and 2 are identical because the noise bandwidth increases with an increase in the sample interval. 
     As expressed in Equations 1 and 2, the estimation of the maximum Doppler frequency based on the sample with increased delay has the following characteristics. 
     Firstly, when estimating the maximum Doppler frequency corresponding to a low speed, in applying the inverse of a Bessel function, the characteristics of the region ‘B’ (see  FIG. 2 ) less sensitive to a change of the measured value are applied to the region ‘A’ (see  FIG. 2 ) sensitive to a change of the measured value. Thus, the sensitivity of a velocity estimation value for a low-speed MS decreases to the sensitivity of the estimation value for the medium speed or more. 
     Secondly, when estimating the maximum Doppler frequency corresponding to a low speed, the difference of a change in covariance or autocorrelation according to the secondary characteristics of a fading signal from the characteristics of covariance or autocorrelation according to the secondary characteristics of a noise decreases. By approximation of Equations 1 and 2, the conditions for equalization of the covariances or autocorrelations of a Rayleigh fading signal and a noise are derived as Equation 3: 
     
       
         
           
             
               
                 
                   
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     where f d  denotes the maximum Doppler frequency and B m  denotes a noise bandwidth. 
     Thus, the noise bandwidth for a signal delayed by a sample interval decreases to B m =B m /l so that the over bias caused by the noise in the low-speed region decreases. 
     Thirdly, if a delayed sample is used, the observable maximum Doppler frequency decreases. This is because an inverse Bessel function used in estimation or a unique region of a squared inverse Bessel function decreases. Thus, the reliable range of measurement based on a delayed sample decreases with an increase in delay. 
     The maximum Doppler frequency selector  430  selects a candidate maximum Doppler frequency in the most reliable period, among the maximum Doppler frequency values received from the first to l th  autocorrelation/covariance units  410 _ 1  to  410   —l , to estimate the corresponding velocity of an MS. That is, the maximum Doppler frequency selector  430  selects the maximum Doppler frequency by using the application result of a small-delay sample in a high-speed region and the application result of a large-delay sample in a low-speed region. 
       FIG. 5  illustrates a process for errorlessly estimating the velocity of an MS moving at low speed in a mobile communication system, according to an exemplary embodiment of the present invention. 
     Referring to  FIG. 5 , the velocity estimation apparatus sets i=1 in step  500 . Herein, i is a parameter for selecting a plurality of candidate maximum Doppler frequencies. 
     In step  502 , the velocity estimation apparatus determines whether i is smaller than or equal to M. Herein, M denotes the total number of candidate maximum Doppler frequencies outputted from the maximum Doppler frequency selector  430 . 
     If i is smaller than or equal to M (in step  502 ), the velocity estimation apparatus proceeds to step  504 . In step  540 , the velocity estimation apparatus selects a delay sample  4  corresponding to i and a threshold value Th i  corresponding to i. 
     In step  506 , the velocity estimation apparatus determines whether the maximum Doppler frequency {circumflex over (f)} d   D=li  based on covariance or autocorrelation based on the delay sample l i  is equal to or greater than the threshold value Thi. If the maximum Doppler frequency {circumflex over (f)} d   D=li  is equal to or greater than the threshold value Th i  (in step  506 ), the velocity estimation apparatus proceeds to step  508 . In step  508 , the velocity estimation apparatus sets the maximum Doppler frequency satisfying the above conditions to {circumflex over (f)} d   D=l     i   . 
     If the maximum Doppler frequency {circumflex over (f)} d   D=li  smaller than the threshold value Th i  (in step  506 ), the velocity estimation apparatus increases i in step  512  and returns to step  502 . 
     If i is greater than M (in step  502 ), the velocity estimation apparatus proceeds to step  510 . In step  510 , the velocity estimation apparatus sets the maximum Doppler frequency to {circumflex over (f)} d   D=l(M) . 
     Thereafter, the velocity estimation apparatus ends the process of the present invention. 
     As described above, in  FIG. 5 , each of the M candidate maximum Doppler frequencies is a value obtained through sample delays (l 1 , l 2 , . . . , l m ) and they are arranged in the relationship of l i &lt;l i+1 . 
     Also, in  FIG. 5 , each of the (M−1) threshold values (Th 1 , Th 2 , . . . , Th M−1 ) is the minimum value of the reliable period of the maximum Doppler frequency measured with the corresponding delay and they have a relationship of Th i &gt;Th i+1 . Thus, in  FIG. 5 , the candidate maximum Doppler frequency selection method selects the candidate maximum Doppler frequency in the reliable period for each delay. The threshold values and the delay values for the M candidate maximum Doppler frequencies in the present invention may be determined according to the system requirement and efficiency. 
       FIGS. 6 to 9  are graphs illustrating velocity estimation experiment results based on autocorrelation according to the present invention. 
     The graphs of  FIGS. 6 and 7  show the experimental results in the case of using 4 candidate maximum Doppler frequencies (M=4) in 20 dB SNR environments. Herein, the 4 candidate maximum Doppler frequencies are delayed respectively by 1, 2, 4 and 8 samples (l 1 =1, l 2 =2, l 3 =4, l 4 =8) and the threshold values for the candidate maximum Doppler frequencies are respectively Th 1 =0.2, Th 2 =0.085, Th 3 =0.035. 
     In  FIGS. 6 and 7 , ‘ACF’, ‘ACF 2 ’, ‘ACF 4 ’ and ‘ACF 8 ’ are respectively the experimental results for sample delays of 1, 2, 4 and 8, and ‘Proposed’ is the experimental result according to the present invention. 
     As a result of the comparison of normalized MSEs, it may be seen from  FIG. 6  that the method of the present invention may greatly reduce the estimation error for the low speed in comparison with the conventional method ‘ACF’. 
     As a result of the comparison of estimation mean values, it may be seen from  FIG. 7  that the method of the present invention may greatly reduce the over bias in comparison with the conventional method ‘ACF’. 
     The graphs of  FIGS. 8 and 9  show the experimental results in 10 dB SNR environments. Except the threshold values, the experimental environments of  FIGS. 8 and 9  are identical to the experimental environments of  FIGS. 6 and 7 . The threshold values used in the 10 dB SNR environments are respectively Th 1 =0.27, Th 2 =0.13, Th 3 =0.061. 
     When comparing the results of  FIGS. 6 and 7  with the results of  FIGS. 8 and 9 , it may be seen that the performance improvement by the present invention increases with an increase in noise. 
       FIGS. 10 to 13  are graphs illustrating velocity estimation experiment results based on covariance according to the present invention. 
     The graphs of  FIGS. 10 and 11  show the experimental results in the case of using 4 candidate maximum Doppler frequencies (M=4) in 20 dB SNR environments. Herein, the delay samples for candidate maximum Doppler frequencies are identical to those of  FIGS. 6 to 8  (l 1 =1, l 2 =2, l 3 =4, l 4 =8). The threshold values for the candidate maximum Doppler frequencies are respectively Th 1 =0.118, Th 2 =0.057, Th 3 =0.025. In  FIGS. 10 and 11 , ‘COV’, ‘COV 2 ’, ‘COV 4 ’ and ‘COV 8 ’ are respectively the experimental results for sample delays of 1, 2, 4 and 8, and Proposed&#39; is the experimental result according to the present invention. 
     As a result of the comparison of normalized MSEs and mean values, it may be seen from  FIGS. 10 and 11  that the method of the present invention may greatly reduce the over bias and the estimation error for the low speed in comparison with the conventional method ‘COV’. 
     The graphs of  FIGS. 12 and 13  show the experimental results in 10 dB SNR environments. Except the threshold values, the experimental environments of  FIGS. 12 and 13  are identical to the experimental environments of  FIGS. 10 and 11 . The threshold values used in the 10 dB SNR environments are respectively Th 1 =0.18, Th 2 =0.085, Th 3 =0.04. When comparing the results of  FIGS. 10 and 11  with the results of  FIGS. 12 and 13 , it may be seen that the performance improvement by the present invention increases with an increase in noise. 
     As described above, the present invention applies a low sampling delay for a high speed and a high sampling delay for a low speed in a mobile communication system, thereby providing good performance for the additive noise and the estimation of the velocity of a low-speed MS. 
     While the invention has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the scope of the invention is defined not by the detailed description of the invention but by the appended claims, and all differences within the scope will be construed as being included in the present invention.