JPH0832550A - Propagation path estimation device - Google Patents

Abstract

PURPOSE:To estimate a propagation path characteristic in an excellent way even when a propagation environment is fluctuated. CONSTITUTION:A Doppler frequency detection part 30 detects a Doppler frequency sequentially at a prescribed period based on a correlation signal V1(i) from which a data modulation component is eliminated. An SNR arithmetic section 40 calculates a signal versus interference wave power ratio sequentially for a prescribed period based on the correlation signal V1(i) and a reception signal V2. An approximation data number decision section 50 decides the number of approximated data to minimize the estimate error by the least square method based on the output. An estimate value calculation section 60 uses the correlation signal V1(i) by the number of decided approximated data to calculate a parameter of the approximate function by the least square method and calculates an estimate value V3(i) of the propagation path characteristic from the correlation signal V1(i) based on the approximated function having the parameter.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention is provided in a communication system of direct spread spectrum / spread spectrum communication system and uses a least square method to set parameters so as to minimize an error from a correlation signal from which a data modulation component is removed. The present invention relates to a channel estimation device that estimates channel characteristics based on the calculated approximation function.

[0002]

2. Description of the Related Art In recent years, in a mobile communication system for connecting a plurality of mobile devices to each other, a code spread multiple access system (hereinafter, referred to as "multiple spread access system" as a multiple access system due to a shortage of frequencies due to the spread of car phones, mobile phones and the like. CDMA ") is attracting attention.

This CDMA is a multiple access system using a direct spread / spread spectrum communication system (hereinafter referred to as "DS / SS system") as a communication system. here,
The DS / SS method is a communication method in which transmission data is directly spread with a predetermined spreading code sequence, digitally modulated, and then transmitted.

In a mobile communication system adopting CDMA as a multiple access system, it is considered to use a RAKE system receiver as a receiver capable of coping with frequency selective fading. This RAKE receiver separates a received signal that has undergone frequency selective fading into a preceding wave and a delayed wave by despreading, weights each separated output according to its reliability, and weights each weighted output. By combining them, path diversity is realized.

When this RAKE receiver is used as a receiver capable of coping with frequency selective fading,
If the weighting coefficient can be accurately determined, the path diversity equivalent to the maximum ratio combining diversity can be obtained.

As the weighting coefficient for synthesis, a complex conjugate of the characteristic of the propagation path through which the received signal is propagated can be used. When this complex conjugate is used as the weighting coefficient, it is necessary to estimate the propagation path characteristic. As a method for estimating the propagation path characteristic, a method described in the following document has been conventionally known.

Reference: Yukitoshi SANADA, Akihiro KAHIWA
RA and Masao NAKAGAWA "Adaptive RAKE Receiver for Mobile Communications" IEICE TRANS. COMMUN., VOL.E76-B, NO.8 AUGUST 1993 The method described in this document is "fading speed hardly changes", "noise is It is assumed that the random signal has an average value of 0. ”Under this assumption, an approximation function that approximates the correlation signal from which the data modulation component is removed is determined in advance, and the propagation path characteristic is determined based on this approximation function. It is an estimate.

That is, this method is based on the above assumption,
First, the order of the approximation function is determined, then the parameters of this approximation function are determined by the method of least squares, and the propagation path characteristics are estimated based on the approximation function in which the order and parameters are determined. is there.

According to such a configuration, it is possible to determine an approximate function that minimizes the error with the correlation signal from which the data modulation component has been removed. Therefore, in an environment where the fading speed is high and the signal-to-noise ratio is high. Even in a low environment, the propagation path characteristics can be estimated well.

[0010]

However, the above-described conventional channel characteristic estimation method has the following problems. That is, in an actual propagation environment, the fading speed and the noise power fluctuate as the moving speed of the receiver of the mobile communication system changes. Therefore, as in the past, assuming a specific propagation environment in advance, and under this assumption,
In a configuration in which a predetermined approximation function is set in advance and the propagation path characteristics are estimated based on this approximation function, if the propagation environment is the same as the environment assumed in advance, the propagation path characteristics should be estimated well. However, if they are different, there is a problem that the expected estimation accuracy cannot be obtained.

[0011]

In order to solve the above-mentioned problems, the present invention relates to a channel estimation apparatus for estimating channel characteristics based on an approximate function whose parameters are determined by the method of least squares. Means for detecting the fading speed and noise power of the correlation signal from which the modulation component has been removed, and means for determining the approximate number of data that can minimize the estimation error based on the detected output are provided. .

Here, the estimation error means an approximation error by the method of least squares, that is, a sum of squares of deviations of the correlation signal from which the data modulation component is removed and the approximation function. Further, the approximate number of data means the number of correlation signals from which the data modulation component used in the calculation by the least square method is removed.

[0013]

In the above structure, at the time of communication, at a predetermined cycle,
The fading speed and noise power of the correlation signal from which the data modulation component has been removed are sequentially detected. Then, based on this detection output, the number of approximate data that can minimize the estimation error is determined. With this, based on the actual propagation environment, it is possible to determine the optimum number of approximate data,
Even if the propagation environment changes, the propagation path characteristics can be estimated well.

[0014]

Embodiments of the present invention will now be described in detail with reference to the drawings. In the following description, the present invention is applied to a channel estimation device provided in a receiver of a mobile communication system using CDMA as a multiple access system and a RAKE receiver as a receiver capable of coping with frequency selective fading. The case of applying the above will be described as a representative.
That is, a case where the present invention is applied to a channel estimation device for RAKE combining will be described as a representative.

FIG. 1 is a block diagram showing the configuration of an embodiment of the present invention. In the figure, 10 is an input terminal to which the correlation signal V1 (i) from which the data modulation component is removed is supplied. Here, the correlation signal is a signal obtained by despreading the received signal. Further, the correlation signal V1 (i) from which the data modulation component is removed is a signal obtained by multiplying the correlation signal obtained by this despreading and the signal indicating the determination result. Therefore, the correlation signal V1 (i) from which the data modulation component is removed is composed of the component showing the propagation path characteristic and the noise. Note that i indicates time, and V1 (i) indicates that the correlation signal is a time discrete signal.

Reference numeral 20 denotes an input terminal to which the above-mentioned received signal V2, that is, the signal before despreading is supplied. Here, V2 indicates that the received signal is a continuous signal.

Reference numeral 30 denotes a Doppler frequency detecting section for sequentially detecting the Doppler frequency of the correlation signal V1 (i) at a cycle which is an integral multiple of the data transmission cycle Td for one symbol based on the correlation signal V1 (i). is there. This Doppler frequency detector 3
0 uses the fact that the correlation signal V1 (i) contains a component exhibiting a propagation path characteristic, and frequency analysis is performed on this correlation signal V1 (i) by spectrum analysis such as Fourier transform to obtain the most power. A large frequency component is detected, and the frequency of this frequency component is used as the Doppler frequency.

Reference numeral 40 denotes the correlation signal V1 (i) and the reception signal V2.
The SNR for calculating the power ratio (hereinafter, referred to as “SNR”) between the target signal (desired wave) and the interference wave (noise) is sequentially calculated based on the above and in a cycle that is an integral multiple of the data transmission cycle Td. It is a calculation unit. This SNR calculator 40 is a CD
In MA, the same frequency is used in all mobile units, so that the characteristics other than the target signal are interference waves, and the correlation signal V1 (i) is used as a signal component and the reception signal V2 is used as noise. , SN by taking the power ratio of both
Calculate R.

Reference numeral 50 is an approximate data number for determining an approximate data number M that can minimize the estimation error S by the least square method based on the detection output of the Doppler frequency detecting unit 30 and the calculation output of the SNR calculating unit 40. It is the decision unit.

Reference numeral 60 denotes an estimated value calculation unit for calculating an estimated value V3 (i) of the propagation path characteristic based on the approximation function F (i) that approximates the correlation signal V1 (i). This estimated value calculation unit 60
Uses the correlation signal V1 (i) for the number M of approximate data determined by the approximate data number determination unit 50 by the least square method so that the estimation error S with the correlation signal V1 (i) is minimized. , A parameter of the approximate function F (i) is calculated, and the correlation signal V1 is calculated based on the approximate function F (i) having this parameter.
The estimated value V3 (i) is calculated using (i).

Reference numeral 70 denotes an output terminal to which the estimated value V3 (i) calculated by the estimated value calculation section 60 is supplied. The output terminal 70 is connected to a weighting part (not shown). That is, it is connected to a weighting unit for obtaining a complex conjugate of the estimated value V3 (i) and multiplying it by the correlation signal V1 (i).

The operation of the above configuration will be described. The correlation signal V1 (i) input from the input terminal 10 is supplied to the Doppler frequency detection unit 30, the SNR calculation unit 40, and the estimated value calculation unit 60. Further, the reception signal V2 input from the input terminal 20 is supplied to the SNR calculation unit 40.

The Doppler frequency detecting section 30 sequentially, at a predetermined cycle, on the basis of the given correlation signal V1 (i) and
The Doppler frequency of the correlation signal V1 (i) is detected. Also,
The SNR calculator 40 sequentially calculates the SNR at a predetermined cycle based on the given correlation signal V1 (i) and the received signal V2.

As a result, the current propagation environment is successively detected at a predetermined cycle. This is because the Doppler frequency has a close relationship with the fading speed of the correlation signal V1 (i) and changes when the fading speed changes, and the SNR is close to the noise power of the correlation signal V1 (i). This is because they have a relationship and change when the noise power changes.

The Doppler frequency detection output and the SNR calculation output are supplied to the approximate data number determination unit 50 as parameters indicating the current propagation environment. Approximate data number determination unit 5
For 0, based on these two outputs, an approximate data number determination diagram showing the relationship between the approximate data number M and the estimation error S as shown in FIG. 3 is created for each Dopp frequency and each SNR. Based on the above, the approximate data number M that can minimize the estimation error S is determined. As a result, the approximate data number M is set to an optimum value according to the propagation environment.

The approximate data number M is set to a small value when the Doppler frequency is high, and is set to a large value when the Doppler frequency is low. This is because when the Doppler frequency is high, the time variation of the propagation path is large, and when the Doppler frequency is low, the time variation of the propagation path is small.

Further, the approximate data number M is set to a small value when the SNR is large (noise is small), and is set to a large value when the SNR is small (noise is large). This is because the noise removal capability may be small when the noise is small, but needs to be increased when the noise is large.

Therefore, when the Doppler frequency and the SNR have the same required direction of the approximated data number M, as in the case where the Doppler frequency is high and the SNR is large, or when the Doppler frequency is low and the SNR is small. Is determined as the approximate number of data M, which is the number of data with a stronger demand.

On the other hand, when the Doppler frequency is high and the SNR is small, or when the Doppler frequency is low and the SNR is large, the number of approximate data M is calculated by the Doppler frequency and the SNR.
When the required directions of 1 are different, the number of data points at the intersections of the two is determined as the approximate data M. As a result, even in such a case, the minimum necessary noise removal capability determined from the noise amount is secured.

The approximate data number M determined by the approximate data number determination unit 50 is supplied to the estimated value calculation unit 60. When the estimated value calculation unit 60 receives this approximate data number M, the estimated value calculation unit 60 first calculates the parameters of the approximate function F (i) by the least square method using the correlation signals V1 (i) for the approximate data number M. . As a result, the correlation signal V1 (i) and the approximation function F (i)
The sum of the squares of the deviations from, that is, the parameter that minimizes the estimation error S is obtained. This estimation error S is
It is expressed by the following equation (1).

S = Σ (V1 (i) -F (i)) ^ 2 (1) Here, the addition range in Σ is from i = T to i = T +
It is (M-1) Td.

Next, the estimated value calculation unit 60 calculates the estimated value V3 based on the approximation function F (i) in which this parameter is determined.
Calculate (i). In this case, the propagation path characteristic at the current time T + MTd is estimated to be F (T + MTd). Therefore,
When the approximate function F (i) is a quadratic function as shown in the following expression (2), the propagation path characteristic at the current time T + MTd is estimated as in the following expression (3).

F (i) = a · i ^ 2 + b · i + c (2) V3 (T + MTd) = a · (T + MTd) ^ 2 + b · (T + MTd) + c (3) Here, a , B, c are parameters of the quadratic function.

The estimated value V3 (i) is supplied to a weighting unit (not shown) via the output terminal 70. This allows
The estimated value V3 (i) is subjected to weighting for RAKE combining. The above is the configuration and operation of the channel estimation device. Next, an example of a specific configuration of the estimated value calculation unit 60 will be described. FIG. 2 is a block diagram showing an example of a specific configuration of the estimated value calculation unit 60.

The estimated value calculation unit 60 shown in the figure is composed of an acyclic digital filter and calculates the tap coefficient by calculating the parameters of the approximate function F (i),
Based on this tap coefficient, the estimated value V3 is obtained by weighting and adding the correlation signals V1 (i) corresponding to the number M of approximate data.
It is designed to calculate (i).

That is, in the figure, reference numeral 61 designates taps P (1) to P (K) (Mmax) corresponding to the largest approximate data number Mmax among the approximate data number M that can be determined by the approximate data number determination unit 50. ≦ K). This shift register 61 has an input terminal 1
By sequentially delaying the correlation signal V1 (i) input from 0, K correlation signals V1 (i) are simultaneously output.

62 (1) to 62 (K) are provided for each tap P (1) to P (K) of the shift register 61,
It is a tap coefficient multiplier that multiplies the correlation signals V1 (i) output from the corresponding taps P (1) to P (K) by tap coefficients A (1) to A (K).

Reference numeral 63 designates K tap coefficient multipliers 62.
It is a summer that adds all the multiplication outputs of (1) to 62 (K) and outputs an estimated value V3 (i).

Reference numeral 64 is a tap coefficient calculator for calculating the tap coefficients A (1) to A (K). The tap coefficient calculation unit 64 uses the correlation signal V1 (i) for the approximate data number M determined by the approximate data number determination unit 50 to minimize the estimation error S with the correlation signal V1 (i). As described above, the tap coefficients A (1) to A (K) are calculated by calculating the parameters of the approximate function F (i) by the method of least squares.

In this case, the tap coefficient calculator 64 calculates the tap coefficients A (1) to A (M) for the approximate data number M by the least square method as described above, and the remaining For the tap coefficients A (M + 1) to A (K), this is set to 0.

The tap coefficient calculation unit 64 actually obtains the tap coefficients A (1) to A (M) for each approximate data number M in advance, and the approximate data number determination unit is actually obtained. When the approximate data number M is determined at 50, the tap coefficients A (1) to A (K) corresponding to the approximate data number M are read from a table or the like, and the tap coefficient multiplier 62 (1) is read.
~ 62 (K).

The operation of the above configuration will be described. The correlation signal V1 (i) input from the input terminal 10 is sequentially delayed by the shift register 61 by the data transmission cycle Td. As a result, the K correlation signals V1 (i) are simultaneously output from the shift register 61. The K correlation signals V1 (i) are output to the corresponding tap coefficient multiplication units 6
Tap coefficients A (1) to A (K) at 2 (1) to 62 (K)
After being multiplied by, the sum is added by the adder 63.

In this case, tap coefficients A (1) to A (K)
Of these, tap coefficients A (1) to A for the number M of approximate data
(M) is calculated by calculating the parameters of the approximation function F (i) by the method of least squares so that the estimation error S is minimized. On the other hand, the remaining tap coefficient A
(M + 1) to A (K) are set to 0. As a result, the estimated value V3 (i) of the current time T + MTd becomes equal to the current time T +
M data immediately before MTd V1 (T) to V1 (T + (M-1)
Calculated from Td).

When the approximate data number determination unit 50 determines the approximate data number M as described above, the tap coefficient calculation unit 64 tap coefficients A (1) to A (A) corresponding to the approximate data number M. (K) is read from a table or the like and supplied to the tap coefficient multipliers 62 (1) to 62 (K).

According to this embodiment described in detail above, the following effects can be obtained. First, according to this embodiment, the Doppler frequency and SNR of the correlation signal V1 (i) from which the data modulation component is removed are detected, and the estimation error S by the least square method is minimized based on these two detection outputs. Since the approximate number of data M to be obtained is determined, based on the actual propagation environment,
The optimum approximate data number M can be set. As a result, it becomes possible to satisfactorily estimate the propagation path characteristics even when the propagation environment changes.

Further, according to this embodiment, in detecting the fading speed of the correlation signal V1 (i) from which the data modulation component has been removed, the Doppler frequency having a close relationship with the fading speed is detected. Therefore, the fading speed can be easily detected.

Further, according to this embodiment, in detecting the noise power of the correlation signal V1 (i) from which the data modulation component has been removed, the SNR having a close relationship with this noise power is detected. Therefore, the noise power can be easily detected.

Further, according to this embodiment, when the estimated value calculation unit 60 is composed of an acyclic digital filter, the tap coefficients A (1) to
Since A (K) can be set, it is possible to avoid the complicated work of setting the tap coefficients A (1) to A (K) at the time of design.

Further, according to this embodiment, when the estimated value calculation unit 60 is configured by the acyclic digital filter, the tap coefficient A is previously set for each approximate data number M.
(1) to A (K) are obtained in advance, and the approximate data number determination unit 5
When the approximate data number M is determined by 0, the tap coefficients A (1) to A (K) corresponding to the approximate data number M are read out from the table or the like, and the tap coefficient multipliers 62 (1) to 62 (62).
Since it is supplied to (K), it is possible to quickly set the tap coefficients A (1) to A (K) after the approximate data number M is determined.

Although one embodiment of the present invention has been described in detail above, the present invention is not limited to the above embodiment.

For example, in the above embodiment, the case where the Doppler frequency is detected by frequency analysis of the correlation signal V1 (i) by spectrum analysis has been described. However, in the present invention, for example, the power P and the first-order correlation function r of the correlation signal V1 (i) represented by the following equations (3) and (4) are obtained, and based on these, the following equation (5) is obtained. The center-of-gravity frequency f may be obtained, and this center-of-gravity frequency f may be used as the Doppler frequency.

P = ΣV1 (i) ^ 2 (4) r = ΣV1 (i) · (V1 (i) −1) (5) f = 1 / (2πTd) arccos (r / P) (6) ) However, the addition range in Σ is from i = T to i = (T +
Up to (M-1) Td).

Further, in the previous embodiment, the correlation signal V1
The case has been described in which the fading speed of the correlation signal V1 (i) is detected by detecting the Doppler frequency of (i). However, the present invention may detect the fading speed by observing the moving speed of the receiver of the mobile communication system, for example.

Further, in the previous embodiment, the correlation signal V1
By calculating the SNR of (i), the correlation signal V1
The case of detecting the noise power of (i) has been described. However, the present invention may detect noise power by a method other than this.

Further, in the above embodiment, the fading speed and noise power of the correlation signal V1 (i) are sequentially detected at a predetermined cycle during communication, and based on the detection output, the fading speed and the noise power shown in FIG.
The case has been described in which the approximate data number determination diagram as shown in (1) is created and the approximate data number M is determined based on this diagram.

However, according to the present invention, the optimum approximate number of data M is calculated in advance for each of a plurality of expected fading speeds and noise powers, and a table is prepared in advance.
The number M of approximate data may be determined by referring to this table based on the detection output of the fading speed and the noise power.

In addition, in the previous embodiment, the present invention is
The case has been described in which CDMA is used as the multiple access scheme and the RAKE scheme receiver is used as a receiver capable of coping with frequency selective fading, and is applied to a propagation path estimation apparatus provided in a receiver of a mobile communication system. That is, the case where the present invention is applied to the propagation path estimation device for RAKE combining has been described.

However, the present invention can also be applied to a channel estimation device used for purposes other than RAKE combining. The present invention can also be applied to a channel estimation device of a mobile communication system using a multiple access system other than CDMA. Further, the present invention can be applied to a propagation path estimation device of a communication system other than the mobile communication system.

That is, the present invention can be applied to a general channel estimation device for estimating channel characteristics based on the approximation function F (i) whose parameters are determined by the method of least squares.

In addition to this, it goes without saying that the present invention can be modified in various ways without departing from the scope of the invention.

[0061]

As described above in detail, according to the present invention,
At the time of communication, the fading speed and noise power of the correlation signal from which the data modulation component is removed are sequentially detected at a predetermined cycle, and based on this detection output, approximate data that can minimize the estimation error by the least square method. Since the number is determined, the optimum approximate data number can be determined based on the actual propagation environment. As a result, it becomes possible to satisfactorily estimate the propagation path characteristics even when the propagation environment changes.

[Brief description of drawings]

FIG. 1 is a block diagram showing the configuration of an embodiment of the present invention.

FIG. 2 is a block diagram showing an example of a specific configuration of an estimated value calculation unit shown in FIG.

FIG. 3 is a diagram for explaining a method of determining the number of approximate data items.

[Explanation of symbols]

 10, 20 ... Input terminal 30 ... Doppler frequency detection unit 40 ... SNR calculation unit 50 ... Approximate data number determination unit 60 ... Estimated value calculation unit 70 ... Output terminal 61 ... Shift register 62 (1) to 62 (K) ... Tap coefficient Multiplier 63 ... Summer 64 ... Tap coefficient calculator.

Claims (6)

[Claims] 1. An approximation function, which is provided in a direct spread spectrum / spread spectrum communication system communication system and whose parameters are calculated by the least squares method so that an error with a correlation signal from which a data modulation component is removed is minimized. On the basis of a channel estimation device for estimating channel characteristics, a fading rate detecting means for detecting a fading rate of the correlation signal from which the data modulation component has been removed, and noise power of the correlation signal from which the data modulation component has been removed. Noise power detection means for detecting, and based on the detection output of the fading speed detection means and the detection output of the noise power detection means, as the number of data used when calculating the parameter, the error can be minimized The data number determining means for determining the number of data as described above, and the number of data before the data number determined by the data number determining means. Using the correlation signal from which the data modulation component has been removed, the parameter is calculated by the least squares method, and the estimated value calculation is performed to calculate the estimated value of the propagation path characteristic based on the approximation function having the calculated parameter. A propagation path estimation apparatus comprising: 2. The fading speed detecting means is configured to detect the fading speed by detecting the Doppler frequency of the correlation signal from which the data modulation component has been removed. The propagation path estimation device according to claim 1. 3. The fading speed detection means is configured to frequency analyze the correlation signal from which the data modulation component has been removed by spectrum analysis, and detect the frequency at which the power shows a maximum value as the Doppler frequency. The propagation path estimation apparatus according to claim 2, wherein 4. The fading speed detecting means is configured to detect a center-of-gravity frequency f represented by the following equation as the Doppler frequency.
The propagation path estimation device described. f = 1 / (2πTd) arccos (r / P) where P: power of the correlation signal from which the data modulation component has been removed r: first-order correlation function of the correlation signal from which the data modulation component has been removed Td: data transmission period 5. The noise power detecting means is configured to detect the noise power by obtaining a ratio between a correlation signal from which the data modulation component has been removed and a received signal. The propagation path estimation apparatus according to claim 1. 6. The estimated value calculating means has at least as many taps as the maximum data number that can be determined by the data number determining means, and sequentially delays the correlation signal from which the data modulation component is removed, The number of data is calculated by the least square method using the delay unit that simultaneously outputs the correlation signals for the number of taps and the correlation signals for the number of data determined by the number-of-data determination unit. Of the correlation signals output from the delay means, based on the tap coefficient calculated by the tap coefficient calculation means, the tap coefficient calculation means for calculating the tap coefficient for the number of data determined by the determination means; Weighting and adding means for weighting and adding the correlation signals for the number of data decided by the number deciding means. The propagation path estimation apparatus according to claim 1, wherein