JP3399019B2 - Viterbi equalizer - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a Viterbi equalizer suitable for use in, for example, automobile telephones.

[0002]

2. Description of the Related Art In the United States, Europe and Japan, car telephone system digitalization is in progress. In mobile communication such as this car telephone, due to the influence of so-called multipath due to the presence of a high-rise building between the mobile station and the base station at high speed like a car, the transmission characteristics between the base station and the mobile station However, data transmission with few errors was difficult. Moreover, this equivalent transmission characteristic changes from moment to moment.

In such a mobile communication system, an equalization technique for correcting such transmission characteristics is indispensable in order to realize reception with few errors.

As a conventional equalization technique, a Viterbi equalizer has been proposed which decodes transmission data based on maximum likelihood sequence estimation by using transmission characteristics between a base station and a mobile station.

The basic configuration of this Viterbi equalizer is as shown in FIG. 4, and here, the Viterbi equalizer shown in FIG. 4 is adopted in a European mobile telephone, GSM (Group Special Moval). An example applied to the method will be described.

In FIG. 4, the received signal supplied to the input terminal 1 is supplied to the branch metric calculation circuit 21 constituting the Viterbi estimation unit 2 and the received signal is supplied to the synchronization signal data detection unit 3 for synchronization. The synchronization signal data from the signal data detector 3 is supplied to the transmission line characteristic estimator 4.

The speech channel from the GSM system base station adopted in Europe to the mobile station (automobile) has a frame structure as shown in FIGS. 5A and 5B. As shown in FIG. 5B, a synchronization signal pattern (SYNC pattern) having a known pattern is added to the central portion of each time slot, and the time slot is used by the transmission path characteristic estimation unit 4. Then, the impulse response (hereinafter referred to as the channel response) of the transmission system interposed between the transmitter and the receiver is estimated.

In the case of this GSM system, a modulation system called GMSK (Gaussian minimum shift keying) is adopted, but since the high frequency transmission system is converted into a baseband signal by passing through a demodulator, it will be explained below. In order to simplify, we will proceed with signal processing in the baseband.

In this GSM system, eight types of data series are designated in advance as the synchronization signal pattern,
One of them is shown in FIG. A conventional general procedure for modeling a channel response using this synchronization signal pattern will be described.

Now, the case where the channel response is as shown in FIG. 7 is taken as an example (in reality, this channel response is unknown). In FIG. 7, the unit in the time axis direction is equal to the symbol transmission interval. The sync signal pattern of FIG. 7 is the sync signal pattern of FIG. The synchronization signal data received when passing through the transmission system having such a channel response is expressed by the following equation.

[0011]

[Equation 1] Here, y _i is a received signal, x _i is a synchronization signal pattern, h _i
Represents the channel response. The values are sampled at the symbol time intervals T, respectively.

When the received signal corresponding to the sync signal pattern portion is calculated according to equation 1, an output signal as shown in FIG. 7 is obtained. On the receiver side, the known information is the sync signal pattern x _i and the received signal y _i .

In the conventional modeling procedure of the transmission path characteristic estimating section 4, first, the synchronization signal data section is detected by taking the correlation between the received signal and the synchronization signal pattern.

Next, the cross-correlation function r _j between the sync signal data portion and the sync signal pattern is calculated.

[0015]

[Equation 2]

Next, normalization is performed using the maximum value of this cross-correlation function r _j . The cross-correlation function calculated in this way is shown in FIG. The branch metric calculation circuit 21 estimates the channel response by this cross-correlation function.
Supply to.

After estimating this channel response, the transmission data sequence is decoded using the Viterbi algorithm. FIG. 8 shows a generalized transmission line equivalent model. Here, the example of FIG. 9 in which the generalized transmission path equivalent model of FIG. 8 is modeled by specifically limiting its channel response length will be described.

When modeled as shown in FIG. 9, it can be regarded as a convolutional encoder with constraint length = 4 and coding rate r = 1/1. However, the difference from the normal convolutional encoder is that the adder 71 performs a linear operation and the shift registers T ₀ , T ₁ , T ₂ and T ₃
The symbol input to is a binary value of <+1> and <-1>, and each output of the shift register is weighted corresponding to the channel responses h _-1 , h ₀ , h ₊₁ and h _+2. These are two points that are added by the adder 71 after the addition.

The symbol G transmitted in the case of modeling in this way is expressed by the following equation.

[0020]

[Equation 3] Here, <T _j > represents the contents stored in the register T _j .

FIG. 10 shows a trellis diagram showing transitions of the internal states of the transmission line in the transmission line equivalent model shown in FIG. The three-letter alphabet corresponding to each state node S _i in FIG. 10 represents the internal state of the shift register in each time slot. Here, since the shift register takes the values of <+1> and <-1>, they are represented as H and L for convenience of expression. In FIG. 10, a grid structure diagram that is normally used is modified so that a solid line is used when the information input symbol <-1> is input, and a broken line is used when the information input symbol <+1> is input. It indicates that the transition as shown occurs.

On the other hand, the received signal data Y _k is input to the branch metric calculation circuit 21 to calculate the likelihood of its transition. Although some metrics have been proposed for measuring the likelihood, the Hamming distance, which is the most general evaluation measure in Viterbi decoder, is applied in a broad sense.

The branch metric at the time slot t (k) is calculated by the following equation.

[0024]

Equation 4] _{b (k, S i → S} n) = | Y k -G k | , where, Y _k is the received signal data and a symbol G _k is transmitted from the equivalent transmission path model , Takes the value calculated by Equation 3.

The branch metric obtained by the branch metric calculation circuit 21 is referred to as ACS (Add Compare Sele).
ct) Supply to the circuit 22. The ACS circuit 22 is composed of an adder, a comparator, and a selector. In each state, the branch metric and the path metric of one time slot before stored in the path metric storage circuit 23 are added to obtain the value. The smaller one is selected as the likely survival path. Here, the path metric is a value obtained by adding the branch metrics in the surviving paths.

The output signal of the ACS circuit 22 is supplied to the path metric storage circuit 23 via the normalization circuit 24, and the output signal of the ACS circuit 22 is supplied to the maximum likelihood path detection circuit 25.

The maximum likelihood path detection circuit 25 detects a path having the minimum path metric value and outputs the contents of the path memory 26 corresponding to the path as decoded data. The path memory 26 is a memory for estimating and storing an information bit string.

FIG. 11 shows a logic unit which constitutes this Viterbi equalizer. In FIG. 11, each metric has the following contents.

P (k−1, S _i ): Path metric P (k−1, S _j ) of the surviving path reaching the state node S _i at time slot t (k−1): Time slot t (k -1) The path metric b (k, S _i → S _n ) of the surviving path that has reached the state node S _j : Corresponds to the transition from the state node S _i to the state node S _{n at the} time slot t (k). Branch metric b (k, S _j → S _n ): Branch metric corresponding to the transition from the state node S _j to the state node S _n in the time slot t (k).

M (k−1, S _i ): Path memory M (k−1, S _j ) of the surviving path reaching the state node S _i at time slot t (k−1): Time slot t (k -1) Path memories <-1>, <+1> of the surviving path reaching the state node S _j : Information symbols P (k, S _n ): Time estimated to be transmitted in the time slot t (k): Time slot t (k) path surviving path which reaches the state node S _n has the metric M (k, S _n): a path memory with the survival path which reaches the state node S _n in time slot t (k)

Here, assuming that the constraint length is k, the number of states is 2
Since there are ^k-1 units, the number of logic units shown in FIG. 11 is basically required to be 2 ^k-1 states. Further, as in the block configuration of the Viterbi equalizer shown in FIG.
In general, 4 is provided to reduce the scale of the path metric storage circuit 23 and prevent overflow during the calculation of the path metric.

As a concrete process of this normalization, first, the minimum value of the path metric is detected, and then the value is subtracted from each path metric amount. The number of surviving paths selected in this way is 2 ^{k−1, which} is the same as the number of states.

In each time slot, the operation of selecting the surviving path and the operation of updating the path metric and the path memory 26 corresponding to that path are repeated. It is known that, if this operation is performed for a sufficiently long time, it merges into the same path before a certain time, and this state is shown in FIG. The length of the path from the latest processing time point to the time when the paths are merged is called the truncated path length.

The method of updating the path memory shown in FIG. 11 is determined according to each state. For example, the logical unit of "LLL" is determined as <-1>, the logical unit of "HLL" is determined as <+1>, and so on.

In the maximum likelihood judgment, the path having the minimum path metric value is detected, and the contents of the path memory corresponding to the path are cut off and traced back by the path length (usually set to 3 to 4 times the constraint length). It is output as an information symbol at that time.

The signal processing flow of this conventional Viterbi equalizer will be described with reference to the flowchart of FIG. First, a sync signal pattern is detected when the received signal data _Yk is supplied to the input terminal 1 (step S1), and the cross-correlation between the sync signal pattern of the received signal data _Yk and the previously stored sync signal pattern is detected. The function is calculated in the transmission path characteristic estimation unit 4 (step S2) and the channel response is estimated (step S3). Next, the branch metric calculation circuit 21 calculates a branch metric (step S4), and then starts calculation for the Nth state (step S5).

Next, the state-1 address one time slot before is set (step S6), the path metric stored in the path metric storage circuit 23 of this set address is read (step S7), and this path is set. The metric is added to the branch metric calculated in step S4 by the ACS circuit 22, and the addition output is added to the register P.
1 (step S8).

Next, in step S9, the address of the state-2 one time slot before is set, the path metric stored in the path metric storage circuit 23 of this set address is read (step S10), and this path metric is read. Is added to the branch metric calculated in step S4 by the ACS circuit 22, and the added output is stored in the register P2 (step S11).

Next, the ACS circuit 22 compares and stores the values stored in the registers P1 and P2 (steps S12 and S13) and outputs the selected value (step S14). The metric storage circuit 23 is updated (step S15) and the path memory 26 is updated (step S16).

Steps S5 to S16 described above
The processes up to are repeated by the number of states 2 ^k-1 (step S
17). After the above processing is completed, the maximum likelihood path detection circuit 2
The path having the minimum path metric value is detected by 5 (step S18), and the normalization processing is performed by subtracting the minimum value of the path metric from each path metric amount (step S19).

Then, the maximum likelihood path detection circuit 25 sets the address of the maximum likelihood path (step S20) and outputs the contents of the path memory 26 as decoded data (step S).
21).

In such a conventional Viterbi equalizer, it can be confirmed that the channel response can be estimated with a certain degree of accuracy by comparing the channel response of FIG. 7 with the cross-correlation function r _j. It is revealed that a "fake impulse response" that should not be detected is also detected. The cause of this is apparent when the autocorrelation function a _j of the synchronization signal pattern is calculated.

[0043]

[Equation 5]

The autocorrelation function thus calculated is shown in FIG. As is clear from FIG. 7, there are some peaks having a considerably large level other than the main peak, which is a factor that deteriorates the accuracy when estimating the channel response.

In view of the above point, the present inventor previously proposed a Viterbi equalizer capable of obtaining an accurate equalization characteristic.

An example of the Viterbi equalizer proposed previously will be described. In this example, the transmission line characteristic estimating unit 4 shown in FIG. 4 is used.
Is configured as described below. The channel response is modeled as shown in FIG. If modeled in this way, the expected signal y _i that will be received is given by equation 1 above.

On the other hand, when the actually received signal is represented by Y _i , the error ε _i regarding the i-th symbol is represented by the following equation.

[0048]

Ε _i = y _i −Y _i The sum of squares E of this error is obtained.

[0049]

[Equation 7]

The impulse train h _n is determined so as to minimize the error E. In this example, the least squares method is applied. Therefore, the equation 7 is partially differentiated with respect to h _n .

[0051]

[Equation 8] In this number 8, n = -km,-(km-1), ... 0, ...
‥ + (kp-1), is simultaneous equations shown in the following equation obtained by substituting + k _p.

[0052]

[Equation 9]

Since the coefficient matrix of this simultaneous equation is a symmetric matrix, it is not necessary to carry out the calculation for each element. Furthermore, in order to solve this simultaneous equation, it is general to first perform LU decomposition of the coefficient matrix and then solve. The transmission line characteristic estimation unit 4 according to this example can accurately determine the channel response by the above means.

The signal processing flow of the Viterbi equalizer of this example will be described with reference to the flowchart of FIG. First, when the received signal data Y _k is supplied to the input terminal 1, the sync signal pattern portion is detected (step S1). The detection of the sync signal pattern portion is performed by correlating the received signal data Y _k with the sync signal pattern stored in advance.

Next, in the transmission path characteristic estimating section 4, a minimum of 2 is obtained by using the detected synchronization signal pattern section as a reference signal.
The impulse response between the transmitter and the receiver is modeled using the multiplication method (step S2), and the channel response is estimated (step S3).

Next, the branch metric calculation circuit 21 calculates the branch metric (step S4), and then starts the calculation for the Nth state (step S5).

Next, the address of the state-1 one time slot before is set (step S6), then the path metric stored in the path metric storage circuit 23 of this set address is read (step S7), and this path is read. The metric is added to the branch metric calculated in step S4 by the ACS circuit 22, and the addition output is added to the register P.
1 (step S8).

Next, in step S9, the address of the state-2 one time slot before is set, the path metric stored in the path metric of this set address is read (step S10), and this path metric is read in step S4. The calculated branch metric is added to the ACS circuit 22, and the added output is stored in the register P2 (step S11).

Next, the ACS circuit 22 compares and selects the stored values of the registers P1 and P2 (steps S12 and S13), outputs the selected value (step S14), and passes the value. The metric storage circuit 23 is updated (step S15) and the path memory 26 is updated (step S16).

Steps S5 to S16 described above
The processes up to are repeated by the number of states 2 ^k-1 (step S
17). After the above processing is completed, the maximum likelihood path detection circuit 2
The path having the minimum path metric value is detected by 5 (step S18), and the normalization processing is performed by subtracting the minimum value of the path metric from each path metric amount (step S19).

Then, the maximum likelihood path detection circuit 25 sets the address of the maximum likelihood path (step S20) and outputs the contents of the path memory 26 as decoded data (step S).
21).

In this example, since the impulse response between the transmitter and the receiver is modeled by using the least squares method with the synchronization signal pattern portion as the reference signal as described above, the signal between the transmitter and the receiver is modeled. There are benefits to being able to uniquely model the impulse response.

In this example, the minimum transmission model is 2 as described above.
Since it is estimated by the multiplication method, it is a model with a minimum error, and has the advantage of obtaining good equalization characteristics.

[0064]

However, there is a problem in that the number of calculation processes is large when the above-mentioned formula 9 is calculated, and this calculation process takes a long time. That is, in Equation 9,
For example, the parameters are (l + m) = 11, km = 2, kp
= 2, channel response length = 5, the following simultaneous equations are obtained.

[0065]

[Equation 10]

In this way, the parameter is set to (l + m) =
FIG. 15 shows the calculation result when modeling and processing when 5, (l + m) = 11 and (l + m) = 21, and FIG. 16 shows the number of calculation processes.

The calculation result of FIG. 15 has parameters (l +
m) = 5, (l + m) = 11, as apparent from comparison with the (l + m) = 21 and also the channel response of FIG. 7 when the (k _i), it can be confirmed that it is possible to very precisely identified.

As shown in FIG. 16, when the parameter is set to (l + m) = 5, the coefficient matrix multiplication (MPY) is 75 times and the right side VeC multiplication (MPY) is performed.
Y) 25 times, L / U decomposition multiplication (MPY) 30 times,
Division (DIV) 10 times, forward and backward substitution multiplication (M
PY) is 20 times, division (DIV) is 5 times, and when the parameter is (l + m) = 11, coefficient matrix multiplication (MPY) is 165 times and right side VeC multiplication (MP) is performed.
Y) 55 times, L / U decomposition multiplication (MPY) 30 times,
Division (DIV) 10 times, forward and backward substitution multiplication (M
PY) is 20 times, division (DIV) is 5 times, and when the parameter is (l + m) = 21, coefficient matrix multiplication (MPY) is 315 times and right side VeC multiplication (MP) is performed.
Y) 105 times, L / U decomposition multiplication (MPY) 30
Times, division (DIV) is 10 times, forward and backward substitution multiplication (MPY) is 20 times, and division (DIV) is 5 times.

As is apparent from FIG. 16, the number of times of this arithmetic processing is the process of obtaining the coefficient matrix and L ·
The number of multiplications in U decomposition is dominant.

In view of the above point, the present invention has an object to enable the transmission characteristics between the base station and the mobile station to be determined accurately and at high speed.

[0071]

A Viterbi equalizer according to the present invention includes a sync signal data detecting means 3 for detecting a sync signal data portion in a received signal data sequence, as shown in FIG.
Transmission path characteristic estimating means 4 for modeling an impulse response between a transmitter and a receiver by using the least squares method with the synchronization signal data portion detected by the synchronization signal data detecting means 3 as a reference signal, and this transmission path. Minimum 2 by the characteristic estimation means 4
A ROM 4a in which a coefficient matrix when using the multiplication method is calculated in advance and written as data, and a decoding means for decoding the transmission data sequence using the Viterbi algorithm based on the transmission model obtained by the transmission path characteristic estimation means 4. It consists of:

In the Viterbi equalizer of the present invention, the data to be written in the ROM 4a is L.
It is the value after U decomposition.

Further, the Viterbi equalizer of the present invention is as described above.
The data written in the ROM 4a is the inverse matrix of this coefficient matrix.

[0074]

According to the present invention, the impulse response between the transmitter and the receiver is modeled by using the least squares method with the synchronization signal data as the reference signal. Can be uniquely modeled, and the model determined in this way is a model with a minimum error in the meaning of least-squares estimation. As a result, good equalization characteristics can be obtained, and a coefficient matrix previously calculated in the ROM 4a, The L / U decomposed value or the inverse matrix of this coefficient matrix is written, and this transmission line characteristic estimation means 4 is used.
When using the least squares method, the coefficient matrix that has been previously calculated and written in the ROM 4a, its L / U decomposed value, or the inverse matrix of this coefficient matrix is used. It can be reduced in number, and can be processed faster.

[0075]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the Viterbi equalizer of the present invention will be described below with reference to FIGS. In FIG. 1, parts corresponding to those in FIG. 4 are designated by the same reference numerals, and detailed description thereof will be omitted. In FIG. 1 as well, as shown in FIG. 4, the received signal supplied to the input terminal 1 is supplied to the branch metric calculation circuit 21 constituting the Viterbi estimation unit 2 and the received signal is supplied to the synchronization signal data detection unit 3. The sync signal data from the sync signal data detector 3 is supplied to the transmission path characteristic estimator 4.

Also in this example, as described above, in the transmission path characteristic estimating unit 4, in order to model the impulse response (channel response) between the transmitter and the receiver using the synchronization signal pattern, The synchronization signal is used as a reference signal by the least square method.

In this case, in this example, the coefficient matrix of Equation 9 is used.

[Equation 11] Is calculated in advance and stored in the ROM 4a, and the ROM is used when the equation 9 is calculated in the transmission path characteristic estimation unit 4.
The coefficient matrix stored in 4a is used.

This coefficient matrix (Equation 11) is uniquely determined regardless of the transmission path characteristics by setting the channel response length to be modeled and the parameters of Equation 9.

For example, the parameters of the equation 9 are set as km = 2kp =
When 2 (l + m) = 11 and channel response length = 5, the coefficient matrix is

[Equation 12] Is.

In this ROM 4a, a necessary number of predictable coefficient matrices are calculated in advance and stored as a table. When storing the coefficient matrix in the ROM 4a, as shown in FIG. 3, first, the synchronization signal pattern is specified (step S1), the coefficient matrix is calculated (step S2), and then the coefficient matrix is stored in the ROM 4a as a table. Store (step S3).

Others are the same as those of the conventional Viterbi equalizer described with reference to FIG.

The operation of this example will be described below with reference to the flowchart of FIG. First, when the received signal data Y _k is supplied to the input terminal 1, the sync signal pattern portion is detected (step S1). The detection of the sync signal pattern portion is performed by correlating the received signal data _Yk with the sync signal pattern stored in advance.

Next, in the transmission path characteristic estimation unit 4, the ROM
The predetermined coefficient matrix of 4a is read (step S
Along with 2), the impulse response between the transmitter and the receiver is modeled by using the least squares method with the detected synchronization signal pattern portion as a reference signal (step S3), and the channel response is identified (step S4).

In this case, the ROM 4a is used to perform the operation of the equation (9).
Since the coefficient matrix previously calculated and stored in is used, this calculation is unnecessary, and the channel response can be identified at such a high speed.

Next, the branch metric calculation circuit 21 calculates the branch metric (step S5), and then starts the calculation for the Nth state (step S6). Next, the address of the state-1 one time slot before is set (step S7), the path metric stored in the path metric storage circuit 23 of this set address is read (step S8), and this path metric is stepped. Branch metric calculated in S5 and A
The CS circuit 22 performs addition, and the addition output is stored in the register P1 (step S9).

Next, in step S10, the address of the state-2 one time slot before is set, the path metric stored in the path metric of the set address is read (step S11), and this path metric is read in step S5. Calculated branch metric and ACS
The circuit 22 performs addition, and the addition output is stored in the register P2 (step S12).

Next, the ACS circuit 22 compares and selects the stored values of the registers P1 and P2 (steps S13 and S14), outputs the selected value (step S15), and passes the value. The metric storage circuit 23 is updated (step S16) and the path memory 26 is updated (step S17).

Steps S6 to S17 described above
The processes up to are repeated by the number of states 2 ^k-1 (step S
18). After the above processing is completed, the maximum likelihood path detection circuit 2
The path having the minimum path metric value is detected by 5 (step S19), and the normalization process is performed by subtracting the minimum value of the path metric from each path metric amount (step S20).

Then, the maximum likelihood path detection circuit 25 sets the address of the maximum likelihood path (step S20), and the contents of the path memory 26 are output as decoded data (step S).
22).

In this example, since the impulse response between the transmitter and the receiver is modeled by using the least squares method with the synchronization signal pattern portion as the reference signal as described above, the signal between the transmitter and the receiver is modeled. There are benefits to being able to uniquely model the impulse response.

In this example, the minimum transmission model is 2 as described above.
Since it is estimated by the multiplication method, it is a model with a minimum error, and has the advantage of obtaining good equalization characteristics.

According to this embodiment, the coefficient matrix calculated in advance is stored in the ROM 4a, and when the transmission line characteristic estimation unit 4 calculates by the least square method, this R
Since the coefficient matrix calculated and stored in advance in the OM 4a is used, the number of calculation processes at this time can be reduced, and there is an advantage that the process can be performed faster.

In the above embodiment, the coefficient matrix is stored in the ROM 4a as a table. However, in order to solve the simultaneous equations of the equation 9, it is general that the coefficient matrix is first decomposed into L and U and then solved. , This ROM4a
The data to be stored in may be the value after L / U decomposition of this coefficient matrix.

In this case, the number of calculation processes can be further reduced, and the high speed processing can be performed.

Generally, when a certain matrix is multiplied by its inverse matrix, it becomes a unit matrix. Therefore, since the equation 9 can be solved using the inverse matrix of this coefficient matrix and the unit matrix, the data stored in the ROM 4a may be the inverse matrix of this coefficient matrix.

In this case, since the coefficient matrix is uniquely determined as described above, the inverse matrix of the coefficient matrix can be obtained in advance. For example, the inverse matrix of Equation 12 is as follows.

[Equation 13]

Further, the present invention is not limited to the above-mentioned embodiments, and it goes without saying that various other configurations can be adopted without departing from the gist of the present invention.

[0098]

According to the present invention, since the impulse response between the transmitter and the receiver is modeled by using the least squares method with the synchronization signal data as the reference signal, the impulse response between the transmitter and the receiver is modeled. The impulse response can be uniquely modeled, and the model thus determined is in the sense of least squares estimation:
It is a model with a minimum error, and good equalization characteristics are obtained as a result, and the coefficient matrix calculated in advance, its U / V decomposed value, or the inverse matrix of this coefficient matrix is written in the ROM 4a. This ROM 4a is used when the road characteristic estimating means 4 calculates by the method of least squares.
Since the coefficient matrix that has been calculated and written in advance, its U / V decomposed value, or the inverse matrix of this coefficient matrix is used, the number of calculation processes at this time can be small, and the benefit of faster processing can be obtained. is there.

[Brief description of drawings]

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

FIG. 2 is a flowchart provided for explanation of FIG.

FIG. 3 is a flowchart for explaining the main part of the present invention.

FIG. 4 is a configuration diagram showing a Viterbi equalizer.

FIG. 5 is a diagram provided for explaining a Viterbi equalizer.

FIG. 6 is a diagram provided for explaining a Viterbi equalizer.

FIG. 7 is a diagram provided for explaining a Viterbi equalizer.

FIG. 8 is a diagram showing a generalized transmission line equivalent model.

FIG. 9 is a diagram showing an embodied transmission line equivalent model.

FIG. 10 is a diagram showing a trellis representation.

FIG. 11 is a diagram showing a logical unit of a Viterbi equalizer.

FIG. 12 is a diagram showing metric calculation and survivor paths.

FIG. 13 is a flowchart for explaining a conventional Viterbi equalizer.

FIG. 14 is a flowchart for explaining a Viterbi equalizer.

FIG. 15 is a diagram used for explanation.

FIG. 16 is a diagram used for explanation.

[Explanation of symbols]

1 input terminal 2 Viterbi estimation section 3 Sync signal data detector 4 Transmission line characteristic estimation unit 4a ROM

Front page continued (58) Fields surveyed (Int.Cl. ⁷ , DB name) H04L 25/08 H03M 13/23 H04B 3/04 H04L 27/01

Claims (3)

(57) [Claims] 1. A least-squares method using a synchronization signal data detecting means for detecting a synchronization signal data portion from a received signal data sequence and a synchronization signal data portion detected by the synchronization signal data detecting means as a reference signal. A transmission path characteristic estimating means for modeling an impulse response between a transmitter and a receiver, and a ROM in which a coefficient matrix when the least square method is used in the transmission path characteristic estimating means is calculated in advance and written as data. A Viterbi equalizer, comprising: a decoding unit for decoding a transmission data sequence using a Viterbi algorithm based on the transmission model obtained by the transmission path characteristic estimating unit. 2. The Viterbi equalizer according to claim 1, wherein
The data to be written in the ROM has a coefficient matrix of L
A Viterbi equalizer characterized in that it is set to a value after U decomposition. 3. The Viterbi equalizer according to claim 1, wherein
A Viterbi equalizer characterized in that data to be written in the ROM is an inverse matrix of the coefficient matrix.