WO2001022598A1

WO2001022598A1 - Soft decision data creating method and device

Info

Publication number: WO2001022598A1
Application number: PCT/JP1999/005053
Authority: WO
Inventors: Natsuhiko Nakayauchi
Original assignee: Fujitsu Limited
Priority date: 1999-09-17
Filing date: 1999-09-17
Publication date: 2001-03-29

Abstract

A soft decision data creating device for converting input data weighted according to the levels to soft decision data the number of bits of which is smaller than that of the input data and outputting the soft decision data. An average calculating section calculates the level average E of the input data for every length of error correcting code. A dispersion calculating section calculates the dispersion V representing the scattering of the levels of the input data from the average. A soft decision data determining section, based on the average E and dispersion V, sets a first input level range for converting the input data to soft decision data all the bits of which are '1', sets a second input level range for converting the input data to soft decision data all the bits of which are '0', and allocates a third level range between the first and second ranges to the other soft decision data than those the bits of which are '1' or '0', thus converting the input data according to the level range in which the data lies to soft decision data and outputting the data.

Description

Specification

Method and apparatus for generating soft decision data

Technical field

The present invention relates to a method and an apparatus for generating soft decision data, and more particularly to a soft decision input data which is weighted by level and output as soft decision data with a small number of bits of the input data. The present invention relates to a method and apparatus for generating judgment data.

Background art

The erroneous correction code is used to correct erroneous information contained in received information, reproduction information, and the like so that the original information can be correctly decoded, and is applied to various systems. For example, when transmitting data without error in mobile communications and other data communications

Or, it is applied when reproducing data from large-capacity storage media such as magnetic disks and CDs without error. Such error correcting codes include convolutional codes and turbo codes.

-Convolutional code

Figure 9 shows an example of a convolutional encoder. It has a 2-bit shift register SFR and two exclusive OR circuits EX0R1 and EX0R2, where EX0R1 is the exclusive OR g of the input and R. Outputs, EX0R2 outputs an exclusive OR gi between the input and the R _0. Therefore, the input / output relationship of the convolutional encoder and the state of the shift register SFR when the input data is 01001 are as shown in FIG.

The contents of the shift register SFR of the convolutional encoder are defined as state. As shown in Fig. 11, four states of 00, 01, 10, and 11 are peri, where each is state a and state b. , State c, and state d. In the convolutional encoder shown in FIG. 9, it is determined whether the state of the shift register SFR is a to d and whether the next input data is "0" or "1". , And the output (g ₀ , and the next state are uniquely determined. Figure 12 shows the relationship between the state of the convolutional encoder and the input and output. The dotted line indicates the “0” input, and the solid line indicates the “1” input. That is,

(1) In state a, if "0" is input, the output will be 00 and the state will be a. If "1" is input, the output will be 11 and the state will be c.

(2) In state b, if "0" is input, the output is 1 1 and the state is a, and "1" is input Then the output will be 00 and the state will be c.

(3) In state c, if "0" is input, the output changes to 01 and the state changes to b. If "1" is input, the output changes to 10 and the state changes to d.

(2) In state d, if "0" is input, the output will be 10 and the state will be b, and if "1" is input, the output will be 01 and the state will be d.

Using this input / output relationship, the convolutional code of the convolutional encoder of FIG. 9 is represented in a lattice-like form (trellis diagram) as shown in FIG. 13 (A). Here, k means the input time of the k-th bit, and the initial (k = 0) state in the encoder is a (00). Also, the dotted line "0" input and the solid line "gamma indicates the input, two numbers on the line indicates the output (g _0, _gl). Therefore, in the initial state a (00)," the input is 0 " Then, the output becomes 00 and the state becomes a, and when "1" is input, the output becomes 11 and the state becomes c.

Referring to this grid-like representation, if the original data is 11001, the state c is reached via the path shown by the two-dot chain line with the arrow in FIG. 13 (B), and the encoder output is

11 → 10 → 10 → 11- → 11

It turns out that it becomes.

-Decoding of convolutional codes

When decoding a convolutional code, the received data is determined first. There are two types of this decision, a hard decision and a soft decision.

In the case of a hard decision, if the detection output level is greater than 0, it is determined by two quantization levels: “Γ”, and if it is smaller than 0, it is determined by two quantization levels. An error decision occurs at the part where the spread of each probability density function shown in the figure is large (the shaded area in the figure).

The soft decision compensates for the disadvantages of the hard decision.As shown as an example at the left end of Fig. 14, the detection output is quantized at, for example, eight levels, and the certainty is weighted according to each level. The decision result is output to the decoder.

The Viterbi decoding method for decoding convolutional codes can output hard-decision input hard-decision output and soft-decision input hard-decision output. In the hard decision input and the hard decision output, assuming that the hard decision received data (go, gi) is 11 → 10 → 10 → 11 → 11 and is in an ideal state without errors, Fig. 15 ( The path indicated by the two-dot chain line with the arrow in A) is obtained, and by setting the dotted line to "0" and the solid line to "1", the decoding result of 11001 can be obtained as shown in Fig. 17 (B). . However, in practice, received data often contains errors. This opens the interface shown in FIG. 1 5 (C), the fifth error bit is generated, the hard-decided received data (g _0, gi) is assumed to be 11 → 10 → 00 → 11 → 11, k = 2 Since there is no 00 path at the time of, it is unclear which of 10 and 01 should be branched (error count ERR = 1). If the upper path is selected assuming that it is 10, then k = 3, K = 4, and it will reach state c without any hesitation. Therefore, the number of errors ERR = 1 in the dotted line path with arrow is set, and the decoding result at that time is 11001. On the other hand, if k = 2 and the lower path is selected assuming 01, then even at k = 3, it is unclear where to branch, and the total number of errors is ERR = 2. After that, when the path is selected in the same way and the branch is lost, the ERR is counted up and the following result is finally obtained. That is, the total number of errors when the decoding result is 11001 ERR: 1

Total error count when decoding result is 11100 ERR: 2

Total number of errors when decoding result is 11110 ERR: 3

Total number of errors when decoding result is 11111 ERR: 3

Becomes Therefore, the decoding result 11001 having the minimum number of errors ERR is selected and output. In this way, the original data 11001 can be correctly restored even if there is an error in the received data.

The above is hard-decision reception data, but decoding can be similarly performed for soft-decision reception data. FIG. 16 is an explanatory diagram of decoding in the case of soft-decision reception data. As shown in (B), the soft-decision reception data (g ₀ , gi) power 1,1 → 1,0 → 1,0 → 2 Suppose that / 8,1 → 1,1. Referring to the grid representation of (A), at k = 3, it is unclear which branch to 11,00. If 11 is selected and the upper path is selected (error count ERR = 6/8), state c is reached without hesitation at K = 4. Therefore, the number of errors in the path indicated by the two-dot chain line with the arrow is ERR = 6/8, and the decoding result at that time is 11001. On the other hand, if k = 3 and 00 is selected as the lower path (error count ERR = l + 2/8), at k = 4 it is unclear where to branch, and the total error count ERR = (2 + 2/8). After that, if the path is selected in the same way and ERR is counted up when the branch is lost, the following result is finally obtained. That is, Total number of errors when decoding result is 11001 ERR: 6/8

Total error count when decoding result is 11010 ERR: 2 + 2/8

Total error count when decoding result is 11011 ERR: 2 + 2/8

Becomes Therefore, the decoding result 11001 having the minimum number of errors ERR is selected and output. In this way, the original data 11001 can be correctly restored even if there is an error in the received data. As described above, the soft decision has the same principle as the hard decision except that the number of errors ERR is no longer an integer, but improves the ability to determine the number of errors with high accuracy and improves the error correction capability.

• Viterbi decoding method

As described above, the process of obtaining the error count ERR of all possible paths based on the received data and decoding the original data based on the path having the smallest ERR is complicated. Therefore, in the Viterbi decoding method, each of the states a, b, c, d at the time of inputting the k-th data by using the data up to the k-th (k = 1, 2,...) Of the coded data having the information length N Then, a path having a small number of errors is selected from the two paths leading to the state, and a path having a large number of errors is discarded. Thereafter, the state is similarly reached for each state at the time of the final N-th data input. The path with the least error is selected from the two paths, and decoding is performed using the path with the least error among the paths selected in each state.

-ALC (Automatic Level Control) circuit

In mobile communications such as CDMA, the level of received data always fluctuates due to the communication environment, noise, etc. Conventionally, in order to enable high-precision decoding even if such a level fluctuation occurs, conventionally, weighting of demodulated data is performed based on the level to obtain soft decision data, and the soft decision data is input to an error correction decoder. A configuration for performing decoding is adopted. In this case, the number of bits of the soft-decision data A obtained by weighting the demodulated data is larger than the number of bits of the soft-decision data B handled by the error correction decoder. That is, the weighted soft decision data A also finely represents the soft decision data input to the error correction decoder. Therefore, as shown in Fig. 17, an ALC (Automatic Level Contor 1) circuit 3 is provided between the demodulator 1 and the soft decision decoder (erroneous correction decoder) 2, and the bit of the soft decision data A is The number is reduced and converted into soft-decision data B having the number of bits that can be handled by soft-decision decoder 2. In this case, soft decision data B is generated by reducing the number of bits as faithfully as It needs to be passed to the decoder 2, which is conventionally done as follows.

First, the soft decision data A (consisting of a sign bit and an amplitude bit) weighted by the separator 3a is separated into a sign bit and an amplitude bit, and the sign bit is stored in the sign bit memory 3b. The amplitude bit is stored in the data holding memory as 3c and input to the maximum value detector 3d. The maximum value detector 3d detects a maximum value for each error correction code length unit, and notifies the bit selector 3e of the maximum value. When the maximum value detection is completed, the bit selector 3e retrieves the specific amplitude bit so as to make use of the maximum value notified in the data storage stored in the data holding memory 3c, and extracts the sign bit memory. 3 Retrieve and add the corresponding sign bit to b, and output it as soft decision data B.

For example, 16-bit (1 sign bit + 15 amplitude bits) weighted soft decision data A including 1 sign bit

da t a {15..0} = xaaa aaaa aaaa aaaa x: Sign hit, a: 0orl (1) is input to ALC circuit 3. Also, the number n of data that constitutes one error correction code length is 300, and the maximum level of the amplitude represented by 15 bits is a binary number.

data [14..0] = "011 1000 1111 0000" (2)

And

The ALC circuit 3 outputs the soft decision data as nan [4..0] to output the weighted data A and the 5-bit (one sign bit + 4 amplitude bits) soft decision data B. , Nan [4] data [15], nan [3..0] ^ da ta [13..10] (3)

And That is, in the maximum level of (2), the 4 bits (data [13..10]) are assigned the sign (data [15]) and output from the first bit with "1". As a result, unnecessary upper bits and fine lower bits are ignored. Thereafter, the bit selection range is switched based on the maximum value obtained for each error correction code length.

As described above, the switching timing of the bit selection range in the conventional method has an advantage that control can be easily performed by switching at a low speed in units of one error correction code length. However, even though the conventional method scans data in units of error correction code length, the bit selection range is determined only by the maximum value. As a result, in an environment where the reception level fluctuates rapidly, However, the ALC circuit 3 cannot convert the weighted soft-decision data A into appropriate soft-decision data B, and the gain in subsequent error correction decoding is significantly reduced, and the original data cannot be decoded correctly. . For example, if the maximum value is higher than the other reception levels and the average reception level is considerably lower, the bit selection range according to the conventional method (4 bits from the first “1” at the maximum level) Is not appropriate, the upper 2 or 3 bits of the 4 bits of soft decision data B become 0, and only "1" appears in the lower 1 or 2 bits, and the amplitude bits are used effectively. And the gain in error-correction decoding decreases significantly.

Therefore, in the conventional method, the number of soft decision bits must be increased in order to generate optimal soft decision data. However, such a method has a problem that the hardware scale of the erroneous correction decoder is significantly increased.

From the above, it is an object of the present invention to improve the gain in error correction decoding by appropriately converting weighted input data into soft decision data of the number of bits required by the soft decision decoder, and to improve the And a method and apparatus for generating soft decision data for decoding data.

Another object of the present invention is to reduce the re-memory and shorten the overall processing time by mixing the soft decision data generation processing with the dinterleaving processing.

Disclosure of the invention

The soft decision data generator of the present invention converts input data weighted by level into soft decision data having a smaller number of bits than the input data and outputs the soft decision data. That is, the average value calculation unit calculates the level average value of the input data for each error correction code length, and the soft decision data determination unit determines that the soft decision data corresponding to the input data having the level average value is all " It is located at the center of all soft decision data from "0" to all "1", and the input data on both sides of the average value are converted to predetermined soft decision data corresponding to the level and output. More specifically, the variance calculating means calculates the variance of the input data level from the level average value, and the soft decision data determination unit converts the variance of the input data level to the soft decision data of 0 (zero) based on the calculated variance. Set the first input level range to be converted, the second input level range to be converted to all "1" soft decision data, and the first and second level ranges The third level range sandwiched between is equally assigned to all soft decision data except for all "0" and all "1". Then, when the input data arrives, the soft-decision data determining unit converts the input data into soft-decision data corresponding to the level range including the level and outputs the soft-decision data.

In another soft decision data generation device of the present invention, the variance calculation means calculates the variance of the input data level from the level average value, and the soft decision data decision unit determines the two central variances based on the calculated variance. The level range of the soft-decision data, the level range of the two soft-decision data on both sides, and so on, are set in the same way, and the input data is set according to the level range where the level is located. Output as soft decision data. In this case, each level range is made wider as it goes away from the average value.

In this way, since the soft decision data corresponding to the input level is generated with the level average as the center, the amplitude bits can be used effectively, the gain in error correction decoding can be improved, and the original Can decrypt data.

Further, in the soft decision data generating device of the present invention, the storage unit stores the input data, and the soft decision data determination unit reads out the input data one by one from the storage unit after calculating the average value and the variance value, and softly reads the input data. Convert to judgment data and output. In this case, if the input data is interleaved, the data is read out from the storage unit in the order of the interleave and the soft decision data decision unit generates and outputs the soft decision data. By doing so, the process of generating soft decision data is mixed with the deinterleaving process to reduce re-memory and shorten the overall processing time.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic configuration diagram of a wireless device including a soft decision data generation unit (ALC circuit) of the present invention.

FIG. 2 is an explanatory diagram of the principle of generating soft decision data according to the first embodiment.

FIG. 3 is a flowchart for generating soft decision data according to the first embodiment.

FIG. 4 is an explanatory diagram of the principle of generating soft decision data according to the second embodiment.

FIG. 5 is a flowchart for generating soft decision data according to the second embodiment.

FIG. 6 is a configuration diagram of the soft decision data generator (ALC circuit) of the present invention.

FIG. 7 shows a modification of the soft decision data generator (ALC circuit) of FIG. FIG. 8 is another configuration diagram of the soft decision data generator (ALC circuit) of the present invention.

FIG. 9 is an example of a convolutional encoder.

FIG. 10 is a diagram for explaining the input / output relationship of the convolutional encoder and the state of the shift register SFR.

FIG. 11 is an explanatory diagram of the state of the convolutional encoder.

FIG. 12 is a diagram showing the relationship between the state of the convolutional encoder and the input / output.

FIG. 13 is a diagram showing the convolutional codes of the convolutional encoder in a lattice form.

FIG. 14 is an explanatory diagram of hard decision and soft decision.

FIG. 15 is an explanatory diagram of a decoding method for hard decision input and hard decision output.

FIG. 16 is an explanatory diagram of a decoding method of a soft decision input and a hard decision output.

FIG. 17 is an explanatory diagram of a conventional ALC circuit.

BEST MODE FOR CARRYING OUT THE INVENTION

(A) Radio equipment including soft decision data generator (ALC circuit)

FIG. 1 is a schematic configuration diagram of a wireless device including a soft decision data generation unit (ALC circuit) according to the present invention. As the wireless device, for example, there are a mobile phone device and a transmitting device of a CDMA system, a PD system, or the like. In the figure, 21 is an upper layer processing unit, an audio codec (code r / decoder :) for compressing / decoding audio data, 22 is a channel codec, an encoder for convolutional encoding, 2 Reference numeral 3 denotes a modulation unit for modulating a carrier wave to code data, and reference numeral 24 denotes a radio unit. The baseband transmission frequency is up-converted to a high-frequency signal, amplified by a transmission power amplifier, transmitted to a transmission antenna 25, and transmitted. The high-frequency signal received by the antenna 26 is down-converted into a baseband signal and output. 27 is a demodulation unit that demodulates the received data and weights and outputs the received level, and 28 converts the weighted input data A to soft-decision data B that has a smaller number of rebits than the input data. A soft decision data generator (ALC circuit) 29 is a channel codec, which is an error correction decoder such as a Viterbi decoder for error correction decoding of a convolutional code.

(B) First embodiment of soft decision data generation

(a) Explanation of the principle of soft decision data generation in the first embodiment

In the first embodiment of the soft decision data generation, first of all, for every error correction code length, The level average value E and the root mean square value E ₂ are obtained, and the variance V is obtained using these E and E ₂ . If the number of input levels per unit of error correction code length is represented by n and each input level is represented by B bits, the input data D is

D = {d ₀ , d ₁ , d ₂ , ---, <3 „-1}

1, '· η- 1) (4)

Can be expressed as If the soft decision data to be output is represented by Κ bits, the soft decision data U is

Can be expressed as Therefore, the mean Ε, the mean square Ε ₂ and the variance V are

E = (do + d, + d2 +---+ dn- i) / ii (6)

E ₂ = (do ² + d ₁ ² + d2 ² + --- + d _n _ ₁ ² ) / n (7)

V = E ₂ — E ² (8)

Can be obtained.

In the first embodiment, as shown in FIG. 2, soft decision data C m or _cm (K bits) corresponding to input data having an average value E from all bits “1” to all bits “0” so as to be positioned in the center of the 2 ^K-number of the entire soft-decision data, and the first level range R 1 for outputting soft decision data of all bits ^{"1" (2 Β _ 1} ) ~

{E + f (V)}, and the second level range R 2 for outputting soft decision data of all bits “0” is {E−f (V)}} 0. Where f (V) is the truncation function, for example, f (V) = ^ V (9)

This is a monotonically increasing function as defined in Fig. 2. In Fig. 2, K = 4 and ^2K = 2m. In addition to the above, in the first embodiment, the third level range R3 sandwiched between the first and second level ranges is equally allocated to the remaining 2 (m-1) soft decision data. Since the width of the third level range R 3 is 2 · f (V), the level width X of each soft decision data is

Χ = 2f (V) / 2 (m-l)

= f (V) / (m-1) (10)

Is determined. Then, the third level range R 3 is divided into (2m−2) level ranges by X, and 2 (ml) soft decision data _{C l to} c _2m — ₂ are allocated to each. As described above, 2m (= ^2K ) soft decision data C

C = {co, c 1, c ₂ , ..., c ₂ mi} ci = i (i = 0, 1,--2m-l) (11) Then, each soft decision data c ₀ , ci, c ₂ , ···, c _2m -i can be assigned to each level range shown in Fig. 2.

Therefore, the soft decision data can be determined and output depending on which level range the input data level falls within. That is, according to the first embodiment, the B-bit input data level can be converted into K-bit soft-decision data using the average value E and the truncation f (V).

According to the first embodiment, the cutoff level E f (V) for outputting soft decision data of all bits “1” and all bits “0” is varied by the variance V. For example, if the dispersion of the input level from the average E is large, the variance V will be large, and the truncation level will be far from the average E. On the other hand, if the dispersion of the input level from the average E is small, the variance V will be As it gets smaller, the cut-off level approaches the average value E. In this way, the width in which all soft-decision data other than all bits "1" and all bits "0" are represented by K bits is switched according to the variance of the input level. Data can be accurately converted to K-bit soft-decision data and output.

(b) Algorithm for generating soft decision data

The following is an algorithm for generating soft decision data according to the first embodiment, and FIG. 3 is a flowchart of generating soft decision data. In the algorithm, j ++ means increment j.

X = f (V) / (m-l)

if (d, <E)

for (j = 0; j <m-2; j ++)

if (E-XX (j + 1) ku diku E— XX j)

di → C _m -l- j

end i f

end for

end i f

else

ior (j = 0; j <m-2; j ++)

if (E + XX j <di <E + XX (j + 1))

end i f

enf for

end if

end i f

The process of generating soft decision data will be described with reference to FIG. It is assumed that the average value E and the variance V have already been obtained from the η pieces of input data constituting the error correction code length, and that n pieces of input data are stored in the memory.

First,

X = f (V) / (m-l)

Then, the level width X of each (2m−2) pieces of soft decision data included in the third level range R 3 (see FIG. 2) is obtained (step 101). However, 2 ^K = 2 m.

Next, the i-th input data d i (the initial value of 1 is 0) is fetched from the memory (step 102), and j = 0 (step 103).

Then, d i is compared with the average value E (step 104). If d i <E, j is compared with (m−2) (step 105).

E-XX (j + l) <d; <E-XXi (12)

Is checked (step 106). Since j = 0 at first,

Check whether or not holds. That is, the input data di is the region R ' ₃ , in FIG. Check if it is included in.

If equation (12) is satisfied, soft decision data is output (step 107). At first, j = 0, so soft decision data c ^-, (= 0111) is output. If equation (12) does not hold, j is incremented (step 108), and the processing from step 105 onward is repeated. On the other hand, in step 105, if j≥m-2, the input data The soft decision data c ₀ (= 0000) is output assuming that the level exists in the first level range R 1 (step 109).

In step 104, if d E, j is compared with (m−2) (step 110). If j <m−2, the following equation is obtained.

E + XXj <di <E + XX (j + 1) (13)

Is checked (step 1 1 1). Since j = 0 at first,

Check whether or not holds. That is, the input data d is the region R ₃ ,. Check whether it is included.

If the equation (13) holds, the soft decision data _{cm + j} is output (step 1 1 2). First, since j = 0, soft decision data _cm (= 1000) is output. However, if the expression (13) does not hold, j is incremented (step 113), and the processing from step 110 is repeated. On the other hand, in step 110, if j≥m-2, the soft data c (= 1111) is output assuming that the input data level exists in the second level range R2 (step 114).

If the soft decision data is output in steps 107, 109, 111, 114, i is incremented (step 115), and it is checked whether i≥n (step 116). ”, Reads the next data d; from the memory and repeats the subsequent processing. If“ YES ”, outputs soft-decision data of n input data in units of 1 error correction code length. To end.

(c) Another example of the truncation function f (V)

The above is the case of the cut-off function f (V) = V (= V ^1/2 ),

f (V) = kV (14)

A monotonically increasing function such as Here, k is a constant.

(d) Extension of the first embodiment

If the soft decision data generation principle of the first embodiment is extended, B bit data can be converted into K (<B) bit data as follows. That is, a plurality of B-bit input data values that can take 2 ^B (= m) values are divided into 各 -bit output data values that take 2 ^K (= η) values. Data conversion is performed by hitting. this In this case, within a predetermined range from the average value of the input data values, the number of input data values to be assigned to one output data value is equal. The data obtained by this data conversion is thereafter input to a Viterbi decoder and Viterbi decoded.

(C) Second embodiment of soft decision data generation

(a) Explanation of the principle of soft decision data generation in the second embodiment

In the second embodiment, like the first embodiment, the input data sequence of the error correction code length for each (6) to (8) Average value E 2 mean square value E ₂ and the variance V of the input data level by formula Is calculated. Also, 2m (= ^2K ) soft decision data C

C = {co, Ci c ₂ c ₂ m-i} c

(i = 0, 1,--2m-l)

Is expressed as

Next, as shown in FIG. 4, the soft decision data C m or _cm corresponding to the input data having the average value E is obtained from the soft decision data c ₀ of all bits “0” to the soft decision of all bits “1”. It is located at the center of all soft decision data up to data C.

Furthermore, based on the variance V, the center of the two soft decision value c _m c _m -, level range Roo of, Roi, two soft decision value c _m c _m on both sides - ₂ level range R ₁₀ Rn,. , And so on, two soft decision data c _2m c. Set the level range of R _m -ο, R _m- . However, the center level range R ₀₀ R ₀₁ is the 0th level range (i = 0), and the level ranges on both sides R ₁₀ Ru are the first level range (i = l),..., The outermost level range R _{If m} -Rm-,,, is the (ml) level range (i = m-1), the boundary level of the i-th level range RiQ RM is

E ± f (V i) (15)

Is decided. Where f (V i) is a cut-off function that determines the boundary of the i-th level range, and

f (V i) = {(i + l) / (m-l)} 'V (16)

Given by m is if Κ the number of bits of soft decision data, m = 2 ^K - ^1. As the specific value of j, 2 and 3 are preferable. According to this cut-off function f (V, i), the i-th level range becomes wider as the average value E departs.

As described above, each soft decision data CQ _{C 1} C ₂ · · ·, (: can be assigned to each level range shown in Fig. 4. Therefore, the input data level is included in any level range. It is possible to determine and output soft decision data depending on whether or not it is rare.

As described above, according to the second embodiment, the i-th level range is determined based on the average value E and the truncation function f (V, i), and the input data level is included in any level range. Since the soft decision data is determined, the B-bit input data can be faithfully converted to K-bit soft decision data according to the level. In particular, since each level range is varied by the variance V, and the level range is widened as it departs from the average value E, the number of input data included in each level range can be made uniform and the B-bit input data can be reduced. It can be faithfully converted to K-bit soft decision data.

(b) Algorithm for generating soft decision data

The following is an algorithm for generating soft decision data according to the second embodiment, and FIG. 5 is a flowchart for generating soft decision data. In the algorithm, j ++ means to increment j, and f (V, -1) = 0.

if (d <E)

for (j = 0; j <m-2; j ++)

if (E-f (V, j)) <di <E-f (V, j-1))

end if

else

end for

else

for (j = 0; j <m-2; j ++)

if (E + f (V, j-l) <di <E + f (V, j))

end i f

else

end for

end if The process of generating soft decision data will be described with reference to FIG. It is assumed that the average value E and the variance V have already been determined from the n pieces of input data constituting the error correction code length, and that the n pieces of input data are stored in the memory.

First, the number of bits of soft decision data to output if the K bits, and m = 2 ^K one ¹ (step 201). Next, the i-th input data di (the initial value of 1 is 0) is fetched from the memory (step 202), and j = 0 (step 203).

Next, d i is compared with the average value E (step 204). If d i d E, j is compared with (m−2) (step 205). If j m m−2, the following equation is obtained.

E-f (V, j) <di <E-f (V, j-1) (17)

Is checked (step 206). Since j = 0 at first,

E-f (V, 0) <d, <E-f (V, -l) f (V, -1) = 0 (18)

Check whether or not holds. That is, the input data di is the region R _{0 in} FIG. Check whether it is included.

If satisfied (17) is soft decision data c _m - "outputs a (step 207). First, since j = 0, soft decision data c ™ —, (= 0111) is output. However, if the expression (17) is not satisfied, j is incremented (step 208), and the processing after step 205 is repeated. On the other hand, in step 205, if it is 2, the input data level is assumed to be within the level range R _m _, and the soft decision data c is determined. (= 0000) is output (step 209).

In step 204, if d i≥E, j is compared with (m−2) (step 2 10). If j <m−2, the following equation is obtained.

E + f (V, j-lXdi <E + f (V, j) (19)

It is checked whether or not is satisfied (step 2 1 1). Since j = 0 at first,

E + f (V, -l) <d, <E + f (V, 0) f (V, -1) = 0 (20)

Check whether or not holds. That is, the input data di is checked whether included in the region R ₀₀ of FIG.

If the equation (19) holds, the soft decision data _{cm +} j is output (step 2 1 2). First, since j = 0, soft decision data _cm (= 1000) is output. However, if equation (19) does not hold, -j is incremented (step 2 13), and Repeat the process. On the other hand, in step 210, if j≥m-2, the input data level is the level range. Then, soft decision data c (= 1111) is output as existing in (step 214).

If the soft decision data is output in steps 207, 209, 212, and 214, i is incremented (step 215), then it is checked whether it is in (step 216), and if "N0", the next data d is read from the memory, and the subsequent processing is repeated. If “YES”, the soft decision data output of n input data of one error correction code length unit is terminated.

(c) Another example of the truncation function f (V, i)

In the above, the truncation function f (V, i) is

f (V, i) = {(i + l) / (m-1)} ^j · ^ TV

Is given by

f (V) = {(i + l) / (m-l)} 'k'V (21)

It can also be. Here, k is a constant.

(d) Extension of the second embodiment

If the soft decision data generation principle of the second embodiment is extended, B bit data can be converted into K (<B) bit data as follows. That is, multiple values of B-bit input data that can take 2 ^B (= m) values are divided into each value of Κ-bit output data that can take 2 ^K (= η) values. Data conversion by hitting. In this case, within a predetermined range from the average value of the input data values, the number of input data values allocated to one value of the output data increases the difference between the input data value and the average value. According to, the assignment is made so as to increase monotonically. The data obtained by this data conversion is thereafter input to a Viterbi decoder and Viterbi decoded.

(D) Configuration of soft decision data generator (ALC circuit)

FIG. 6 is a block diagram of the soft decision data generator (ALC circuit) 28 of the present invention. It is assumed that input data is interleaved on the data transmitting side.

The average value calculation unit 51 calculates the average value の of η pieces of input data constituting one error correction code length according to equation (6), and the variance calculation unit 52 calculates the variance V as (7), (8 ) Equation is calculated. The first and second memories 53a and 53b for ALC are composed of n error correction code lengths. The input data is alternately stored, and when one is written, the other is read and input to the soft decision data determination unit 54. The soft decision data decision unit 54 uses the average value E, the variance V, and n pieces of input data constituting one error correction code length stored in the first and second memories 53a and 53b. Then, the soft decision data is determined and output according to the processing flow of FIG. 3 or FIG. The first and second dinterleave memories 55a and 55b alternately store n pieces of soft-decision data constituting one error correction code length output from the soft-decision data generator 28, and When the data is written to the other side, the soft decision data is read out in the order of the reinterleaving, rearranged in the order before interleaving, and input to the error correction decoder (soft decision Viterbi decoder, etc.) 56. The error correction decoder 56 performs a decoding process on the input data and outputs the result.

Briefly explaining the operation, the first memory 53 a stores n input data constituting the first one error correction code length, the average value calculation unit 51 calculates the average value E, The variance calculation unit 52 calculates the variance V.

Thereafter, the soft-decision data determining unit 54 sequentially uses the average value E and the variance V to sequentially input n pieces of input data constituting one error correction code length stored in the first memory 53 a. The data is converted into soft decision data and written to the next stage memory 55a. In parallel with the above, the second memory 53b stores n input data constituting the following one error correction code length, and the average value calculation unit 51 calculates the average value E, The calculation unit 52 calculates the variance V.

Next, the soft-decision data determination unit 54 configures the 1 error correction code length stored in the second memory 53 b using the average value E and the variance V of the second 1 error correction code length. The n input data are sequentially converted to soft decision data and written to the next-stage ding leave memory 55b. In parallel with the above, the first memory 53 a stores the n input data constituting the third 1 error correction code length, and the average calculation unit 51 calculates the average E. The variance calculation unit 52 calculates the variance V. In addition, the interleave 55 a reads out the soft decision data in the order of the interleave, rearranges the data in the order before interleaving, inputs the data to the error correction decoder (soft decision decoder) 56, and outputs the error correction decoder 56. The input data is decrypted and output. Thereafter, the read / write of the memos 53a, 53b and the memos 55a, 55b are alternately switched for each error correction code length. And repeat the above operation.

The soft decision data generator 28 can be realized using a DSP (digital signal processor) or using a microcomputer.

Fig. 6 is applicable to the case where the input data is all positive, for example, but if the input data is positive or negative signed, as shown in Fig. 8, the separator 57 and the sign bit memories 58 a and 58 b are used. Provide.

Separator 57 separates the input data into code bits and amplitude bits, and code bit memories 58a and 58b store the code bits alternately for each error correction code length. It is read out and input to the soft decision data decision unit 54.

The average value calculation unit 51 calculates the average value E of n pieces of amplitude data constituting one error correction code length according to equation (6), and the variance calculation unit 52 calculates the variance V as (7), ( 8) Calculate using equation. The first and second memories 53a and 53b for ALC alternately store n pieces of amplitude data constituting one error correction code length, and when writing to one, read from the other to make a soft decision data decision section. Enter 54.

The soft decision data determination unit 54 uses the average value E, the variance V, and n pieces of amplitude data constituting one error correction code length stored in the first and second memories 53a and 53b, The soft decision amplitude data is determined according to the processing flow of FIG. 3 or FIG. 5, and the corresponding sign bit is retrieved from the sign bit memories 58a and 58b and added to the amplitude data to produce soft decision data. Output.

(E) Alternative configuration of soft decision data generator (ALC circuit)

FIG. 8 is another block diagram of the soft decision data generator (ALC circuit) 28 of the present invention, and the same parts as those of the soft decision data generator of FIG. The difference is

(1) Dintale memos 55a and 55b are deleted, and the first and second memories 53a and 53b are used for both ALC and Dental leave.

(2) A deinterleave address controller 61 that generates addresses in deinterleave order is provided.

(3) First and second memories 53a, 53b Read input data in the order of de-interleaving, rearrange the input data in the order before interleaving, and enter the soft-decision data determining unit 54. That is, the soft decision data is directly input to the error correction decoder 56.

Since the ding leave processing and the soft decision data generation processing can be performed by using the mittas, the memory can be reduced, and the processing delay required for the ding leave can be reduced as compared with the embodiment of FIG.

FIG. 8 is applicable to the case where all the input data is positive. However, if the input data is positive or negative, a separator and a sign bit memory are provided as in FIG.

(F) Effect

By using the soft decision data generator (ALC circuit) of the present invention, even in an environment where the reception level fluctuates rapidly, the reception level can be established. It can be easily converted to statistical soft decision data, and error correction can be performed later. A high gain can be obtained in decoding, and communication quality can be improved.

In addition, the soft decision data generator (ALC circuit) of the present invention can output more optimal soft decision than before, so that the number of soft decision bits in error correction decoding can be reduced to obtain the same gain. And the hardware scale of the receiver can be reduced. In addition, since the soft decision data generation processing and the deinterleaving processing can be performed in a mixed manner, the overall processing time can be reduced.

As described above, the present invention has been described with reference to the embodiments. However, the present invention can be variously modified in accordance with the gist of the present invention described in the claims, and the present invention does not exclude these.

Claims

The scope of the claims

1. A soft-decision data generator that converts input data weighted by level into soft-decision data having a small number of bits of the input data and outputs the soft-decision data,

An average calculator for calculating an average level E of input data for each error correction code length; soft-decision data corresponding to input data having the average level is converted from all bits “1” to all bits “0”; A soft-decision data deciding unit that converts the input data having levels on both sides of the average value into soft-decision data corresponding to the level and outputs the data,

A soft decision data generator comprising:

2. The soft decision data generator according to claim 1,

The apparatus further includes a variance calculating unit that calculates a variance V indicating a degree of dispersion of the input data level from the average value,

The soft-decision data determining unit converts the input data into soft-decision data of all bits “1” based on the calculated variance, and converts the input data to soft-decision data of all bits “0”. Set the second input level range to be converted to, and set the third level range sandwiched by the first and second level ranges to all soft bits except for all bits "1" and all bits "0". Assigning evenly to the data, converting the input data to soft-decision data according to the level range that includes that level, and outputting

A soft decision data generator characterized by the above-mentioned.

3. The soft decision data generator according to claim 2,

The soft decision data determination unit sets the boundary level between the first and third level ranges to (E- ^ V), and sets the boundary level between the second and third level ranges to (E + V). A soft decision data generator.

4. The soft decision data generator according to claim 2,

The soft decision data determination unit sets the boundary level of the first and third level ranges to (Ek−V) (where k is a constant), and sets the boundary level of the second and third level ranges to (Ek−V). + k · V), a soft decision data generator.

5. The soft decision data generator according to claim 1,

The apparatus further calculates a variance, which indicates a scatter of the input data level from the average value. Variance calculation means to output

Based on the calculated variance, the soft-decision data determining unit determines the level range of the center two soft-decision data, the level range of the two soft-decision data on both sides, and so on. Setting a range, converting the input data to soft-decision data according to the level range that includes that level, and outputting it.

A soft decision data generator characterized by the above-mentioned.

6. The soft decision data generator according to claim 5,

Widening the level range away from the average value,

A soft decision data generator characterized by the above-mentioned.

7. The soft decision data generator according to claim 6,

When the central level range is the 0th level range, the level ranges on both sides thereof are the first level range, and the level ranges on both sides of the jth level range are generally the (j + 1) th level range, the soft decision data is determined. The unit calculates the boundary level of the j-th level range as

E soil {(j + l) / (m— 1)} ^J · V

To be determined based on

A soft decision data generator characterized by the above-mentioned.

8. The soft decision data generator according to claim 6,

When the center level range is the 0th level range, the level ranges on both sides are the 1st level range, and the level ranges on both sides of the jth level range are the (j + 1) th level range in general.

The soft decision data determination unit calculates the boundary level of the j-th level range as

E ± {(j + l) / (m-1)} ^j · k · V

(Where E is the average level, and m is the soft-decision data amplitude in K bits, m = 2 ^K —

A soft decision data generator characterized by the above-mentioned.

9. The soft-decision data generation device according to claim 2 or 5, further comprising a storage unit for storing input data,

The soft decision data determining unit reads out the input data one by one from the storage unit after calculating the average value and the variance, converts the input data into soft decision data, and outputs the data.

Characteristic soft decision data generator.

10. The soft decision data generator according to claim 9,

The apparatus further comprises a dint-leaved address generating unit for generating addresses in dint-leaving order,

The storage unit stores the interleaved input data,

A dinterly bad address generation unit that generates addresses in a dint leave order, and inputs input data specified by the address to the soft decision data determination unit;

A soft-decision data generator.

1 1. In a method of outputting input data weighted according to level as soft decision data with a smaller number of rebits than the input data,

Calculate the average value of the input data level for each error correction code length, and calculate the variance that indicates the degree of variance of the input data level from the average value,

The soft decision data of the input data having the level average value is located at the center of all the soft decision data from all bits “1” to all bits “0”, and the input data is determined based on the variance. A first input level range for converting soft-decision data of all bits "1" and a second input level range for converting input data to soft-decision data of all bits "0" are set. The third level range sandwiched between the first and second level ranges is equally allocated to all soft decision data except for all bits "1" and all bits "0", and the input data includes that level. To convert to soft decision data according to the level range

A soft-decision data generating method, characterized in that:

1 2. In the method of outputting soft decision input data weighted according to the level as soft decision data with a smaller number of rebits than the input data,

The soft decision data of the input data having the level average value is located at the center of all soft decision data from all bits “1” to all bits “0”, and based on the calculated variance. , The level range of the two soft decision data at the center, the level range of the two soft decision data on both sides, and so on. ,

Converting the input data into soft-decision data corresponding to the level range in which the level is included, and outputting the data;

A soft decision data generator characterized by the above-mentioned.

13. The method for generating soft decision data according to claim 12.

Widening the level range away from the average value,

A soft decision data generation method characterized by the above-mentioned.

14. Data conversion that performs data conversion by assigning multiple values of input data that can take m values to each value of output data that can take n (m> n) values In the device,

Adjusting means for allocating so that the number of input data values to be assigned to one output data value is equal to at least a predetermined range from the average value of the input data values;

A data conversion device comprising:

15. In a data conversion device that performs data conversion by assigning a plurality of values of input data that can take m values to each value of output data that can take n (m> n) values ,

In at least a predetermined range from the average value of the input data values, the number of input data values assigned to one value of the output data increases as the difference between the input data value and the average value increases. A data conversion device characterized by comprising adjustment means for performing allocation so as to monotonically increase.

16. The data converter according to claim 14 or claim 15, wherein data excluding positive and negative signs is used as the input data.

1 7. In a Viterbi decoding device that receives data weighted to a level and performs Viterbi decoding using the received data,

An average value calculation unit for calculating a level average value of received data,

The soft decision data corresponding to the received data having the level average value is converted so as to be located at the center of all the soft decision data from all bits “1” to all bits “0”, and A soft decision data determining unit that converts received data having levels on both sides of the average value into soft decision data corresponding to the level and outputs the soft decision data;

A Viterbi decoder that performs Viterbi decoding using the soft decision data,

A Viterbi decoding device comprising:

18. A Viterbi decoding device that receives level-weighted data and performs Viterbi decoding using the received data

When data conversion is performed by assigning a plurality of values of received data that can take m values to each value of output data that can take n (m> n) values, Adjusting means for allocating, in at least a predetermined range from the average value, the number of received data values to be converted into one value of the output data,

A Viterbi decoder that performs Viterbi decoding using the converted data,

A Viterbi decoding device comprising:

1 9. In a Viterbi decoding device that receives data weighted to a level and performs Viterbi decoding using the received data,

When re-data conversion is performed by assigning a plurality of values of received data that can take m values to each value of output data that can take n (m> n) values, In at least a predetermined range from the average value of the data values, the number of input data values converted to one value of the output data increases as the difference between the received data value and the average value increases. Adjustment means for reallocating so as to increase monotonically,

A Viterbi decoder that performs Viterbi decoding using the converted data,

A Viterbi decoding device comprising:

20. In a mobile phone device that receives data weighted to a level and performs Viterbi decoding using the received data,

The soft decision data corresponding to the received data having the level average value is converted so as to be located at the center of all the soft decision data from all bits “1” to all bits “0”. A soft-decision data determining unit that converts received data having a level of

A Viterbi decoder that performs Viterbi decoding using the soft decision data, A mobile phone device comprising:

2 1. In a mobile phone device that receives data weighted by a level and performs Viterbi decoding using the received data,

When performing data conversion by assigning a plurality of values of received data that can obtain m values to each value of output data that can obtain n (m> n) values, Adjusting means for allocating so that the number of values of received data converted to one value of output data is equal to at least a predetermined range from the average value of the values;

A Viterbi decoder that performs Viterbi decoding using the converted data,

A mobile phone device comprising:

2 2. In a mobile phone device that receives data weighted by level and performs Viterbi decoding using the received data,

When performing data conversion by assigning a plurality of values of received data that can take m values to each value of output data that can take n (m> n) values, In at least a predetermined range from the average value of the input data values, the number of input data values converted to one output data value increases as the difference between the received data value and the average value increases. Adjustment means for reallocating so as to increase monotonically,

A Viterbi decoder that performs Viterbi decoding using the converted data,

A mobile phone device comprising: