CN101305575A - Method and apparatus for normalizing input metrics to a channel decoder in a wireless communication system - Google Patents

Abstract

Provided are an apparatus and method for normalizing soft metrics input to a channel decoder in a wireless communication system. The demapper uses the in-phase (X _k ) and quadrature (Y _{k ) components of the received modulation symbol (R k )} , the channel fading coefficient (g _k ) and the constant value (c) to generate soft metrics. The normalizer receives the soft metric, calculates a normalized log-likelihood ratio (LLR) by multiplying the soft metric by the ratio of the constant value to the value of the noise variable, and transforms the normalized LLR to the desired range and desired number of bits, and outputs the input LLR of the channel decoder.

Description

Method and apparatus for normalizing input metrics to a channel decoder in a wireless communication system

technical field

The present invention generally relates to wireless communication systems. More specifically, the present invention relates to methods and apparatus for normalizing input metrics to a channel decoder.

Background technique

Code Division Multiple Access 2000 (CDMA 2000), Wideband CDMA (WCDMA), and Institute of Electrical and Electronics Engineers (IEEE) 802.16 systems perform quadrature phase-shift keying (QPSK), 8PSK, 16-quadrature amplitude modulation (16-QAM), 64 quadrature amplitude modulation (64-QAM) and other modulation methods. Furthermore, these systems perform Adaptive Modulation and Coding (AMC) in conjunction with channel codes such as turbo codes. The system obtains an optimal transmission rate suitable for channel conditions. The receiving stage uses a demapper (demapper) to calculate the logarithmic likelihood ratio (LLR) of each bit according to various modulation methods, and obtains the input metric to the channel decoder. The channel decoder receives and decodes the metrics.

FIG. 1 shows the structure of a transceiver in a conventional wireless communication system.

Referring to FIG. 1, binary data i(n) to be transmitted is encoded in a channel encoder 110 within a transmitter 100. Referring to FIG. Channel encoder 110 generates a series of binary code symbols c(n). Mapper 120 generates blocks of several code symbols of the generated code symbols, performs mapping to a point on the signal constellation, and transforms into complex-valued modulation symbols x(n). Modulation symbols x(n) are applied to modulator 130 . The modulator 130 generates a time continuous wave in a code division multiple access (CDMA) or orthogonal frequency division multiplexing (OFDM) scheme according to the modulation symbol x(n), and transmits the generated wave to the receiver 150 through the channel 140 .

In receiver 150, demodulator/channel estimator 160 performs baseband demodulation and channel estimation processing on the received signal. The demodulator can be implemented according to various technologies. For example, the demodulator may be an OFDM demodulator implemented with a CDMA rake receiver or an Inverse Fast Fourier Transform (IFFT) processor and channel estimator. After baseband demodulation, the channel estimate c(n) and the received symbols y(n) modulated by QAM or PSK are obtained.

The demapper 170 uses the received symbols y(n) and the channel estimate c(n) to compute a measure of the bits making up the codeword of the channel code. The sequence Λ(n) corresponding to the metric values calculated in the demapper 170 is input into the channel decoder 180 and decoded into originally transmitted binary data. When the channel decoder 180 completes the decoding operation, the receiver 150 completes basic operations in the physical layer. At this time, the channel decoder 180 may use a Viterbi decoder for convolutional codes, a Soft Output Viterbi Algorithm (SOVA) iterative decoder for turbo codes, a logarithmic maximum a posteriori probability (MAP) iterative decoder, and a maximum pair Several MAP iterative decoders, etc.

In implementing a conventional wireless communication system that works as described above, the dynamic range of the metrics input to the decoder is not limited when floating point operations are performed in a conventional manner. However, when implementing hardware for performing fixed-point arithmetic, it is affected by quantization noise, clipping noise, and the like in terms of dynamic range. Therefore, every step of the communication system should ensure optimal performance with minimal hardware by performing normalization operations appropriate to the metric representation. However, since the conventional method does not consider the normalization of the metrics computed in the demapper, there is a problem: the performance of high coding rates and high-order modulations is lower than that of conventional coding rates and modulations.

Contents of the invention

Accordingly, certain exemplary embodiments of the present invention address the above and other problems of the prior art. Exemplary aspects of the present invention provide a method and apparatus capable of obtaining optimal performance using a log-likelihood ratio (LLR) of a small number of bits in channel decoding in a wireless communication system.

Furthermore, exemplary embodiments of the present invention provide a method and apparatus capable of improving decoding performance with a small number of bits by normalizing a metric used as an input of a channel decoder in a wireless communication system.

Also, exemplary embodiments of the present invention provide a method and apparatus capable of properly normalizing a metric used as an input of a channel decoder according to a modulation order and a noise level of a current state in a wireless communication system.

In addition, exemplary embodiments of the present invention provide a method that can use information on modulation order, channel coding rate, and channel when information on noise variance used as an input to a channel decoder is lacking in a wireless communication system. Methods and apparatus for encoding frame length information to perform proper normalization.

According to an exemplary aspect of the present invention, an apparatus for normalizing soft metrics input to a channel decoder in a wireless communication system is provided. In an exemplary implementation, the apparatus includes a demapper for using in-phase (X _k ) and quadrature (Y _k ) components of received modulation symbols (R _k ), channel fading coefficients (g _k ) and A soft metric is generated by a constant value (c) defined by the modulation order of the received modulation symbol; and a normalizer is used to receive the soft metric by multiplying the soft metric by the constant value and the value of the noise variable Ratio to calculate the normalized log-likelihood ratio (LLR), transform the normalized LLR to the desired range and the desired number of bits, and output the input LLR of the channel decoder.

According to another exemplary aspect of the present invention, an apparatus for normalizing soft metrics input to a channel decoder in a wireless communication system is provided. In an exemplary implementation, the apparatus includes a demapper for using in-phase (X _k ) and quadrature (Y _k ) components of received modulation symbols (R _k ), channel fading coefficients (g _k ) and A soft metric is generated by a constant value (c) defined by the modulation order of the received modulation symbol; and a normalizer for receiving the soft metric by multiplying the soft metric by an adaptive modulation and coding (AMC ) information to calculate the normalized log-likelihood ratio (LLR), transform the normalized LLR to the desired range and the desired number of bits, and output the input LLR of the channel decoder.

According to still another exemplary aspect of the present invention, there is provided a method of normalizing soft metrics input to a channel decoder in a wireless communication system. In an exemplary implementation, the method includes: using the in-phase component (X _k ) and the quadrature component (Y _k ) of the received modulation symbol (R _k ), the channel fading coefficient (g _k ) and the received modulation symbol The soft metric is generated by a constant value (c) defined by the modulation order of ; the soft metric is received and the normalized log-likelihood ratio ( LLR); and transform the normalized LLR to the desired range and the desired number of bits, and output the input LLR of the channel decoder.

According to another exemplary aspect of the present invention, there is provided a method of normalizing soft metrics input to a channel decoder in a wireless communication system. In an exemplary implementation, the method includes: using the in-phase component (X _k ) and the quadrature component (Y _k ) of the received modulation symbol (R _k ), the channel fading coefficient (g _k ) and the received modulation symbol The soft metric is generated by a constant value (c) defined by the modulation order of ; the soft metric is received and the normalization is computed by multiplying the soft metric by a normalization coefficient computed from Adaptive Modulation and Coding (AMC) information and transforming the normalized LLR to the desired range and the desired number of bits, and outputting the input LLR of the channel decoder.

Description of drawings

The above and other features and advantages of the present invention will become more comprehensible through the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same drawing reference numerals will be understood to refer to the same elements, features and structures, in:

Fig. 1 shows the structure of the transceiver in the traditional wireless communication system;

FIG. 2 shows the structure of a transceiver to which an input metric normalizer is applied according to a first exemplary embodiment of the present invention;

Figure 3A shows a Quadrature Phase Shift Keying (QPSK) constellation and mapping;

Figure 3B shows a 16-Quadrature Amplitude Modulation (16-QAM) constellation and mapping;

Figure 3C shows a 64 quadrature amplitude modulation (64-QAM) constellation and mapping;

Figure 4 shows an example of calculating soft metrics;

Fig. 5 shows the working structure of the input metric normalizer according to the first exemplary embodiment of the present invention;

FIG. 6 shows another working structure of the input metric normalizer according to the first exemplary embodiment of the present invention;

Figure 7 shows the frame error rate (FER) performance of an additive white Gaussian noise (AWGN) channel;

FIG. 8 shows the structure of a transceiver to which an input metric normalizer is applied according to a second exemplary embodiment of the present invention;

FIG. 9 shows the working structure of an input metric normalizer according to a second exemplary embodiment of the present invention;

FIG. 10 shows another working structure of the input metric normalizer according to the second exemplary embodiment of the present invention;

Fig. 11 shows another working structure of the input metric normalizer according to the second exemplary embodiment of the present invention;

Fig. 12 shows the performance of the convolutional turbo decoder of the 6-bit input metric applied to the metric normalizer according to the first and second exemplary embodiments of the present invention; and

Fig. 13 shows the performance of a convolutional turbo decoder for a 6-bit input metric applied to the metric normalizer according to the first and second exemplary embodiments of the present invention.

Detailed ways

The working principles of the exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the following description, detailed descriptions of functions and structures incorporated herein that are known to those skilled in the art will be omitted for conciseness and clarity. It should be understood that the phraseology and terminology employed herein are for the purpose of description only and should not be regarded as limiting of the present invention.

Exemplary embodiments of the present invention provide a method and apparatus for obtaining optimal decoding performance using a log-likelihood ratio (LLR) metric with a small number of bits when encoding a channel. Certain exemplary embodiments of the present invention enable improved decoding performance with a small number of bits by normalizing the input metrics of the channel decoder.

<First Exemplary Embodiment>

A first exemplary embodiment of the present invention provides a structure and an operation procedure for performing normalization using information on noise variables used as an input of a channel decoder.

FIG. 2 shows the structure of a wireless communication transceiver to which an input metric normalizer is applied according to a first exemplary embodiment of the present invention.

Referring to FIG. 2, binary data i(n) to be transmitted is encoded in a channel encoder 210 within the transmitter 200. Referring to FIG. Channel encoder 210 generates a series of binary code symbols c(n). A mapper 220 generates blocks of several code symbols of the generated code symbols, mapped to a point on the signal constellation, and transformed into complex-valued modulation symbols x(n). The sequence x(n) is applied to modulator 230 . The modulator 230 generates a time continuous wave in a Code Division Multiple Access (CDMA) or Orthogonal Frequency Division Multiplexing (OFDM) scheme according to the symbol, and transmits the generated wave to the receiver 250 through the channel 240 .

In receiver 250 , demodulator/channel estimator 260 performs baseband demodulation and channel estimation processing on the signal passing through channel 240 . The demodulator can be implemented according to various technologies applied to baseband. For example, the demodulator may be an OFDM demodulator implemented with a CDMA rake receiver or an Inverse Fast Fourier Transform (IFFT) processor and channel estimator.

In an exemplary embodiment of the present invention, Institute of Electrical and Electronics Engineers (IEEE) 802.16e and Orthogonal Frequency Division Multiple Access (OFDMA) systems will be mainly described. After demodulator/channel estimator 260 has completed baseband demodulation, the received symbols and channel estimates are output into noise variable estimator 265 and demapper 270 . The noise variable estimator 265 estimates a noise variable value σ _n ² from channel estimation using various algorithms, and outputs the estimated noise variable value to the LLR normalizer 275 .

Demapper 270 receives channel estimate c(n) and received symbols y(n) modulated by quadrature amplitude modulation (QAM) or phase shift keying (PSK) from demodulator/channel estimator 260, and The metrics per bit are output by demapping. Demapper 270 can use various algorithms to obtain this metric. Demapping methods traditionally use simplified algorithms that are close to optimal. One of the various algorithms is described in reference 1 (Y.Xu, H.-J.Su, E.Geraniotis, "Pilot symbol assisted QAM with interleaved filtering and turbo decoding over Rayleigh flat-fading channel, "inProc.MILCOM '99 , pp.86-91), the disclosure of which is incorporated herein by reference.

The IEEE 802.16e system uses higher order modulation of 16 quadrature amplitude modulation (16-QAM) or 64 quadrature amplitude modulation (64-QAM). The modulated transmitted signal may be distorted due to channel fading and noise. Since the convolutional turbo decoder serving as the channel decoder 280 in the receiver 250 of the IEEE 802.16 system receives and decodes the soft metric corresponding to the reliability information of each bit, it is necessary to receive from the distortion at the previous stage of the channel decoder 280. The process of computing soft metrics in the incoming signal. This process is performed by the demapper 270 in the receiver 250 . Now, a demapping algorithm applied to the present invention will be described.

The IEEE 802.16 system uses a modulation method of Quadrature Phase Shift Keying (QPSK), 16QAM, or 64QAM. When the number of bits representing one modulation symbol in the output sequence of the binary channel encoder is m, the number of signal points in the constellation is M=2 ^m , where m=2, 4, 6 and so on. The m bits are mapped to a specific signal point of signal points. When an equation is used to express the M-QAM mapping, the in-phase and quadrature components of the modulation symbol can be obtained from m binary symbols as shown in Equation (1).

${\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; QAM</span>}}{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In equation (1), s _{k i} (i=0, 1, ..., m-1) is the i-th symbol of the output sequence of the binary channel encoder mapped to the k-th signal point, x _k and y _k are the in-phase component and quadrature component of the kth signal point, respectively. In the case of 16QAM, m=4.

3A to 3C show a QPSK constellation, a 16QAM constellation and a 64QAM constellation, respectively.

As can be seen from Figures 3A to 3C, x _k of the symbol to be modulated is determined by s _{k m-1} , s _{k, m-2} , ..., s _{k m/2} , and y _k is determined by s _{k m/2 1} ,..., s _{k 0} to determine. A constant c capable of determining each constellation point is defined by equation (2). This is the value used to set the symbol's average energy to 1.

${\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0.70711</span>}$

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="16"\; label="c"\; filenames="A20068004211400321.png,A20068004211400351.png"\; state="\{\{state\}\}">c</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 16</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 10</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0.3162</span>}$

${\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 64</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 42</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0.1543</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Here, c ₄ is the reference value of QPSK, c ₁₆ is the reference value of 16QAM, and c ₆₄ is the reference value of 64QAM. The modulated symbols have complex values of x _k + jy _k . After the modulated symbols pass through the channel 240 and the baseband demodulator 260 , the signal shown in equation (3) is input into the demapper 270 .

R _k ＝g _k (x _k +jy _k )+n _xk +jn _yk ......(3)

＝ _XK + _jYK

Here, g _k is a channel fading coefficient and expressed as g _k =g _xk +jg _yk ·n _xk and _nyk are noise and interference components. The log-likelihood ratio (LLR) of the bit symbols _sk,i corresponding to the elements of the QAM symbol can be approximated as shown in Equation (4).

$\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; log</span>}\frac{\mathrm{<span\; class="notranslate">\; PR</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; \}</span>}{\mathrm{<span\; class="notranslate">\; PR</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; \}</span>}$

$<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; log</span>}\frac{\underset{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \{</span>\frac{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \}</span>}{\underset{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \{</span>\frac{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \}</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; \&ap;</span>\mathrm{<span\; class="notranslate">\; log</span>}\frac{\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \{</span>\frac{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\mathrm{<span\; class="notranslate">\; min</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \}</span>}{\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \{</span>\frac{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\mathrm{<span\; class="notranslate">\; min</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \}</span>}$

$<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; min</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ]</span>$

Here, z _k (s _k,i =0) is a changed constellation point calculated by multiplying the symbol of s _k,i =0 by a fading constant g _k , and σ _n ² is a noise and interference variable.

In Equation (4), the log maximum a posteriori probability (MAP) scheme is applied to compute the LLR, and a highly reliable estimate can be obtained with a small amount of computation. Equation (4) can be approximated as shown in Equation (5).

$\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; [</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; no</span>}}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Here, nk _,i is the i-th information bit value mapped to the constellation point close to the received symbol R _k , and _nk,i is the negation of nk _,i . The bit symbols _{sk, i} constituting the QPSK, 16QAM and 64QAM symbols are respectively related to only one of the in-phase and quadrature components of the received symbol. Regarding R _k and z _k of equation (5), one of the x and y axis components is estimated from s _k,i .

FIG. 4 shows an example of calculating LLRs when g _k is a real value.

Assuming that R _k has been received, the LLR of s ₃ can be defined by equation (6) as shown in FIG. 4 .

$\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; [</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="16"\; label="g\; k\; c"\; filenames="A20068004211400321.png,A20068004211400351.png"\; state="\{\{state\}\}">g</figure-callout></span><figure-callout\; id="16"\; label="g\; k\; c"\; filenames="A20068004211400321.png,A20068004211400351.png"\; state="\{\{state\}\}">\; </figure-callout>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{}_{}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 16</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="16"\; label="g\; k\; c"\; filenames="A20068004211400321.png,A20068004211400351.png"\; state="\{\{state\}\}">g</figure-callout></span><figure-callout\; id="16"\; label="g\; k\; c"\; filenames="A20068004211400321.png,A20068004211400351.png"\; state="\{\{state\}\}">\; </figure-callout>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{}_{}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 16</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 4</span>}{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="16"\; label="c"\; filenames="A20068004211400321.png,A20068004211400351.png"\; state="\{\{state\}\}">c</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 16</span>}}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 16</span>}}<span\; class="notranslate">\; )</span>$

并且括号中的部分是对于输入信号的线性等式。在示范性实施方式中，能够利用软度量产生器(SMG)的线性函数来实现解映射器。在包括衰落系数g_k的常数输入到SMG中时，能够以合适的定标(scale)方法来处理该常数。假定用于通过从LLR中估算系数来产生软度量的函数为SMG(a，b)，则用于计算LLR的等式(6)可以被改写为等式(7)。When calculating the LLR using the method shown in equation (6), there are coefficients in each case

And the part in parentheses is the linear equation for the input signal. In an exemplary embodiment, the demapper can be implemented using a linear function of a soft metric generator (SMG). When a constant comprising the fading coefficient g _k is input into the SMG, the constant can be processed in a suitable scaling method. Assumed to be used to estimate the coefficients from the LLR by The function to generate the soft metric is SMG(a, b), then Equation (6) for calculating LLR can be rewritten as Equation (7).

$\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; c</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\mathrm{<span\; class="notranslate">\; SM</span>}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; c</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>$

Equation (7) shows the LLR calculation with respect to the in-phase component X _k only. Of course, the LLR calculation related only to the orthogonal component Y _k can use |g _k | ² Y _k instead of |g _k | ² X _k .

The inputs of the SMG are |g _k | ² X _k , |g _k | ² Y _k and |g _k | ² c, where g _k is obtained by channel estimation. Thus, the input to the SMG can be easily calculated from the received symbols and the channel estimate.

When g _k is a complex number, the input of the SMG mapped to the in-phase signal component and the quadrature signal component can be defined as shown in equation (8).

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; xk</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; yk</span>}}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; xk</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; yk</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; xk</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; yk</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}$

That is, the input to the SMG in Equation (8) can be easily calculated from the received signal using Equation (9).

(X _k g _xk +Y _k g _yk ，|g _k | ² c)＝(I _k ，a _k )

(Y _k g _xk -Y _k g _yk ，|g _k | ² c)＝(Q _k ，a _k )......(9)

从而使得在产生软输出之后执行归一化，并且量化的LLR具有合适的范围和分辨率。Coefficients between the outputs of the SMG attached to the In , the value 4 is a common coefficient for QPSK, 16QAM and 64QAM, thus reflecting quantization. set up

Thus normalization is performed after soft output is generated, and the quantized LLRs have proper range and resolution.

The metric to be computed in the demapper 270 can then be simplified as shown in equation (10), the function of SMG _i ( ) is a simple linear computation implemented using only shift operations and adders.

$\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; SMG</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; [</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; no</span>}}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Here, a _k is |g _k | ² c. Equation (10) is used to calculate the soft metrics associated with the in-phase component. SMG _i (Q _k , a _k ) is used to compute soft metrics related to the quadrature components shown in equation (7).

As shown in the table below, the metric of 16QAM obtained by the function SMG _i () can be obtained according to the domain to which the in-phase signal component I _k and the quadrature signal component Q _k calculated from the received symbols and the channel fading coefficient belong. For computing soft metrics, only I _k , Q _k and a _k are considered.

Table 1

The condition of I _k Λ(s _{k, 3} ) Λ(s _{k, 2} ) I _k >2a _k 2I _k -2a _k 2a _k -I _k 0<I _k ≤2a _k I _k 2a _k -I _k -2a _k <I _k ≤0 I _k I _k +2a _k I _k ≤ -2a _k 2I _k +2a _k I _k +2a _k

Table 2

Conditions for Q _k Λ(s _{k, 1} ) Λ(s _{k, 0} ) Q _k >2a _k 2Q _k -2a _k 2a _k -Q _k 0<Q _k ≤2a _k 2Q _k -2a _k 2a _k -Q _k -2a _k <Q _k ≤0 _Q Q _k +2a _k Q _k ≤ -2a _k 2Q _k +2a _k Q _k +2a _k

Table 1 shows the metrics of 16QAM generated by I _k , and Table 2 shows the metrics of 16QAM generated by Q _k . In the same way, the soft bit metrics of Λ(s _{k, 5} ), Λ(s _{k, 4} ) and Λ(s _{k, 3} ) related to 64QAM can be calculated, as shown in Table 3. In addition, Λ(s _k,2 ), Λ(s _k,1 ) and Λ(s _k,0 ) can be calculated from Q _k . The soft output related to I _k will be described below.

table 3

The condition of I _k Λ(s _{k, 5} ) Λ(s _{k, 4} ) Λ(s _{k, 3} ) I _k >6a _k 4I _k -12a _k 10a _k -2I _k 6a _k -I _k 4a _k <I _k ≤6a _k 3I _k -6a _k 4a _k -I _k 6a _k -I _k 2a _k <I _k ≤4a _k 2I _k -2a _k 4a _k -I _k I _k -2a _k 0<I _k ≤-2a _k I _k 6a _k -2I _k I _k -2a _k -2a _k <I _k ≤0 I _k 6a _k +2I _k -I _k -2a _k -4a _k <I _k ≤-2a _k 2I _k +2a _k 4a _k +I _k -I _k -2a _k -6a _k <I _k ≤-4a _k 3I _k +6a _k 4a _k +I _k 6a _k +I _k I _k ≤ -6a _k 4I _k +12a _k 10a _k +2I _k 6a _k +I _k

Table 3 shows the soft metrics for 64QAM generated by _Ik . In this way, the soft output of QPSK, 16QAM and 64QAM can be calculated. However, the soft output value itself is computed by removing 4c/ _σn2 from equation (7) expressing ^the original input LLR to the decoder.

In an exemplary hardware implementation, the dynamic range of the decoder's input metrics may be additionally increased, or its performance may be degraded. Therefore, c/σ _n ² is reflected in the normalization.

Fig. 5 shows an exemplary implementation of the working structure of the input metric normalizer according to the first exemplary embodiment of the present invention.

Figure 5 shows an ^example of a metric normalizer for reflecting the value of c/ _σn2 . Since the 'c' value is stored according to QPSK, 16QAM, and 64QAM modulation schemes, the normalizer 275 may set the 'c' value when receiving a modulation order or modulation information mod_order mapped to a modulation order. In order to calculate the noise variable σ _n ² corresponding to the sum variable of noise and interference, a noise variable estimator (as indicated by reference numeral 265 of FIG. 2 ) is required. The noise variable estimator 265 is capable of estimating the noise variable value σ _n ² using various algorithms.

In normalizer 275 , multiplier 520 receives c/σ _n ² calculated by transforming the variable values using transformation table 510 reflecting division. The LLRs are normalized when the multiplier 520 multiplies the metric Λ(n) from the demapper 270 by c/σ _n ² . After normalizing the LLRs, the rounding/truncation section 530 inputs the LLRs Λ'(n) with the desired range and desired number of bits into the decoder. According to the modulation order or coding rate supported by the system, the number of bits M of the input measure is about 24-26, and the number of normalized output bits is 6-8.

In Figure 5, various methods can be used to estimate the noise variable. For example, the method disclosed in reference 1 (T.A.Summers and S.G.Wilson, "SNR mismatch and online estimation inturbo decoding," IEEE Trans.Commun.vol.46, no.4, Apr.1998) can be used, and the disclosed content Incorporated hereby by application. Furthermore, variables related to noise and interference (ie, noise variables) can be estimated from a pilot channel of a CDMA system or a pilot tone of an OFDM system.

Fig. 6 shows another exemplary implementation of the working structure of the input metric normalizer according to the first exemplary embodiment of the present invention.

FIG. 6 shows an example of implementing the normalizer of FIG. 5 . Normalization is implemented using two shifters 630 and 640 and an adder 650 . The normalization structure can perform proper normalization while minimizing power consumption.

In FIG. 6, the modulation order (mod_order) and the noise variable are input into a normalization index calculator 610, so that a normalization index (norm_index) is calculated.

Next, an example of a normalization method will be described in detail. The normalization index calculator 610 has a temp_norm_index that maps to an estimated value that can be received from the noise variable estimator 265 . Since division by the noise variable should be reflected, temp_norm_index should be chosen to be inversely proportional to the value of the noise variable. For example, temp_norm_index should be chosen such that $\left[\mathrm{Gain}(\mathrm{temp}\_\mathrm{the\; norm}\_\mathrm{index})\left(\mathrm{dB}\right)\right]+\left[{\mathrm{\&sigma;}}_{\mathrm{no}}^2\left(\mathrm{dB}\right)\right]+a=0.$ However, "a" is a constant defined in relation to the operating range of noise and data channel values. Gain and noise estimates require logarithmic functions and are expressed on a dB scale. Also, [.] indicates conversion to the integer closest to the input. To reflect multiplication by a constant c that varies with modulation order, the following calculation is used to obtain the norm_index value.

norm_index=temp_norm_index, (QPSK)

norm_index=temp_norm_index-2, (16QAM)

norm_index=temp_norm_index-4, (64QAM)

In the normalization table 620, the norm_index value is transformed into a normalization gain value multiplied by the normalization coefficient shown in Table 4. In one step of Table 4, it is possible to adjust the LLR normalization by about 3dB. The normalization coefficients of Table 4 can be divided into more precise steps and multiple adders can be used only when more precise adjustments are possible and the number of LLR bits is to be reduced.

The values calculated by multiplying the norm_index value by the normalization coefficient are then input into shifters 630 and 640 and used to shift the metric Λ(n) from the demapper 270 . The shifted values are added in adder 650 to calculate the LLR. The normalized LLRs are input into the rounding/truncation section 660 . From the rounding and truncation section 660 output LLRΛ'(n) of the desired range and desired number of bits.

Table 4

NORM_INDEX gain '0'00000 96 '1'00001 64 '2'00010 48 '3'00011 32 '4'00100 twenty four '5'00101 16 '6'00110 12=8+4 '7'00111 8 '8'01000 6＝4+2 '9'01001 4 '10'01010 3=2+1 '11'01011 2 '12'01100 1.5＝1+1/2 '13'01101 1 '14'01110 0.75＝1/2+1/4 '15'01111 0.5 '16'10000 0.375＝1/4+1/8 '17'10001 0.25 '18'10010 0.1875＝1/8+1/16 '19'10011 0.125 '20'10100 0.09375＝1/16+1/32 ’21’10101 0.0625 '22'10110 0.046875＝1/32+1/64 '23'10111 0.03125 '24'-'31' -

The normalization method described above is an example of implementing normalization in a channel decoder of a system using QPSK, 16QAM, and 64QAM. Of course, the invention includes all possible methods of using noise estimation and the output LLR of the SMG of the modulation order.

<Second Exemplary Embodiment>

There are cases where it is difficult to calculate an accurate noise variable value in a communication system, which is different from the first exemplary embodiment. In the case of channel codes such as turbo codes and low-density parity-check (LDPC) codes unerringly approaching the Shannon limit of channel capacity, there is a noise threshold at a predetermined signal-to-noise ratio (SNR), capable of Error-free transmission. That is, if the modulation order, coding rate, and frame size are set in a communication system that uses multiple modulation methods and coding rates, the SNR of the operating area is defined as the one that can achieve the frame error rate (FER) required by the system Those SNRs. When this SNR is predefined in the system, it can be used for normalization of LLRs.

In an exemplary embodiment, when the modulation mode and the coding rate can be set in the system, expected values can be obtained through system simulation.

Fig. 7 shows the FER performance of _{the additive white Gaussian noise (AWGN) channel for QPSK and 1/2 coding, QPSK and 3/4 coding ,} ^{and 16QAM} and ^1/2 coding in IEEE 802.16e system.

Referring to FIG. 7, when the FER required by the system is about 1%, _the statically operated carrier-to-interference-noise ratio (CINR) region is about 2~3dB in the case of QPSK and ^1/2 coding. Since the CINR in the SNR region over about 2~3dB is sufficiently high even when the normalization of the LLR is not optimal, the FER is sufficiently reduced, so the performance of the whole system is not affected. In case of lower CINR, FER has a value close to "1" regardless of LLR normalization.

Therefore, the performance of LLR normalization hardly degrades even when the system uses predefined values instead of actual measured noise variables. The SNR is defined when the signal power obtained by the automatic gain control is substantially known, so that noise variable values can also be detected. In the case of QPSK and ^1/2 coding, it is assumed _that the basic operating band has 3dB. Furthermore, assuming that an automatic gain loop is applied and that the signal power P is constant, the noise variation mapped to the signal power P and CINR of 3dB has the relationship shown in the following equation.

$\mathrm{<span\; class="notranslate">\; 20</span>}{\mathrm{<span\; class="notranslate">\; log</span>}}_{\mathrm{<span\; class="notranslate">\; 10</span>}}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; P</span>}}{{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

That is, the noise variable is defined by the following equation.

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="10"\; label="P"\; filenames="A20068004211400302.png,A20068004211400321.png"\; state="\{\{state\}\}">P</figure-callout></span>}}{{\mathrm{<span\; class="notranslate">\; 10</span>}}^{\mathrm{<span\; class="notranslate">\; 0.15</span>}}}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

In the case of QPSK and ^1/2 encoding, if the calculated noise variable is pre-stored in _the receiver, even when the normalization of LLR is performed using the pre-stored noise variable value every time without calculating the actual noise variable value Also can get the best performance.

In the second exemplary embodiment of the present invention, CINR is fixed at about 3dB based on AWGN. In an exemplary case, QAM symbols constituting one frame are subject to almost independent fading due to interleaving or the like. Achieving the 1% FER required by the system at higher CINR compared to AWGN. Thus, in the exemplary case, the FER should be considered to set the noise variable value pre-stored in the system. Examples of QPSK and ^1/2 encoding have _been described. Of course, the same approach can be applied even when selecting other modulation orders and other coding rates.

In an exemplary embodiment, the automatic gain controller (AGC) of the system works normally with the configuration described above, and the variation from the ideal value is small.

FIG. 8 shows the structure of a wireless communication transceiver to which a metric normalizer is applied according to a second exemplary embodiment of the present invention.

Referring to FIG. 8 , binary data i(n) to be transmitted is encoded in a channel encoder 810 within a transmitter 800 . Channel encoder 810 generates a series of binary code symbols c(n). A mapper 820 generates blocks of several code symbols of the generated code symbols, mapped to a point on the signal constellation, and transformed into complex-valued modulation symbols x(n). The sequence x(n) is applied to modulator 830 . The modulator 830 generates a time continuous wave in a CDMA or OFDM scheme according to the symbol, and transmits the generated wave to the receiver 850 through the channel 840 .

In receiver 850 , a demodulator/channel estimator 860 performs baseband demodulation and channel estimation processing on the signal passing through channel 840 . The demodulator can be implemented according to various technologies applied to baseband. For example, the demodulator may be an OFDM demodulator implemented with a CDMA rake receiver or an IFFT processor and channel estimator.

The resulting channel estimate and received symbols after baseband modulation are output from demodulator/channel estimator 860 into demapper 870 . The demapper 870 receives the channel estimate c(n) from the demodulator/channel estimator 860 and the received symbol y(n) modulated by QAM or PSK, and outputs the metric per bit through demapping. Demapper 870 can use a variety of algorithms to derive metrics. The demapping algorithm described with reference to FIG. 2 may be used.

Since the convolutional turbo decoder serving as the channel decoder 880 in the receiver 850 of the IEEE 802.16 system receives and decodes the soft metric corresponding to the reliability information of each bit, it is necessary to receive from the distortion in the preceding stage of the channel decoder 880. The process of computing soft metrics in the incoming signal. This process is performed by a demapper 870 in the receiver 850 .

According to the metric Λ(n) output from the demapper 870 and the adaptive modulation and coding (MAC) information of the above-mentioned modulation scheme and coding rate from the controller 865, the LLR normalizer 875 receives and normalizes the predefined Noise variable value. The channel decoder 880 receives the normalized value Λ'(n) and then outputs i(n).

Fig. 9 shows an exemplary implementation of the working structure of the input metric normalizer according to the second exemplary embodiment of the present invention.

In FIG. 9, the AMC information according to the modulation scheme and coding rate uses a predefined noise variable table. Referring to FIG. 9 , the noise variable table 910 of the normalizer 875 stores 'c' values according to modulation schemes of QPSK, 16QAM, and 64QAM. When receiving AMC information such as modulation order, encoding rate, frame size, etc., the normalizer 875 can set a reference 'c' value according to the noise value and modulation order predetermined by the AMC information.

In normalizer 875, multiplier 930 receives c/ _σn2 calculated by transforming noise variable values and reference "c" values using transformation table ⁹²⁰ reflecting division. The LLRs are normalized when the multiplier 930 multiplies the metric Λ(n) from the demapper 870 by c/σ _n ² . After normalizing the LLRs, the rounding/truncation section 940 inputs the LLRs Λ'(n) with the desired range and desired number of bits into the decoder.

Fig. 10 shows another exemplary implementation of the working structure of the input metric normalizer according to the second exemplary embodiment of the present invention.

In FIG. 10 , the normalizer predefines a set of normalization coefficients to replace the transformation table 920 in FIG. 9 , only receives the normalization index, and sets the normalization coefficients.

The normalized index receiver 1010 receives information about the modulation order, coding rate, frame size, etc., sets a normalized index that can reflect the "c" value and the noise variable value, and outputs the set normalized index to the normalized A Chemical Form 1020. The normalized index value can be set using a predefined table.

When the set normalization index is received, a set of possible normalization coefficients may be predefined in the normalization table 1020 . When a normalization index is received, the normalization coefficient is set. The LLRs are normalized when the multiplier 930 multiplies the metric Λ(n) from the demapper 870 by the normalization coefficient. After normalizing the LLRs, the rounding/truncation section 1040 inputs the LLRs Λ'(n) with the desired range and desired number of bits into the decoder.

Fig. 11 shows yet another exemplary implementation of the working structure of the input metric normalizer according to the second exemplary embodiment of the present invention.

FIG. 11 shows a structure obtained by simplifying the structure of FIG. 10 . Normalization is implemented using two shifters 1130 and 1140 and one adder 1150 . The normalization structure can perform proper normalization while minimizing power consumption.

Information on a modulation system and a coding rate (or a forward error correction (FEC) coding type) is input into a normalization index calculator 1110, so that a normalization index (norm_index) is calculated. In the normalization table 1120, the calculated norm_index value is transformed into a normalization gain value multiplied by the normalization coefficient shown in Table 4. In one step of Table 4, it is possible to adjust the LLR normalization by about 3dB. The normalization coefficients of Table 4 can be divided into more precise steps and multiple adders can be used only when more precise adjustment is possible and the number of LLR bits is to be reduced.

Then, the value calculated by multiplying the norm_index value by the normalization coefficient is input into shifters 1130 and 1140 and used to shift the metric Λ(n) from demapper 870 . The shifted values are added in an adder 1150 to calculate the LLR. The normalized LLRs are input into the rounding/truncation section 1160 . Output LLRΛ'(n) with desired range and desired number of bits.

An example of the normalization method using Table 4 is as follows.

This example is for situations where it is difficult to estimate accurate noise variables in IEEE 802.16e systems. The fact that codes at the same coding rate in the same modulation scheme have a 1% FER at substantially the same SNR is used. In each modulation scheme, the SNR at an FER of 1% is calculated, and norm_index is provided in which a virtual noise index is reflected. The IEEE 802.16e system has the following modulation codes for data bursts to which convolutional turbo coding is applied. In this embodiment, norm_index_basic is used for the actual norm_index. Table 5 shows an example of IEEE 802.16e normalization when implementing the structure of FIG. 8 .

table 5

FEC code type Modulation encoding rate gain Norm_index_basic 0 QPSK ¹ / ₂ 2.0 11 1 QPSK ³ / ₄ 6.0 8 2 16QAM ¹ / ₂ 3.0 10 3 16QAM ³ / ₄ 8.0 7 4 64QAM ¹ / ₂ 8.0 7

5 64QAM 2/3 12.0 6 6 64QAM ³ / ₄ 16.0 5 7 64QAM 5/6 24.0 4

In Table 5, norm_index_basic is used to reflect the burst boosting or area boosting defined in the IEEE 802.16e system. In the concept of burst power control, the IEEE 802.16e system supports an increase of -12dB to 9dB. When the frequency reuse factor is 1/3, an area increase of 4.77dB is supported. In this case, since the LLR value is affected by the increase, it needs to be compensated so that the effective operating area of the LLR can be reduced. For example, norm_index can be calculated using the following equation.

......(13)

In Equation (13), the increase unit is dB, and [a] means rounding to the nearest integer. Also, norm_index has values within the given range [0 24]. Using this method, a more general LLR normalization is possible.

The above-described implementation method of the present invention is an example of a method of normalizing LLRs corresponding to metrics input to a decoder using normalization coefficients and AMC information. The present invention applies normalization to the output of the soft output generator acting as a demapper, and includes all implementations that use AMC information to perform normalization.

Figures 12 and 13 show the performance of the convolutional turbo codes defined in the IEEE 802.16e system when using a 6- or 8-bit soft-input metric and when performing floating-point operations. The Turbo Decoder uses the Maximum Log MAP method. As can be seen, normalized LLR and floating point operations (denoted by "Fade, Ft" and Indicated by "AWGN, Ft") there is substantially no performance difference between the exemplary embodiments of the present invention.

As can be seen from the above description, exemplary implementations of specific embodiments of the present invention have the following effects.

According to an exemplary embodiment of the present invention, in a wireless communication system, a channel has a different value in each symbol by normalizing a soft output from a demapper. Also in the case of OFDM systems requiring higher metric resolution, the desired performance can be achieved with a small number of bits input to the turbo decoder.

Although exemplary embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will understand that various modifications, additions and substitutions can be made without departing from the scope of the present invention. Accordingly, the invention is not to be limited to the embodiments described above, but to be defined by the appended claims along with their full scope of equivalents.

Claims (28)

1. A device for normalizing soft metrics input to a channel decoder in a wireless communication system, comprising: a demapper for generating soft metrics; and A normalizer, for receiving the soft metric, calculates a normalized log-likelihood ratio LLR by multiplying the soft metric by the ratio of a constant value to a noise variable value, and transforms the normalized LLR into a range and a number of bits, and outputs the input LLR of the channel decoder. 2. The apparatus of claim 1, wherein the demapper uses _{at least one of the in-phase component X k and the quadrature component Y k of} the received modulation symbol R _k , the channel fading coefficient g _k and the received The constant value c defined by the modulation order of the modulation symbol is used to generate soft metrics. 3. The apparatus of claim 1, wherein the normalizer multiplies the soft metric by to perform normalization, where σ _n ² is the value of the noise variable computed from the channel estimate of the received modulation symbols. 4. The apparatus of claim 3, wherein the soft metric is derived from the following table: The condition of I _k Λ(s _{k, 3} ) Λ(s _{k, 2} ) I _k >2a _k 2I _k -2a _k 2a _k -I _k 0<I _k ≤2a _k I _k 2a _k -I _k -2a _k <I _k ≤0 I _k I _k +2a _k I _k ≤ -2a _k 2I _k +2a _k I _k +2a _k and Conditions for Q _k Λ(s _{k, 1} ) Λ(s _{k, 0} ) Q _k >2a _k 2Q _k -2a _k 2a _k -Q _k 0<Q _k ≤2a _k 2Q _k -2a _k 2a _k -Q _k -2a _k <Q _k ≤0 _Q Q _k +2a _k Q _k ≤ -2a _k 2Q _k +2a _k Q _k +2a _k , where I _k , Q _k and a _k are |g _k | ² X _k , |g _k | ² Y _k and |g _k | ² c respectively. 5. The apparatus of claim 1, wherein the normalizer comprises: a transformation table for receiving noise variable values calculated from channel estimates of received modulation symbols and outputting a ratio of the constant value to the noise variable value; a multiplier for outputting a normalized LLR obtained by multiplying the soft metric output from the demapper by a ratio of a constant value to a noise variable value; and A rounding/truncation section for outputting the input LLRs of the channel decoder obtained by transforming the normalized LLRs to the range and the number of bits. 6. The apparatus of claim 2, wherein the normalizer comprises: a normalization index calculator for receiving a noise variable value calculated from a channel estimate of a received modulation symbol, selecting a temporary normalization index mapped to a division by the noise variable value, and selecting a temporary normalization index from the selected temporary normalization index subtracting a predetermined value according to the modulation order from the normalization index to generate a normalization index; a normalization table for transforming the normalization index into a normalization gain value calculated by multiplying the normalization index by a normalization coefficient; a shifter for shifting the in-phase component and the quadrature component of the received modulation symbol according to the normalized gain value; an adder for outputting a normalized LLR obtained by adding the shifted values; and A rounding/truncation section for outputting the input LLRs of the channel decoder obtained by transforming the normalized LLRs to the range and the number of bits. 7. A device for normalizing soft metrics input to a channel decoder in a wireless communication system, comprising: a demapper for generating soft metrics; and A normalizer for receiving the soft metric, calculating a normalized log-likelihood ratio LLR by multiplying the soft metric by a normalization coefficient calculated by the adaptive modulation and coding AMC information, the normalized The optimized LLRs are transformed to the desired range and desired number of bits, and output as input LLRs to the channel decoder. 8. The apparatus of claim 7, wherein the demapper uses _{at least one of the in-phase component X k and the quadrature component Y k of} the received modulation symbol R _k , the channel fading coefficient g _k and the received The constant value c defined by the modulation order of the modulation symbol is used to generate soft metrics. 9. The apparatus of claim 7, wherein the normalizer performs normalization by multiplying the soft metric by the normalization coefficient calculated by the AMC information set by the chemical index. 10. The apparatus of claim 9, wherein the soft metric is derived from the following table: The condition of I _k Λ(s _{k, 3} ) Λ(s _{k, 2} ) I _k >2a _k 2I _k -2a _k 2a _k -I _k 0<I _k ≤2a _k I _k 2a _k -I _k -2a _k <I _k ≤0 I _k I _k +2a _k I _k ≤ -2a _k 2I _k +2a _k I _k +2a _k and The condition of I _k Λ(s _{k, 5} ) Λ(s _{k, 4} ) Λ(s _{k, 3} ) I _k >6a _k 4I _k -12a _k 10a _k -2I _k 6a _k -I _k 4a _k <I _k ≤6a _k 3I _k -6a _k 4a _k -I _k 6a _k -I _k 2a _k <I _k ≤4a _k 2I _k -2a _k 4a _k -I _k I _k -2a _k 0<I _k ≤-2a _k I _k 6a _k -2I _k I _k -2a _k -2a _k <I _k ≤0 I _k 6a _k +2I _k -I _k -2a _k -4a _k <I _k ≤-2a _k 2I _k +2a _k 4a _k +I _k -I _k -2a _k -6a _k <I _k ≤-4a _k 3I _k +6a _k 4a _k +I _k 6a _k +I _k I _k ≤ -6a _k 4I _k +12a _k 10a _k +2I _k 6a _k +I _k , where I _k , Q _k and a _k are |g _k | ² X _k , |g _k | ² Y _k and |g _k | ² c respectively. 11. The apparatus of claim 7, wherein the normalizer comprises: a noise variable table for setting and outputting the noise variable value and the constant value according to at least one of a plurality of pieces of information input from the receiving controller storing the AMC information and the increase information; A conversion table, used to receive the noise variable value and the constant value, and output the ratio of the constant value to the noise variable value; a multiplier for outputting a normalized LLR obtained by multiplying the soft metric output from the demapper by a ratio of a constant value to a noise variable value; and A rounding/truncation section for outputting the input LLRs of the channel decoder obtained by transforming the normalized LLRs to the range and the number of bits. 12. The apparatus of claim 7, wherein the normalizer comprises: a normalized index calculator for generating a normalized index in which the noise variable value and the constant value are reflected based on at least one of a plurality of pieces of information input from the receiving controller storing the AMC information and the increase information; A normalization table, used to set the normalization coefficient mapped to the normalization index; a multiplier for outputting a normalized LLR obtained by multiplying a soft metric output from the demapper by a set normalization coefficient; and A rounding/truncation section for outputting the input LLRs of the channel decoder obtained by transforming the normalized LLRs to the range and the number of bits. 13. The apparatus of claim 7, wherein the normalizer comprises: a normalized index calculator for generating a normalized index in which the noise variable value and the constant value are reflected based on at least one of a plurality of pieces of information input from the receiving controller storing the AMC information and the increase information; a normalization table for transforming the normalization index into a normalization gain value calculated by multiplying the normalization index by a normalization coefficient; a shifter for shifting the in-phase component and the quadrature component of the received modulation symbol according to the normalized gain value; an adder for outputting a normalized LLR obtained by adding the shifted values; and A rounding/truncation section for outputting the input LLRs of the channel decoder obtained by transforming the normalized LLRs to the range and the number of bits. 14. The apparatus of claim 7, wherein the AMC information includes at least one of a modulation order, a coding rate and a frame size. 15. A method of normalizing soft metrics input to a channel decoder in a wireless communication system, comprising: generate soft metrics; receiving the soft metric and calculating a normalized log-likelihood ratio LLR by multiplying the soft metric by the ratio of the constant value to the value of the noise variable; transforming the normalized LLRs to a range and a number of bits; and The input LLR of the output channel decoder. 16. The method of claim 15, wherein the step of generating soft metrics comprises using the in-phase component X _k and the quadrature component Y _k of the received modulation symbol R _k , the channel fading coefficient g _k and the received The constant value c defined by the modulation order of the modulation symbol is used to generate soft metrics. 17. The method of claim 15, wherein by multiplying the soft metric by

to compute the normalized LLR, where σ _n ² is the value of the noise variable computed from the channel estimate of the received modulation symbols. 18. The method of claim 17, wherein said step of generating a soft metric comprises generating a soft metric from the following table: The condition of I _k Λ(s _{k, 3} ) Λ(s _{k, 2} ) I _k >2a _k 2I _k -2a _k 2a _k -I _k 0<I _k ≤2a _k I _k 2a _k -I _k -2a _k <I _k ≤0 I _k I _k +2a _k I _k ≤ -2a _k 2I _k +2a _k I _k +2a _k and The condition of I _k Λ(s _{k, 5} ) Λ(s _{k, 4} ) Λ(s _{k, 3} ) I _k >6a _k 4I _k -12a _k 10a _k -2I _k 6a _k -I _k 4a _k <I _k ≤6a _k 3I _k -6a _k 4a _k -I _k 6a _k -I _k 2a _k <I _k ≤4a _k 2I _k -2a _k 4a _k -I _k I _k -2a _k 0<I _k ≤-2a _k I _k 6a _k -2I _k I _k -2a _k -2a _k <I _k ≤0 I _k 6a _k +2I _k -I _k -2a _k -4a _k <I _k ≤-2a _k 2I _k +2a _k 4a _k +I _k -I _k -2a _k -6a _k <I _k ≤-4a _k 3I _k +6a _k 4a _k +I _k 6a _k +I _k I _k ≤ -6a _k 4I _k +12a _k 10a _k +2I _k 6a _k +I _k , where I _k , Q _k and a _k are |g _k | ² X _k , |g _k | ² Y _k and |g _k | ² c respectively. 19. The method of claim 15, wherein the step of outputting the input LLRs of the channel decoder comprises: receiving a noise variable value calculated from a channel estimate of the received modulation symbols, and outputting a ratio of the constant value to the noise variable value; outputting a normalized LLR obtained by multiplying the soft metric by the ratio of the constant value to the value of the noise variable; and Output the input LLRs of the channel decoder obtained by transforming the normalized LLRs to this range and this number of bits. 20. The method of claim 15, wherein the step of outputting the input LLRs of the channel decoder comprises: receiving a noise variable value calculated from a channel estimate of the received modulation symbol, and selecting a temporary normalization index that maps to dividing by the noise variable value; Generate a normalization index by subtracting the value according to the modulation order from the selected temporary normalization index; transforming the normalized index into a normalized gain value calculated by multiplying the normalized index by a normalized coefficient; shifting the in-phase and quadrature components of the received modulation symbols according to the normalized gain value; output the normalized LLR obtained by adding the shifted values; and Output the input LLRs of the channel decoder obtained by transforming the normalized LLRs to this range and this number of bits. 21. A method of normalizing soft metrics input to a channel decoder in a wireless communication system, comprising: generate soft metrics; receiving the soft metric and computing a normalized log-likelihood ratio LLR by multiplying the soft metric by a normalization coefficient computed from adaptive modulation and coding AMC information; and The normalized LLRs are transformed to a range and a bit number, and output as input LLRs to the channel decoder. 22. The method of claim 21, wherein the step of generating soft metrics comprises using the in-phase component X _{k and the quadrature component Y k of} the received modulation symbol R _k , the channel fading coefficient g _k and the received The constant value c defined by the modulation order of the modulation symbol is used to generate soft metrics. 23. The method of claim 21, wherein by multiplying the soft metric by

to compute the normalized LLR, where σ _n ² is the value of the noise variable computed from the channel estimate of the received modulation symbols. 24. The method of claim 23, wherein said step of generating a soft metric comprises generating a soft metric from the following table: The condition of I _k Λ(s _{k, 3} ) Λ(s _{k, 2} ) I _k >2a _k 2I _k -2a _k 2a _k -I _k 0<I _k ≤2a _k I _k 2a _k -I _k -2a _k <I _k ≤0 I _k I _k +2a _k I _k ≤ -2a _k 2I _k +2a _k I _k +2a _k and The condition of I _k Λ(s _{k, 5} ) Λ(s _{k, 4} ) Λ(s _{k, 3} ) I _k >6a _k 4I _k -12a _k 10a _k -2I _k 6a _k -I _k 4a _k <I _k ≤6a _k 3I _k -6a _k 4a _k -I _k 6a _k -I _k 2a _k <I _k ≤4a _k 2I _k -2a _k 4a _k -I _k I _k -2a _k 0<I _k ≤-2a _k I _k 6a _k -2I _k I _k -2a _k -2a _k <I _k ≤0 I _k 6a _k +2I _k -I _k -2a _k -4a _k <I _k ≤-2a _k 2I _k +2a _k 4a _k +I _k -I _k -2a _k -6a _k <I _k ≤-4a _k 3I _k +6a _k 4a _k +I _k 6a _k +I _k I _k ≤ -6a _k 4I _k +12a _k 10a _k +2I _k 6a _k +I _k , where I _k , Q _k and a _k are |g _k | ² X _k , |g _k | ² Y _k and |g _k | ² c respectively. 25. The method of claim 21, wherein the step of outputting the input LLRs of the channel decoder comprises: setting the noise variable value and the constant value according to at least one of a plurality of pieces of information input from the receiving controller storing the AMC information and the increase information; Receive the noise variable value and the constant value, and output the ratio of the constant value to the noise variable value; outputting a normalized LLR obtained by multiplying the soft metric by the ratio of the constant value to the value of the noise variable; and Output the input LLRs of the channel decoder obtained by transforming the normalized LLRs to this range and this number of bits. 26. The method of claim 21, wherein the step of outputting the input LLRs of the channel decoder comprises: generating a normalized index in which the noise variable value and the constant value are reflected according to at least one of a plurality of pieces of information input from the receiving controller storing the AMC information and the increase information; Set the normalization coefficient mapped to this normalization index; outputting a normalized LLR obtained by multiplying the soft metric by a set normalization coefficient; and Output the input LLRs of the channel decoder obtained by transforming the normalized LLRs to this range and this number of bits. 27. The method of claim 21, wherein the step of outputting the input LLRs of the channel decoder comprises: generating a normalized index in which the noise variable value and the constant value are reflected according to at least one of a plurality of pieces of information input from the receiving controller storing the AMC information and the increase information; transforming the normalized index into a normalized gain value calculated by multiplying the normalized index by a normalized coefficient; shifting the in-phase and quadrature components of the received modulation symbols according to the normalized gain value; output the normalized LLR obtained by adding the shifted values; and Output the input LLRs of the channel decoder obtained by transforming the normalized LLRs to this range and this number of bits. 28. The method of claim 21, wherein the AMC information includes at least one of modulation order, coding rate and frame size.

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