Extraction of Channel State Information in COFDM System and Its Application in Soft Decision Compared with a single carrier system, an orthogonal frequency division multiplexing (COFDM) system uses multiple subcarriers to transmit a parallel data stream. The encoded and interleaved data stream is modulated on multiple subcarriers. In the path fading channel, the COFDM system has better anti-fading performance than the single carrier system, so the COFDM technology has been increasingly widely used in the field of broadband communication. The wireless channel of a broadband communication system is usually frequency selective and time-varying, and its channel transfer function exhibits non-uniformity in both time and frequency domains. Therefore, in the COFDM system, the channel change must be dynamically estimated at its demodulation end. In this paper, based on the results of channel estimation and equalization in the COFDM system, combined with the specific requirements of the COFDM transmission system in the European digital TV standard DVB-T, two methods for extracting channel state information (CSI) are given. Channel state information in COFDM system In a time-invariant system, such as a Gaussian white noise channel, the data signal is modulated on a single carrier, and all data signals at the demodulation end are superimposed with the same average noise power. Therefore, in a single carrier communication system, for the received signal used for decision, the reliability of the decision depends only on the proportional relationship between the distance between the received signal value and the decision threshold. That is to say, the received signals located on the same carrier frequency show a certain uniformity in the time domain or the frequency domain, that is, the reliability of the decision is fair to all the signals. However, there are other factors in the actual wireless channel that affect the reliability of the received signal. The COFDM system must be considered during demodulation. In a typical frequency selective fading channel such as Rayleigh channel, if the COFDM transmit signal is modulated on multiple carriers with the same power, but due to the non-uniform channel characteristics, different carriers on the demodulation end will have different Signal-to-noise ratio (SNR). Therefore, when the signal is judged, the data modulated on the carrier with a high signal-to-noise ratio has higher decision reliability than the data transmitted on the carrier with a low signal-to-noise ratio. This priori reliability information that is time-varying before the decision is called channel state information (CSI), which dynamically reflects the change of the channel. The non-uniform, unfair credibility caused by channel changes must be considered in soft-decision decoding. Therefore, before the Viterbi decoding at the COFDM demodulation end, it is very necessary to extract the CSI. This is also a very important and unique structure that distinguishes the multi-carrier system from the single-carrier system. Generally, the channel state information is defined as the signal-to-noise ratio of each carrier position. Under the Gaussian white noise channel, the CSI can be calculated only by estimating the signal power. However, in frequency-selective channels and channels with narrow-band interference within the effective signal bandwidth, the noise power on each subcarrier is different. In order to improve system performance, the noise power at each carrier position needs to be continuously estimated. Using the results of channel estimation and channel equalization, we can easily obtain an estimate of signal power, but it is more difficult to estimate noise power. Two methods for extracting CSI using channel estimation and equalization results are given below. Figure 1: Using channel equalization results to extract CSI Method for extracting CSI in COFDM system CSI information can be extracted using the results of COFDM system front-end channel estimation and equalization. The implementation block diagram is shown in Figure 1. In the figure, yk is the received signal, hk * is the estimated value of the actual channel response hk, and zk is the estimated value of the actual transmitted signal xk. All we need is the SNR value of zk at different carrier frequencies. Here are two methods for extracting CSI. Due to the special data processing process of COFDM, the following algorithms are all performed in the frequency domain. Use the normalization method to find the average noise power to obtain CSI zk is the estimated value of the actual transmitted signal xk, and its SNR should be the ratio of the effective signal power contained to the noise power superimposed on it. It can be known from the channel equalization result: Where Wk is Gaussian white noise superimposed on the channel, and Wk follows an independent normal distribution. Take the mean square of the two ends of the above formula: Where δXk2 is the average power of the transmitted signal, and δw2 is the average Gaussian noise power. It can be seen that the first term in the above formula is the effective signal power, and the second term is the noise power superimposed on the effective signal. The signal-to-noise ratio of the Zk point can be obtained from the definition of SNR: At the modulation end of the COFDM system, since the data signal has undergone randomization processing such as scrambling and interleaving before mapping, it should be a constant, and its value is only related to the constellation used in the modulation end mapping. Therefore, the signal-to-noise ratio formula can also be simplified to: Substituting into equation (2) shows that SNR can be expressed as the reciprocal of the noise power. In the actual implementation, in order to avoid division, the average noise power can be calculated first: The mean square power δZk of Zk can be obtained by the normalization method. Since δXk2 is a constant, the channel state information can be obtained by quantization and table lookup mapping. Figure 2 is a system block diagram of iterative calculation of CSI using the normalization method. CSIk is the CSI value of the k-th carrier position. In frequency-selective channels where channel fading changes rapidly, a large amount of statistical averaging is required to reflect the statistical characteristics of noise. The statistics in Figure 2 uses a FIFO (first-in first-out data register) to continuously update the data and average it. The coefficient Ï can adjust the iteration speed. The longer the iteration time, the more it can reflect the statistics of the noise in the channel Characteristics, thereby obtaining real-time channel state information CSIK. Moreover, since the CSI at this time is a long-term statistical averaging result, the influence of narrow-band interference in the channel on the channel estimation is eliminated. Use the signal power transfer function of the channel as CSI From the above and observing equation (3), it can be seen that the SNR is proportional to the channel power transfer function | hk * | 2. In frequency-selective channels with slow signal fading, the statistical characteristics of channel noise also change slowly, so | hk * | 2 can be used directly as channel state information. In this way, the calculation of CSI becomes relatively simple. The CSI function at this time fully reflects the change of signal power with channel fading. However, since the influence of channel noise on SNR is not considered, it is only roughly equivalent to CSI, and it cannot eliminate the narrow-band interference existing in the effective bandwidth of the channel. Therefore, using | hk * | 2 as CSI can only be applied to channels where fading changes such as fixed reception are relatively slow, but not to channels where fading changes such as mobile reception are relatively fast and there is narrow-band interference. Application of CSI in soft decision decoding As mentioned above, in the frequency selective channel, each carrier position has a different SNR, so the extracted CSIk value also fluctuates, and data with a high CSI value has higher reliability than data with a low CSI value. In multipath fading channels such as Rice and Rayleigh, the range of CSI value is very large, so we must set a threshold for the value of CSI, uniformly quantify the reliability of CSI, that is, the data, and set it to Multiple different credibility steps. The following will combine the decision credibility in soft decision with CSI to jointly illustrate this process. In order to improve the performance of the system, soft decision is used when de-mapping the data before Viterbi decoding, that is, the decision of mi (the i-th bit of QAM symbol) Reliability is measured by the distance from the position of the data symbol to the decision threshold of the constellation diagram. Figure 3: Uniform 64QAM constellation Figure 3 is the uniform 64QAM constellation used in the European DVB-T system and its corresponding bit relationship. Figure 4: 64QAM constellation decision confidence measurement The curve in FIG. 4 represents the measurement of the decision reliability when the 64QAM constellation is demapped. In Figure 4, curve a is the metric value of 64QAM demapping the first bit (b0) and second bit (b1), curve b is the metric value of the third bit (b2) and the fourth bit (b3), curve c Is the measurement value of the fourth bit (b4) and the fifth bit (b5). The decision thresholds of b0 and b1 are 0, the decision thresholds of b2 and b3 are +4 and -4, and the decision thresholds of b4 and b5 are +6, -6, +2 and -2. According to the signal characteristics of QAM, b0 and b1 have higher priority than b2, b3, b4 and b5, and b2 and b3 have higher priority than b4 and b5. This feature can be used as a measure of credibility. Since CSI and mi have a direct relationship with the reliability of data judgment, with CSI and mi, the final credible metric value ri can be obtained by multiplying CSI by mi. Where CSIk represents the CSI value of the k-th carrier position, and i represents the ith bit output by demapping the k-th carrier data symbol. Figure 5: Performance comparison of two CSI extraction methods In order to make full use of the information of the channel output signal and improve the reliability of decoding, the credibility value r must be properly quantized, and then input to the soft-decision Viterbi decoder for decoding. The more accurate the quantization, the more accurately the credibility of the received symbol can be reflected, so that the performance of the decoder is close to maximum likelihood decoding. Performance simulation analysis Figure 5 is the algorithm simulation of the two CSI extraction methods proposed in this paper. The simulation is based on the European digital TV DVB-T standard, the carrier number is 2048, the 2/3 code rate shrinking convolutional code, and the uniform 64-QAM constellation mapping, Guard interval 1/16. The simulations were performed in the Rice channel (F) and Rayleigh channel (P), and the channel parameters were based on the channel model provided by the DVB-T standard. In the figure, 1 represents using the normalization method to extract CSI, and 2 represents using the signal power transfer function to replace CSI. It can be seen from the figure that the normalization method has better performance. Comparing the Rice channel (F) and the Rayleigh channel (P), it can be seen that under the Rayleigh channel (P), the first extraction method can obtain better performance than the second extraction method in the Rice channel (F). This is mainly because the Rayleigh channel is closer to the actual wireless mobile channel and has a worse fading characteristic than the Rice channel. Since the second extraction method uses the signal power transfer function as CSI, it does not take into account the change of the noise statistical characteristics of the channel, so it is not suitable for channels with fast fading changes and narrow-band interference, but due to its relatively simple implementation, It can be used for fixed reception channels with relatively slow fading changes. The CSI extracted by the normalization method is obtained after a long-term iteration, which fully reflects the statistical characteristics of the channel noise, so it is more suitable for mobile reception and other fading channels that change rapidly and have narrow-band interference. About Silicone Insoles :
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