CN111818661A - Optimization algorithm for PUCCH SINR estimation - Google Patents
Optimization algorithm for PUCCH SINR estimation Download PDFInfo
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- CN111818661A CN111818661A CN202010720217.5A CN202010720217A CN111818661A CN 111818661 A CN111818661 A CN 111818661A CN 202010720217 A CN202010720217 A CN 202010720217A CN 111818661 A CN111818661 A CN 111818661A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/20—Control channels or signalling for resource management
- H04W72/21—Control channels or signalling for resource management in the uplink direction of a wireless link, i.e. towards the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/535—Allocation or scheduling criteria for wireless resources based on resource usage policies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
- H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
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- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses an optimization algorithm for PUCCH SINR estimation, which estimates a channel and a signal-to-interference-and-noise ratio in a time domain, converts the extracted PUCCH frequency domain data into time domain data through sample point fast inverse Fourier transform, and estimates the signal-to-interference-and-noise ratio through the time domain data of the sample point. The invention solves the problem of inaccurate noise estimation caused by channel time delay under the condition of high signal-to-noise ratio, thereby improving the SINR estimation precision and improving the PUCCH performance.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to an optimization algorithm for PUCCH SINR estimation.
Background
Most of the existing technologies do not consider the influence of the channel on the estimation of the PUCCH (Physical uplink control channel) NI (Noise plus Interference), and determine the equivalent Noise power value of the PUCCH channel according to the signal on the PUCCH pilot symbol of the received Physical uplink control channel, specifically include estimating a power sequence for one time domain pilot channel, and adding or averaging the average of m time domain pilot channel estimation power values with the minimum time domain channel estimation power value as the equivalent power value of the PUCCH channel, however, in the case of high signal-to-Noise ratio, after the PUCCH signal passes through the channel, a fractional time delay may occur after the time delay is subjected to inverse fast fourier transform at a sample point, and the Interference introduced by the fractional time delay causes inaccurate Noise estimation, the estimated Noise power is larger, and the Interference-to-Noise ratio is smaller, so the channel time delay causes inaccurate Noise estimation, and the channel estimation, Low SINR accuracy and poor PUCCH performance.
Disclosure of Invention
The present invention aims to solve the above problems and provide an optimization algorithm for PUCCH SINR estimation, which includes the following steps:
s1, converting the received PUCCH frequency domain data into time domain data after fast Fourier inverse transformation of sample points;
s2, calculating the time domain signal power value of the sample point, finding the maximum value of the time domain signal power value on the orthogonal sequence where the PUCCH user is located and the position index thereof, and estimating the timing deviation of the integral multiple sample;
s3, calculating gradient values of two adjacent points of the maximum power value and the maximum power value according to the position of the maximum power value, and calculating the absolute value of the ratio of the gradient values of the two adjacent points; if the maximum power value is at two ends of the sample point data, the gradient value is not calculated;
s4, setting a high signal-to-noise ratio threshold value and an interval step length, traversing all the signals higher than the high signal-to-noise ratio threshold value by the interval step length to obtain linear values of noise estimation under different system bandwidth conditions, selecting a group of linear values with the largest noise estimation error as an optimization coefficient table, and obtaining noise power normalization coefficients with different signal-to-noise ratios and different time delays;
multiplying the absolute value of the gradient value ratio obtained in the step S3 by half of the number of all time delays, and taking the result as an index value of the time delay dimension in the optimization coefficient table;
s5, finding out the index value of signal-to-noise ratio dimension in the optimization coefficient table according to the maximum value of the time domain signal power found in the step S1;
s6, obtaining a noise power normalization coefficient under the current time delay of the current signal-to-noise ratio according to the step S4 and the step S5, judging whether the current signal-to-noise ratio is in the traversal interval, if so, interpolating the noise power normalization coefficient to obtain an interpolated optimization coefficient, otherwise, turning to S7;
and S7, calculating the power average value of the users without PUCCH configuration as PUCCH noise power, multiplying the estimated noise power by the optimization coefficient to obtain an optimized noise power estimated value, and obtaining the optimized PUCCH SINR estimated value by utilizing the ratio of the signal power to the noise power.
The PUCCH algorithm estimates a channel and a signal-to-interference-and-noise ratio in a time domain, converts extracted PUCCH frequency domain data into time domain data through fast inverse Fourier transform of sample points, and estimates the signal-to-interference-and-noise ratio through the time domain data of the sample points.
The invention has the beneficial effects that: the optimization algorithm for estimating the SINR of the PUCCH solves the problem of inaccurate noise estimation caused by channel time delay under the condition of high signal-to-noise ratio, thereby improving the SINR estimation precision and improving the PUCCH performance.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
as shown in fig. 1, the optimization algorithm for PUCCH SINR estimation of the present invention includes the following steps:
s1, converting the received PUCCH frequency domain data into time domain data after fast Fourier inverse transformation of sample points;
s2, calculating the time domain signal power value of the sample point, finding the maximum value of the time domain signal power value on the orthogonal sequence where the PUCCH user is located and the position index thereof, and estimating the timing deviation of the integral multiple sample;
s3, calculating gradient values of two adjacent points of the maximum power value and the maximum power value according to the position of the maximum power value, and calculating the absolute value of the ratio of the gradient values of the two adjacent points; if the maximum power value is at two ends of the sample point data, the gradient value is not calculated;
s4, setting a high SNR threshold value and an interval step, and traversing all the intervals higher than the high SNR threshold value, such as: the threshold value of the high signal-to-noise ratio is 10dB, the range of the high signal-to-noise ratio is 10-30 dB, traversal is carried out by using an interval step, and if the interval step is 2dB, the traversed objects are as follows: 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 dB. Acquiring linear values of noise estimation under the condition of different system bandwidths, selecting a group of linear values with the largest noise estimation error as an optimization coefficient table, and acquiring noise power normalization coefficients with different signal-to-noise ratios and different time delays;
multiplying the absolute value of the gradient value ratio obtained in the step S3 by half of the number of all time delays, and taking the result as an index value of the time delay dimension in the optimization coefficient table;
s5, finding out the index value of signal-to-noise ratio dimension in the optimization coefficient table according to the maximum value of the time domain signal power found in the step S1;
s6, obtaining a noise power normalization coefficient under the current time delay of the current signal-to-noise ratio according to the step S4 and the step S5, judging whether the current signal-to-noise ratio is in the traversal interval, if so, interpolating the noise power normalization coefficient to obtain an interpolated optimization coefficient, otherwise, turning to S7;
the interpolation steps are as follows:
according to the maximum power value, calculating the difference value of the signal-to-noise ratios taking the interval traversed values as intervals and 2 adjacent values, and calculating the signal-to-noise ratio difference value ratio between the maximum power value and the signal-to-noise ratio which is smaller than the signal-to-noise ratio taking the interval traversed values as intervals; and searching for the difference between the maximum power value and the noise power normalization coefficient corresponding to the adjacent 2 signal-to-noise ratios taking the interval traversal value as the interval in the optimization coefficient table as a noise power difference value, and adding the product of the signal-to-noise ratio difference value ratio and the noise power difference value to the noise power normalization coefficient corresponding to the small signal-to-noise ratio closest to the maximum power value in the optimization coefficient table under the same time delay to serve as the interpolated optimization coefficient.
And S7, calculating the power average value of the users without PUCCH configuration as PUCCH noise power, multiplying the estimated noise power by the optimization coefficient to obtain an optimized noise power estimated value, and obtaining the optimized PUCCH SINR estimated value by utilizing the ratio of the signal power to the noise power.
The method carries out channel estimation and signal-to-interference-and-noise ratio estimation on a time domain, converts the extracted PUCCH frequency domain data into time domain data through fast inverse Fourier transform of a sample point, and estimates the signal-to-interference-and-noise ratio through the time domain data of the sample point.
The optimization algorithm for estimating the SINR of the PUCCH solves the problem of inaccurate noise estimation caused by channel time delay under the condition of high signal-to-noise ratio, thereby improving the SINR estimation precision and improving the PUCCH performance.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
Claims (2)
1. An optimization algorithm for PUCCH SINR estimation is characterized by comprising the following steps:
s1, converting the received PUCCH frequency domain data into time domain data after fast Fourier inverse transformation of sample points;
s2, calculating the time domain signal power value of the sample point, finding the maximum value of the time domain signal power value on the orthogonal sequence where the PUCCH user is located and the position index thereof, and estimating the timing deviation of the integral multiple sample;
s3, calculating gradient values of two adjacent points of the maximum power value and the maximum power value according to the position of the maximum power value, and calculating the absolute value of the ratio of the gradient values of the two adjacent points; if the maximum power value is at two ends of the sample point data, the gradient value is not calculated;
s4, setting a high signal-to-noise ratio threshold value and an interval step length, traversing all the signals higher than the high signal-to-noise ratio threshold value by the interval step length to obtain linear values of noise estimation under different system bandwidth conditions, selecting a group of linear values with the largest noise estimation error as an optimization coefficient table, and obtaining noise power normalization coefficients with different signal-to-noise ratios and different time delays;
multiplying the absolute value of the gradient value ratio obtained in the step S3 by half of the number of all time delays, and taking the result as an index value of the time delay dimension in the optimization coefficient table;
s5, finding out the index value of signal-to-noise ratio dimension in the optimization coefficient table according to the maximum value of the time domain signal power found in the step S2;
s6, obtaining a noise power normalization coefficient under the current time delay of the current signal-to-noise ratio according to the step S4 and the step S5, judging whether the current signal-to-noise ratio is in the traversal interval, if so, interpolating the noise power normalization coefficient to obtain an interpolated optimization coefficient, otherwise, turning to S7;
and S7, calculating the power average value of the users without PUCCH configuration as PUCCH noise power, multiplying the estimated noise power by the optimization coefficient to obtain an optimized noise power estimated value, and obtaining the optimized PUCCH SINR estimated value by utilizing the ratio of the signal power to the noise power.
2. The optimization algorithm for PUCCH SINR estimation according to claim 1, wherein the step of interpolation in S6 is as follows:
according to the maximum power value, calculating the difference value of the signal-to-noise ratios taking the interval traversed values as intervals and 2 adjacent values, and calculating the signal-to-noise ratio difference value ratio between the maximum power value and the signal-to-noise ratio which is smaller than the signal-to-noise ratio taking the interval traversed values as intervals; and searching for the difference between the maximum power value and the noise power normalization coefficient corresponding to the adjacent 2 signal-to-noise ratios taking the interval traversal value as the interval in the optimization coefficient table as a noise power difference value, and adding the product of the signal-to-noise ratio difference value ratio and the noise power difference value to the noise power normalization coefficient corresponding to the small signal-to-noise ratio closest to the maximum power value in the optimization coefficient table under the same time delay to serve as the interpolated optimization coefficient.
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| CN113271279A (en) * | 2021-05-14 | 2021-08-17 | 成都爱瑞无线科技有限公司 | High-precision detection method for random access channel of narrow-band Internet of things |
| CN116156611A (en) * | 2023-04-21 | 2023-05-23 | 三维通信股份有限公司 | SINR estimation method, device, system and storage medium for PUCCH |
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