CN108521311B - Signal-to-noise ratio estimation method based on Gray sequence - Google Patents

Signal-to-noise ratio estimation method based on Gray sequence Download PDF

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CN108521311B
CN108521311B CN201810371002.XA CN201810371002A CN108521311B CN 108521311 B CN108521311 B CN 108521311B CN 201810371002 A CN201810371002 A CN 201810371002A CN 108521311 B CN108521311 B CN 108521311B
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CN108521311A (en
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吕启福
李帅
罗志刚
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Chengdu Jiwei Technology Co ltd
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Abstract

The invention belongs to the technical field of wireless communication, and relates to a signal-to-noise ratio estimation method based on a Gray sequence. The invention mainly comprises that a transmitter transmits a channel estimation sequence CEF containing a complementary Gray sequence, and the CEF and a local complementary Gray sequence are respectively added in a correlation way at a receiving end to obtain a group of new sequences. Separating the sequence in time domain to obtain the corresponding noise-only sequence and noise-containing channel estimation sequence, finally calculating the power of the two sequences, and then scaling the two sequences appropriately to obtain the signal-to-noise ratio in the actual system. The invention has the advantages that the signal-to-noise ratio estimation can be directly carried out by using the channel estimation sequence CEF in the frame structure without modifying the frame structure, thereby reducing the system cost; meanwhile, the method has the advantages of small calculated amount, high accuracy and capability of being used for signal-to-noise ratio estimation of an actual system.

Description

Signal-to-noise ratio estimation method based on Gray sequence
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a signal-to-noise ratio estimation method based on a Gray sequence.
Background
The signal-to-noise ratio information is very important in a wireless communication system, and accurate estimation of the signal-to-noise ratio has very important significance for ensuring that the wireless communication system approaches to ideal performance. For example, the code division multiple access system utilizes the signal-to-noise ratio information to carry out power distribution of each link; the adaptive modulation system selects a modulation mode or a coding rate according to the signal-to-noise ratio; in MMSE equalization of block transmission (OFDM and SC-FDE) systems, it is also necessary to design a corresponding frequency domain equalizer according to accurate snr information to improve system performance.
Unfortunately, in practical communication systems, the received signal-to-noise ratio information is unknown, and the signal-to-noise ratio value must be estimated from the received signal to obtain the signal-to-noise ratio information. Signal-to-noise ratio estimation methods can be mainly divided into two main categories: one is blind estimation method, such as second order fourth moment method, symbol autocorrelation method, etc.; another class is pilot-based data-aided methods, etc. However, the blind estimation method has a complex algorithm and a slow convergence speed, while the data-aided method can obtain a more accurate estimation with less data, but the pilot-based data-aided method is susceptible to the influence of channel frequency selectivity and has poor performance under the frequency selective channel. Aiming at the defects of the method, the improved signal-to-noise ratio estimation method is provided according to the frame format in the 802.11ad standard, the method does not need to modify the frame structure, and can directly use the channel estimation sequence CEF in the frame structure to carry out signal-to-noise ratio estimation, thereby reducing the system cost; meanwhile, the method has the advantages of small calculated amount, high accuracy and capability of being used for signal-to-noise ratio estimation of an actual system.
Disclosure of Invention
The invention aims to provide a signal-to-noise ratio estimation method based on a Gray sequence. Complementary Gray sequences and the sum function of the autocorrelation functions of the complementary Gray sequences are Dirac-Delta functions, and the time-domain impulse response of a channel can be estimated through a channel estimation sequence by utilizing the property, so that the signal-to-noise ratio in a practical system can be conveniently calculated. The method for estimating the signal-to-noise ratio by utilizing the property of the complementary Gray sequence greatly simplifies the complexity of the traditional method for estimating the signal-to-noise ratio. Specifically, the transmitter transmits a channel estimation sequence CEF including a complementary golay sequence, and the correlation addition is performed on the channel estimation sequence CEF and the local complementary golay sequence at the receiving end to obtain a new set of sequences. Separating the sequence in time domain to obtain the corresponding noise-only sequence and noise-containing channel estimation sequence, finally calculating the power of the two sequences, and then scaling the two sequences appropriately to obtain the signal-to-noise ratio in the actual system.
For ease of understanding, the signal properties of the complementary golay sequences used in the present invention will first be described.
Complementary golay sequences a and b, defined with a total length N, satisfy the following properties:
Figure GDA0001683428880000021
wherein R isa(u) and Rb(u) are the autocorrelation functions of the golay sequences a and b, respectively.
The technical scheme of the invention is as follows:
s1, the transmitter sends a pair of Gray sequences a, b, and the receiving sequences are obtained through a multipath channel and additive white Gaussian noise, wherein the receiving sequences are respectively as follows:
Figure GDA0001683428880000022
wherein v is1(n) and v2(n) is additive white noise with the same noise power; the complementary golay sequences may be transmitted separately at the same time, or may be transmitted together to form a channel estimation sequence CEF.
S2, channel estimation is carried out:
using local Gray sequences a and b to respectively receive the sequence ainAnd binPerforming correlation operation, and adding the correlation operation results to obtain a channel estimation sequence h2(n) when the length of the channel estimation sequence is twice the length of the golay sequence a;
s3, noise power calculation is carried out:
calculating the average power of the first half part of the channel estimation sequence, wherein the average power is the noise power; separating the sequence in time domain to obtain the corresponding noise-only sequence part and the noise-containing channel estimation sequence part;
s4, calculating the signal power:
because the noise is stationary noise, the noise power of the first half of the sequence is equal to the noise power of the second half of the sequence, the total energy of the noise of the second half of the channel estimation sequence is subtracted from the total energy of the noise of the second half of the channel estimation sequence to obtain the total energy of the channel in the absence of the noise, and the power of the signal is obtained according to the positive correlation relationship between the total energy of the channel in the absence of the noise and the power of the transmitted signal; let the length of the noise-only sequence be n1Obtaining the average power of noise; carrying out power calculation on the channel estimation sequence containing the noise to obtain the total signal power PaThen the signal power is Ps＝Pa-n2PnWherein n is2Estimating a sequence length for a noisy channel;
s5, calculating the signal-to-noise ratio:
carrying out division operation on the estimated signal power and the estimated noise power to obtain a signal-to-noise ratio;
calculating SNR (signal-to-noise ratio) < alpha x Ps/Pn. Where α is a scaling factor, the specific value is related to the length of the gray sequence and the number of additions performed.
S6, correcting the signal-to-noise ratio:
because the signal-to-noise ratio is improved by carrying out correlation operation on the signal-to-noise ratio and the multiple of the improved signal-to-noise ratio is related to the length of the gray sequence, the estimated signal-to-noise ratio is scaled to obtain the corrected signal-to-noise ratio.
The method has the advantages that the method does not need to modify the frame structure, can directly use the channel estimation sequence CEF in the frame structure to carry out signal-to-noise ratio estimation, and reduces the system cost; meanwhile, the method has the advantages of small calculated amount, high accuracy and capability of being used for signal-to-noise ratio estimation of an actual system.
Drawings
FIG. 1 is a block diagram of the present invention for calculating noise power using complementary Gray sequence pairs;
FIG. 2 shows the result of the correlation sum of complementary Gray sequence pairs in a noise-free single-path channel;
fig. 3 shows the result of the correlation sum of complementary golay sequence pairs in a single-path channel with SNR of 10 db;
FIG. 4 shows the result of the correlation sum of complementary Gray sequence pairs under a noise-free multipath channel;
fig. 5 shows the correlation sum of complementary golay sequence pairs in a multipath channel with SNR of 18 db.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of a typical signal-to-noise ratio estimation using complementary gray sequence pairs. The main structure is a Gray sequence correlator and a sequence separator part. Wherein, the values of alpha and beta are constant coefficients, and the values of the constant coefficients are related to the selection of a specific gray sequence pair. Assuming that the selected gray sequence pair is Ga128 and Gb128, the signal processing flow of the system is:
a sending end: and transmitting Ga128 gray sequences and Gb128 gray sequences respectively under the same channel environment. By using the property of complementary golay sequence pair, the maximum channel symbol length that can be estimated by the sequence is 128 symbols.
Receiving end: and carrying out correlation operation on the received noise-containing gray sequence pair and local gray sequences Ga128 and Gb128 respectively, and then adding the two correlated sequences to obtain a new 256-length sequence y (n).
Assuming that the channel being traversed is a single path AWGN channel, the resulting new 256-length sequence, in the absence of noise, has the first 128 symbol points all being zeros. When the SNR is 10db, the first 128 points of y (n) are all noise.
Assuming that the channel being traversed is a multipath channel and the multipath channel length is less than 64 symbol lengths, the 64 symbol points in the left box are still zeros and the 64 symbol points in the right box are the channel values we have estimated in the absence of noise. At a signal-to-noise ratio SNR of 18db, the 64 symbol points in the left box are all noise and the 64 symbol points in the right box are estimated noisy channel values.
Dividing the sequence y (n) with the length of 256 into two parts, wherein one part takes a part from 64 symbols to 128 symbols in y (n), and is marked as y1The other part takes 129 symbols in y (n) to 192 parts, which are marked as y2。
Calculating the sequence y1Total energy P of1So that the average power of the noise is Pn＝P1/64。
Calculating the sequence y2Total energy P of2So that the estimated noiseless channel energy is Ph＝P2-αP1Where α represents the ratio of the length of the sequence chosen to be divided into two parts.
Since the estimated noiseless channel energy is in one-to-one correspondence with the average power of the transmitted signal, the average power of the signal is Psignal＝Ph。
The estimated SNR is calculated as
Figure GDA0001683428880000041
Beta is a constant, a specific value andthe correlation operations are performed. Regarding the value of β, for example, making one Ga128 correlation (equivalent to adding 128 times in shift), the signal amplitude becomes 128 times the original one, and the signal power increases 1282 times the original one, which is equivalent to increasing
Figure GDA0001683428880000042
The average power of the noise is related by Ga128 once (equivalent to 128 times of addition), the average Gaussian noise is changed to 128 times of the original average power, so the noise power is increased correspondingly
Figure GDA0001683428880000043
Therefore, it can be found that the signal-to-noise ratio can be significantly improved by performing a correlation operation, and the total improvement factor is denoted as β.

Claims (2)

1. A signal-to-noise ratio estimation method based on Gray sequences is characterized by comprising the following steps:
s1, the transmitter sends a pair of Gray sequences a, b, and the receiving sequences are obtained through a multipath channel and additive white Gaussian noise, wherein the receiving sequences are respectively as follows:
Figure FDA0002680559730000011
wherein v is1(n) and v2(n) additive white gaussian noise with the same noise power;
s2, channel estimation is carried out:
using local Gray sequences a and b to respectively receive the sequence ainAnd binPerforming correlation operation, and adding the correlation operation results to obtain a channel estimation sequence h2(n) when the length of the channel estimation sequence is twice the length of the golay sequence a;
s3, noise power calculation is carried out:
calculating the average power of the first half part of the channel estimation sequence, wherein the average power is the noise power;
s4, calculating the signal power:
because the noise is stationary noise, the noise power of the first half of the sequence is equal to the noise power of the second half of the sequence, the total energy of the noise of the second half of the channel estimation sequence is subtracted from the total energy of the noise of the second half of the channel estimation sequence to obtain the total energy of the channel in the absence of the noise, and the power of the signal is obtained according to the positive correlation relationship between the total energy of the channel in the absence of the noise and the power of the transmitted signal;
s5, calculating the signal-to-noise ratio:
carrying out division operation on the estimated signal power and the estimated noise power to obtain a signal-to-noise ratio;
s6, correcting the signal-to-noise ratio:
and scaling the estimated signal-to-noise ratio according to the length of the Gray sequence to obtain a corrected signal-to-noise ratio.
2. The method of claim 1, wherein the length of the golay sequence is 2 times of the required estimated channel length.
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CN104202285A (en) * 2014-08-26 2014-12-10 江苏中兴微通信息科技有限公司 Low-PAPR sequence designing method for wireless communication system
CN104917711A (en) * 2015-05-31 2015-09-16 电子科技大学 Phase noise compensation improved method under wireless communication system
CN106464630A (en) * 2014-06-30 2017-02-22 华为技术有限公司 Training sequence generation device, apparatus and method

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US10075224B2 (en) * 2016-05-04 2018-09-11 Intel IP Corporation Golay sequences for wireless networks
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CN104202285A (en) * 2014-08-26 2014-12-10 江苏中兴微通信息科技有限公司 Low-PAPR sequence designing method for wireless communication system
CN104917711A (en) * 2015-05-31 2015-09-16 电子科技大学 Phase noise compensation improved method under wireless communication system

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