CN110535546B - Sliding cross-correlation frame detection method based on sparse multipath perception - Google Patents

Sliding cross-correlation frame detection method based on sparse multipath perception Download PDF

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CN110535546B
CN110535546B CN201910663066.1A CN201910663066A CN110535546B CN 110535546 B CN110535546 B CN 110535546B CN 201910663066 A CN201910663066 A CN 201910663066A CN 110535546 B CN110535546 B CN 110535546B
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CN110535546A (en
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张渭乐
韩璐
穆鹏程
肖海涛
王文杰
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Xian Jiaotong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
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Abstract

A sliding cross-correlation frame detection method based on sparse multipath perception is characterized in that sliding cross-correlation processing is conducted on a pilot frequency sequence LTF and a received signal to obtain a frame detection metric value, when the frame detection metric value is larger than a detection threshold, a frame is considered to arrive, and otherwise, the frame does not arrive. In a non-line-of-sight communication environment, due to the influence of multipath transmission, a plurality of signals reaching a receiver at intervals of a certain time delay are provided, the invention provides a method based on sparse multipath perception in a maximum time delay expansion window of a communication channel in the non-line-of-sight environment, a proper decision threshold is selected according to the characteristic that the mean values of frame detection metric values at multipath reaching positions and noise positions are different, multipath reaching signals and noise signals are distinguished, only energy at multipath reaching ideal signal points is collected in the selected window for frame reaching detection, and the system frame detection performance in the non-line-of-sight communication environment is improved.

Description

Sliding cross-correlation frame detection method based on sparse multipath perception
Technical Field
The invention belongs to the field of wireless communication, and relates to a sliding cross-correlation frame detection method based on sparse multipath perception.
Background
In today's society, the convenience of wireless communication has enabled the use of a number of advanced technologies, which are ubiquitous in everyday society. Non line of sight (NLOS) transmission is a typical application scenario in wireless communications. Electromagnetic transmission in wireless communication systems has two modes, line-of-sight (LOS) transmission and non-line-of-sight (NLOS) transmission. NLOS propagation refers to a propagation mode in which no direct path exists between the transmitting end and the receiving end, and a transmitted signal reaches the receiving end through multiple paths under the actions of reflection, refraction, diffraction, scattering, diffraction and the like of an obstacle. NLOS propagation subjects the received signal to severe frequency selective fading, which greatly affects the improvement of the broadband wireless transmission rate and quality. Therefore, it is of great significance to study NLOS-related technologies.
As the name implies, the frame arrival detection technique is to detect whether a signal frame arrives and determine the approximate position of the frame header. Frame arrival detection is the first step in the communication and its importance is visible. In a non-line-of-sight communication scene, there are adverse factors such as multipath interference and external noise, and due to the multipath interference, there are a plurality of signals arriving at a receiver with a certain time delay. Therefore, the frame detection technology is researched in a non-line-of-sight environment, whether the signal reaches the receiver or not is accurately judged, the performance of a communication system can be improved, and the method has important significance.
Disclosure of Invention
The invention aims to provide a sliding cross-correlation frame arrival detection method based on sparse multipath perception in a non-line-of-sight communication environment.
In order to achieve the purpose, the invention adopts the following technical scheme:
a sliding cross-correlation frame detection method based on sparse multipath perception calculates a frame detection metric value according to a pilot frequency sequence LTF and a receiving signal, when the frame detection metric value is larger than a detection threshold, the frame is considered to arrive, otherwise, the frame does not arrive.
A further improvement of the present invention is that the specific process of calculating the frame detection metric value according to the pilot sequence LTF and the received signal is as follows:
the method comprises the following steps: performing sliding cross-correlation processing on the pilot frequency sequence LTF and the received signal, and solving a modular square of a result after the sliding cross-correlation; in a selected window, distinguishing multipath arrival signals and noise through a sparse perception algorithm, and summing the modulus squares of the multipath arrival signals to obtain a molecule of a frame detection metric value;
step two: the product of the LTF of the pilot frequency sequence and the energy of the received signal is the denominator of the frame detection metric value;
step three: the numerator of the component frame detection metric value is divided by the denominator of the frame detection metric value to obtain a frame detection metric value.
The invention has the further improvement that in the step one, when the multipath arriving signal and the noise are distinguished by the sparse perception algorithm, if the square of the mode is smaller than the judgment threshold, the signal is regarded as the noise, and if the square of the mode is larger than the judgment threshold, the signal is regarded as the multipath arriving signal.
The invention has the further improvement that the specific process of the step one is as follows:
at the receiving end, assume the receiving end noise power is
Figure BDA0002139193950000021
The received signal power is expressed as
Figure BDA0002139193950000022
hlFor channel time-domain impulse response, LchFor the maximum possible channel delay spread, i.e. the selected window size, the total power of the received signal y is the noise power
Figure BDA0002139193950000023
And received signal power
Figure BDA0002139193950000024
Sum sigma2Expressed as:
Figure BDA0002139193950000025
signal to noise ratio
Figure BDA0002139193950000026
Order sequence
Figure BDA0002139193950000027
Represents a pilot sequence LTF; wherein L isLTFIs the length of the pilot sequence LTF;
assuming transmit symbol power normalization; at the receiving end, L starting with dLTFThe received signal sequence of the point is noted as:
Figure BDA0002139193950000028
modulus square of sliding cross correlation result of pilot frequency sequence LTF and receiving signal
Figure BDA0002139193950000029
Comprises the following steps:
Figure BDA00021391939500000211
wherein d is the starting point of the received signal y; l is a variable; the variable L has a value in the range of 0, 1, 2,, Lch；
When channel time domain impulse response hlWhen the path is 0, that is, when there is no path but noise:
Figure BDA00021391939500000210
the multipath arrival signals and noise are distinguished through a sparse perception algorithm:
Figure BDA0002139193950000031
wherein the content of the first and second substances,
Figure BDA0002139193950000032
for signals received starting at d at LLTFMaximum value of energy within length;
defining the index set psi of the multipath identification, if
Figure BDA0002139193950000033
The decision threshold is
Figure BDA0002139193950000034
Given a parameter eta>1, considering that only noise exists in unaligned signal components in the signals which take the starting point d of the received signals as the starting point; otherwise, if the signal component exists in the signal which takes the starting point d of the received signal as the start, adding the starting point d of the received signal into the index set psi of the multipath identification;
the numerator of the frame detection metric is:
Figure BDA0002139193950000035
the invention is further improved in that the specific process of the step two is: the energy of the pilot sequence LTF is the length of the pilot sequence LTF, and the length of the pilot sequence LTF is compared with the energy (y) of the received signalH(d) y (d)) as the denominator of the frame detection metric value, the denominator of the frame detection metric value being: l isLTF×(yH(d) y (d), wherein y (d) is the received signal, yH(d) The conjugate transpose of the received signal y (d).
The invention is further improved in that the metric value G of the frame detectionCS(d) Write as:
Figure BDA0002139193950000036
when there is a signal, consider LpThe taps are randomly distributed in [0, L ]ch]To (c) to (d); expectation of frame detection metric EG assuming correct multipath location identificationCS(d)]Comprises the following steps:
Figure BDA0002139193950000037
if there is no signal, only noise, then there are:
Figure BDA0002139193950000038
selecting a parameter η>1, such that for all d there is
Figure BDA0002139193950000039
So that in the absence of signal, the frame detection metric value GCS(d)＝0。
Compared with the prior art, the invention has the following beneficial effects: in a non-line-of-sight communication environment, the invention provides a sliding cross-correlation frame arrival detection method based on sparse perception. That is, at the receiving end, a window is considered, and the window length is the maximum possible channel delay spread value of the communication channel. And selecting a proper decision threshold in the window to distinguish the multipath arriving signals from the noise signals, and only collecting the multipath arriving signal energy as the basis for calculating the frame detection metric value in the selected window without considering the noise signals. And then, a proper detection threshold is selected for frame arrival detection, so that multipath components of signals arriving at a receiving end due to multipath transmission in a non-line-of-sight communication environment are not lost, and the system frame arrival detection performance in the non-line-of-sight environment is improved.
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FIG. 1 is a block diagram of a non-line-of-sight communication environment system model employed in the present invention;
FIG. 2 is a signal frame structure diagram employed by the present invention;
FIG. 3 is a performance diagram of the present invention with sparse multipath sensing versus no sparse multipath sensing.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings.
A sliding cross-correlation frame detection method based on sparse multipath perception is based on the basic idea that a frame detection metric value is calculated, a proper detection threshold is set, when the frame detection metric value is larger than the detection threshold, a frame is considered to arrive, and otherwise, the frame does not arrive. The frame detection measurement value is in a fractional form, and the molecular calculation mode is to perform sliding cross-correlation processing on the pilot frequency sequence LTF and the received signal and then calculate the square of the mode of the result after the sliding cross-correlation. In a selected window, the multipath arrival signals and noise are distinguished through a sparse perception algorithm, only the modulo squares of the sliding cross-correlation result of the multipath arrival signals are summed, and the denominator is the product of the pilot frequency sequence LTF and the energy of the received signals. Specifically, the method comprises the following steps:
the method comprises the following steps: calculating the numerator of the frame detection metric:
the invention calculates the numerator of the frame detection metric value based on the sparse multipath perception method: collecting pilot frequency and receiving signals, solving the square of the sliding cross-correlation result of the pilot frequency sequence LTF and the receiving signals, distinguishing multipath arrival signals and noise by the sparse sensing method of the invention in a selected window, if the square of the sliding cross-correlation result is less than a certain decision threshold, considering the signals as noise and discarding the corresponding values, and if the square of the sliding cross-correlation result is more than a certain decision threshold, considering the signals as multipath arrival signals; within a selected window, only the modulo squares of the sliding cross-correlation results of the multipath arriving signals are summed to obtain the numerator of the frame detection metric.
The specific process is as follows:
at the receiving end, assume the receiving end noise power is
Figure BDA0002139193950000051
The received signal power may be expressed as
Figure BDA0002139193950000052
hlFor channel time-domain impulse response, LchThe maximum possible channel delay spread, i.e., the selected window size. The total power of the received signal y is the sum sigma of the noise power and the received signal power2It can be expressed as:
Figure BDA0002139193950000053
signal to noise ratio of
Figure BDA0002139193950000054
Order sequence
Figure BDA0002139193950000055
Indicating the pilot sequence LTF. Wherein L isLTFIs the length of the pilot sequence LTF.
Assuming transmit symbol power normalization, L starting with d will be received at the receiving endLTFThe received signal sequence of the point is noted as:
Figure BDA0002139193950000056
modulo square of the result of sliding cross-correlation of the pilot sequence LTF with the received signal
Figure BDA0002139193950000057
Comprises the following steps:
Figure BDA0002139193950000058
wherein the content of the first and second substances,d is the starting point of the received signal y. L is a variable with a value range of 0, 1, 2,, Lch。
When channel time domain impulse response hlWhen the path is 0, that is, when there is no path but noise:
Figure BDA0002139193950000059
the multipath arrival signals and noise are distinguished by a sparse perception method:
Figure BDA00021391939500000510
wherein the content of the first and second substances,
Figure BDA00021391939500000511
for signals received starting at d at LLTFThe maximum of the energy in the length.
Defining the index set psi of the multipath identification, if
Figure BDA00021391939500000512
The decision threshold is
Figure BDA00021391939500000513
Given a parameter eta>1, it is considered that only noise exists for a signal component which is not aligned in a signal starting from the starting point d of the received signal. Otherwise, the signal component is considered to exist in the signal which takes the starting point d of the received signal as the starting point, and the starting point d of the received signal is added into the index set psi of the multipath identification.
For received signal sequence starting from d, the result of sliding cross-correlation is squared modulo and then within a certain selected window LchAnd selecting a proper decision threshold to distinguish the multipath arrival signals from the noise. Only multipath arrival signals of which the modulus square of the sliding cross-correlation result is larger than the decision threshold are summed, the sum value is a numerator of the frame detection metric value, and multipath arrival signal components are added into the set psi.
The numerator of the frame detection metric is:
Figure BDA0002139193950000061
step two: calculating the denominator of the frame detection metric value: the energy of the pilot sequence LTF is the length of the pilot sequence LTF and is a fixed value. The denominator is calculated by multiplying the energy of the received signal y by the pilot sequence LTF, and the energy of the received signal y starting with d is (y)H(d) y (d)), the length of the pilot sequence LTF and the energy (y) of the received signalH(d) y (d)) as the denominator of the frame detection metric.
The denominator of the frame detection metric is: l isLTF×(yH(d) y (d), wherein y (d) is the received signal, yH(d) The conjugate transpose of the received signal y (d).
Step three: a frame detection metric value is calculated and compared to a detection threshold.
And (4) dividing the numerator calculated in the step one and the denominator calculated in the step two to form a frame detection metric value, selecting a proper detection threshold, and if the frame detection metric value is greater than the detection threshold, determining that the frame arrives, otherwise, determining that the frame does not arrive.
Metric value G of frame detectionCS(d) Write as:
Figure BDA0002139193950000062
when there is a signal, consider LpThe taps are randomly distributed in [0, L ]ch]In the meantime. Expectation of frame detection metric EG assuming correct multipath location identificationCS(d)]Comprises the following steps:
Figure BDA0002139193950000063
if there is no signal, only noise, then there are:
Figure BDA0002139193950000071
selecting a suitable parameter eta>1, can be such that there are for all dSo that in the absence of signal, the frame detection metric value is GCS(d)＝0。
Therefore, through multi-path identification, the relative difference of the metric values when signals exist or not is infinitely enlarged, and the detection performance can be improved.
Fig. 1 is a diagram of a non-line-of-sight communication scenario applicable to the present invention, where NLOS transmission causes a received signal at a receiving end to be a superposition of a large number of non-direct waves (mainly reflected waves), and in non-line-of-sight transmission, due to obstruction, the signal received by a receiver also includes signals arriving through paths such as refraction, diffraction, reflection, and the like. Thus, non-line-of-sight transmissions have multipath interference and the received signal has significant rayleigh fading.
Fig. 2 shows the structure of a signal frame used in the present invention, where the signal frame is composed of a training sequence and a load. The training sequence is composed of a pilot frequency sequence LTF, a guard interval UW is also added to the pilot frequency sequence, the load of a signal frame is formed by splicing a data block and the guard interval UW block by block, the data is divided into blocks at a transmitting end, and the guard interval UW is added at the tail. In the subsequent simulation, UW has a length of 64, the pilot sequence LTF is composed of m-sequences having a length of 128, and one data block has a length of 512.
When the sparse multipath perception method is not adopted, the frame detection is carried out in the non-line-of-sight environment, and L is utilizedLTFThe LTF of a point performs a normalized correlation calculation on the received signal. Order to
Figure BDA0002139193950000073
Indicating the pilot sequence LTF. The transmit symbol power normalization is assumed. L will start with d at the receiving endLTFThe received signal sequence of the point is noted as:
Figure BDA0002139193950000074
considering the maximum possible channel delay spread as LchFor a received signal sequence starting with d, the following correlation coefficients are taken as frame detection metric values:
Figure BDA0002139193950000075
the meaning of the numerator is to count the square sum of all the received signals arriving within the maximum channel delay spread window starting with d and the sliding correlation value of the pilot sequence LTF. In a channel environment where the channel delay spread is large but only a few paths are sparse, the energy of much noise is actually collected, and the system frame arrival detection performance is affected. And selecting a proper detection threshold according to the calculated frame detection metric value, considering that the frame arrives when the frame detection metric value is greater than the detection threshold, and if not, continuing moving backwards until finding out a point at which the frame detection metric value is greater than the detection threshold.
The simulation comparison is based on that under the two conditions of sparse perception and no sparse perception, the selected detection threshold ensures that the false alarm probability under the two conditions is basically the same, and then the false alarm probability MDP is compared. The pilot frequency sequence LTF is formed by an m sequence with the length of 128 in simulation, a BPSK modulation mode is adopted, the receiver receives by one antenna, each data block has 512 symbols, wherein the UW length is 64, the pilot frequency sequence LTF length is 128, the detection threshold is set to be 1.7 when sparse sensing is not adopted, the detection threshold is set to be 1.2 when sparse sensing is adopted, the false alarm probability of the pilot frequency sequence LTF and the data block is basically the same, the parameter eta used when ideal signals and noise are distinguished in sparse multipath sensing is 2, the maximum time delay expansion of a channel, namely the window length is 50, 3 paths are randomly distributed in the window length, and fig. 3 is a schematic diagram for comparing the detection performance of sparse multipath sensing and non-sparse multipath sensing in a non-line-of sight communication environment. It can be seen from fig. 3 that after sparse multipath sensing is performed, the false-alarm-missing probability MDP is lower, and the performance of the system is better.
In a non-line-of-sight communication environment, due to the influence of multipath transmission, a plurality of signals reaching a receiver at intervals of a certain time delay are provided, the invention provides a method based on sparse multipath perception in a maximum time delay expansion window of a communication channel in the non-line-of-sight environment, a proper decision threshold is selected according to the characteristic that the mean values of frame detection metric values at multipath reaching positions and noise positions are different, multipath reaching signals and noise signals are distinguished, only energy at multipath reaching ideal signal points is collected in the selected window for frame reaching detection, and the system frame detection performance in the non-line-of-sight communication environment is improved.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (4)

1. A sliding cross-correlation frame detection method based on sparse multipath perception is characterized in that a frame detection metric value is calculated according to a pilot frequency sequence LTF and a received signal, when the frame detection metric value is larger than a detection threshold, a frame is considered to arrive, otherwise, the frame does not arrive;
the specific process of calculating the frame detection metric value according to the pilot sequence LTF and the received signal is as follows:
the method comprises the following steps: performing sliding cross-correlation processing on the pilot frequency sequence LTF and the received signal, and solving a modular square of a result after the sliding cross-correlation; in a selected window, distinguishing multipath arrival signals and noise through a sparse perception algorithm, and summing the modulus squares of the multipath arrival signals to obtain a molecule of a frame detection metric value;
step two: the product of the LTF of the pilot frequency sequence and the energy of the received signal is the denominator of the frame detection metric value; the specific process is as follows: the energy of the pilot sequence LTF is the length of the pilot sequence LTF, and the length of the pilot sequence LTF is compared with the energy (y) of the received signalH(d) y (d)) as the denominator of the frame detection metric value, the denominator of the frame detection metric value being: l isLTF×(yH(d) y (d), wherein y (d) is the received signal, yH(d) Conjugate transpose of received signal y (d);
step three: the numerator of the component frame detection metric value is divided by the denominator of the frame detection metric value to obtain a frame detection metric value.
2. The sliding cross-correlation frame detection method based on sparse multipath sensing as recited in claim 1, wherein in the first step, when the sparse sensing algorithm is used to distinguish multipath arriving signals from noise, if the square of the mode is smaller than the decision threshold, the signals are considered as noise, and if the square of the mode is larger than the decision threshold, the signals are considered as multipath arriving signals.
3. The method for detecting the sliding cross-correlation frame based on the sparse multipath sensing as claimed in claim 1, wherein the specific process of the step one is as follows:
at the receiving end, assume the receiving end noise power is
Figure FDA0002482877230000011
The received signal power is expressed as
Figure FDA0002482877230000012
hlFor channel time-domain impulse response, LchFor the maximum possible channel delay spread, i.e. the selected window size, the total power of the received signal y is the noise power
Figure FDA0002482877230000013
And received signal power
Figure FDA0002482877230000014
Sum sigma2Expressed as:
Figure FDA0002482877230000015
signal to noise ratio
Figure FDA0002482877230000016
Order sequence
Figure FDA0002482877230000017
Represents a pilot sequence LTF; wherein L isLTFIs the length of the pilot sequence LTF;
assuming transmit symbol power normalization; at the receiving end, L starting with dLTFThe received signal sequence of the point is noted as:
Figure FDA0002482877230000021
modulus square of sliding cross correlation result of pilot frequency sequence LTF and receiving signal
Figure FDA0002482877230000022
Comprises the following steps:
Figure FDA0002482877230000023
wherein d is the starting point of the received signal y; l is a variable; the variable L ranges from 0, 1, 2 up to Lch；
When channel time domain impulse response hlWhen the path is 0, that is, when there is no path but noise:
Figure FDA0002482877230000024
the multipath arrival signals and noise are distinguished through a sparse perception algorithm:
Figure FDA0002482877230000025
wherein the content of the first and second substances,
Figure FDA0002482877230000026
for signals received starting at d at LLTFMaximum value of energy within length;
defining the index set psi of the multipath identification, if
Figure FDA0002482877230000027
The decision threshold is
Figure FDA0002482877230000028
Given a parameter η > 1, it is assumed that the signal component corresponding to a signal starting from the starting point d of the received signal is not alignedNoise only; otherwise, if the signal component exists in the signal which takes the starting point d of the received signal as the start, adding the starting point d of the received signal into the index set psi of the multipath identification;
the numerator of the frame detection metric is:
Figure FDA0002482877230000029
4. the method of claim 1, wherein the metric value G of frame detection isCS(d) Write as:
Figure FDA00024828772300000210
when there is a signal, consider LpThe taps are randomly distributed in [0, L ]ch]To (c) to (d); expectation of frame detection metric EG assuming correct multipath location identificationCS(d)]Comprises the following steps:
Figure FDA00024828772300000211
if there is no signal, only noise, then there are:
Figure FDA0002482877230000031
the parameter eta > 1 is chosen such that for all d there is
Figure FDA0002482877230000032
So that in the absence of signal, the frame detection metric value GCS(d)＝0。
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