CN103856972B - Broader frequency spectrum cognitive method and device and corresponding user equipment and base station - Google Patents
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Abstract
Include the user equipment according to device of the present invention and base station the present invention relates to a kind of broader frequency spectrum cognitive method detected based on Sub-nyquist sampling and cycle characteristics and device and applied in cordless communication network.It the described method comprises the following steps：Input signal is converted to the first output signal through Sub-nyquist sampling module；The first circulation cross-spectral density vector or first circulation cross correlation function vector of first output signal are calculated by recycling cross spectrum density or recycling cross correlation function generator；Using sparse signal recovery technology according to the first circulation cross-spectral density is vectorial or the first circulation cross correlation function vector calculate the input signal the signal after frequency spectrum shift and LPF second circulation cross-spectral density vector or second circulation cross correlation function vector；And the second circulation cross-spectral density is vectorial or the second circulation cross correlation function vector handles through thresholding and draws occupied sub-band.
Description
Technical Field
The invention relates to a wireless network technology, in particular to a broadband spectrum sensing method and device based on Nyquist sampling and cyclic characteristic detection, and corresponding user equipment and a base station.
Background
Nowadays, Cognitive Radio (CR) is gaining more and more general attention due to its ability to more efficiently utilize limited spectrum resources. A key technology for implementing cognitive radio is wideband spectrum detection: cognitive radio users need to identify free sub-bands for their use from a large bandwidth range. The traditional broadband spectrum detection technology is provided with a group of adjustable narrow-band-pass filters to identify the occupation condition of each sub-band, and the method can only identify whether one sub-band is occupied at a time, so that a larger time delay is brought to the spectrum detection process. In order to detect whether a plurality of sub-bands are occupied at a time, one method is to set a plurality of narrow-band radio frequency channels, which brings huge development cost and increased system complexity to the system. Another approach is to use a wideband radio channel to process the signal over the entire bandwidth. This method requires a sampling frequency of more than 2 times the entire bandwidth, according to the nyquist away. The cognitive radio usually needs to detect a wide bandwidth, so the required sampling frequency is very high, and high-speed analog-to-digital conversion and digital signal processing equipment must be used, which has a negative effect on the cost of system implementation.
To avoid the adverse effects of high sampling rates resulting from satisfying the nyquist theorem, under-nyquist sampling techniques based on Compressed Sensing (CS) are good choices. This technique is based on the fact that: the occupancy of the broadband spectrum is typically sparse. With compressed sensing techniques, the sampling rate required for sparse signal detection can be much lower than the nyquist sampling rate. Therefore, the compressed sensing can remarkably reduce the sampling rate of the broadband spectrum detection, thereby greatly reducing the requirements on analog-to-digital conversion and digital signal processing equipment. Among the under-nyquist sampling techniques based on compressed sensing, the Modulated Wideband Converter (MWC) technique is the most excellent one. As shown in fig. 1, the input signal is processed by the modulated wideband converter to generate a set of narrowband signals that can be sampled at a lower sampling rate. Under ideal conditions (without noise influence), the original sparse broadband signal can be recovered from the narrow-band signal output by the modulation broadband converter, so that broadband spectrum sensing under the under-nyquist sampling rate is realized.
However, in practical systems, the signal-to-noise ratio of the output signal is much smaller than that of the input signal because the input signal inevitably carries noise such as white gaussian noise, and the noise is accumulated due to the spectral superposition of the modulated wideband converter. In order not to interfere with primary users, cognitive radio users typically need to have the ability to identify very weak primary user signals. When the primary user signal is very weak, the noise accumulation effect of the modulated wideband converter will further worsen the situation, making the primary user signal in the output signal completely drowned in the noise signal and thus rendered unrecognizable. Therefore, the modulated wideband converter technique becomes unable to work properly in practical applications due to the accumulation of noise.
Disclosure of Invention
In light of the foregoing understanding of the background and the problems with the prior art, the present invention is directed to a method and apparatus for sensing a wideband spectrum based on sub-nyquist sampling and cyclic characteristic detection, which is a technique for effectively detecting a wideband spectrum by using a modulated wideband converter to take advantage of its sub-nyquist sampling while removing the noise superposition effect of the modulated wideband converter.
According to a first aspect of the present invention, a broadband spectrum sensing method based on nyquist under-sampling and cyclic characteristic detection is provided, which includes:
a. converting an input signal into a first output signal through a Nyquist sampling module;
b. calculating, by a cyclic cross spectral density or cyclic cross correlation function generator, a first cyclic cross spectral density vector or a first cyclic cross correlation function vector of the first output signal;
c. calculating a second cyclic cross spectral density vector or a second cyclic cross correlation function vector of the input signal after the frequency spectrum shifting and the low-pass filtering according to the first cyclic cross spectral density vector or the first cyclic cross correlation function vector by using a sparse signal recovery technology; and
d. and thresholding the second cyclic cross spectral density vector or the second cyclic cross correlation function vector to obtain an estimate of the occupied sub-band.
In one embodiment, the sub-nyquist sampling module is a modulated wideband converter. A Modulated Wideband Converter (Modulated Wideband Converter) is a form of under-nyquist sampling technique that has the advantages of low computational complexity and robustness to model mismatch and hardware imperfections.
In one embodiment, the signal-to-noise ratio of the input signal is below-3 dB. The method of the invention works well even at signal to noise ratios of-10 dB or even lower.
According to a second aspect of the present invention, a broadband spectrum sensing apparatus based on nyquist under-sampling and cyclic characteristic detection is provided, which includes:
a sub-nyquist sampling module for converting an input signal into a first output signal;
a cyclic cross spectral density or cyclic cross correlation function generator for calculating a first cyclic cross spectral density vector or a first cyclic cross correlation function vector of the first output signal;
a sparse signal recovery module, configured to calculate, by using a sparse signal recovery technique, a second cyclic cross spectral density vector or a second cyclic cross correlation function vector of the input signal after the frequency spectrum shifting and low-pass filtering according to the first cyclic cross spectral density vector or the first cyclic cross correlation function vector; and
a thresholding module for estimating occupied sub-bands from the second cyclic cross-spectral density vector or the second cyclic cross-correlation function vector.
In one embodiment, the sub-nyquist sampling module is a modulated wideband converter. A modulated wideband converter is a form of under-nyquist sampling technique that has the advantages of low computational complexity and robustness to model mismatch and hardware imperfections.
In one embodiment, the signal-to-noise ratio of the input signal is below-3 dB. The method of the invention works well even at signal to noise ratios of-10 dB or even lower.
According to a third aspect of the present invention, a user equipment in a wireless communication network is provided, the user equipment comprises the wideband spectrum sensing apparatus based on the undersnyquist sampling and the cyclic characteristic detection according to the second aspect of the present invention.
According to a fourth aspect of the present invention, there is provided a base station in a wireless communication network, the base station comprising the wideband spectrum sensing apparatus based on the sub-nyquist sampling and the cyclic characteristic detection according to the second aspect of the present invention.
Generally speaking, the invention provides a broadband spectrum sensing technology working under the under-nyquist sampling rate, and the technology has the advantages of low sampling rate, high accuracy, low computation complexity, robustness to factors such as hardware nonideal and the like, can normally work under the environment of low signal-to-noise ratio, and has strong practicability. In addition, the invention effectively solves the inherent noise superposition problem of the modulation broadband converter technology, so that the modulation broadband converter can be really applied to an actual broadband spectrum sensing system.
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Other features, objects and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments thereof, which proceeds with reference to the accompanying drawings.
FIG. 1 shows a schematic diagram of a modulated wideband converter MWC technique;
FIG. 2 shows a schematic diagram of a conventional modulated broadband converter-based broadband spectrum sensing system;
FIG. 3 shows a flow chart of a wideband spectrum sensing method based on Nyquist undersampling and cyclic property detection according to the present invention;
FIG. 4 is a schematic diagram of a wideband spectrum sensing apparatus based on Nyquist sampling and cyclic characteristic detection according to the present invention;
FIG. 5 illustrates an effect graph according to one embodiment of the invention; and
fig. 6 shows an effect diagram of another embodiment according to the invention.
In the drawings, like or similar reference numbers indicate like or similar devices (modules) or steps throughout the different views.
Detailed Description
In order to better illustrate the method and apparatus of the present invention and the user equipment and base station applied to the wireless communication network using the method according to the present invention or including the apparatus according to the present invention, two important prior art techniques used by the present invention, respectively, the modulation wideband converter technique and the cyclic characteristic detection technique, are first described below. The following is a brief description with reference to the drawings and related equations.
Modulated Wideband Converter technology (Modulated Wideband Converter: MWC)
Fig. 1 shows a schematic diagram of a modulated wideband converter MWC technique. As can be seen, first, the converter comprises M channels, and the input signal x (t) is first multiplied by a periodic signal waveform p in the mth channelm(T) the period of the signal waveform is Tp＝1/fpThen low-pass filtered by h (T) and processed by Ts＝1/fsThe output signal is obtained by sampling the frequency of (a). Due to pm(t) is a periodic signal, which can be expressed asNote the bookThenFourier transform of (a) into:
remember ym(t) is the output of the low pass filter in the mth channel, which is fourier transformed:
writing the low pass filter outputs of the M channels into a vector form yields:
whereinAnd Z isl(f)＝X(f+(l-L0-1)fp) The elements of matrix a are determined by cm, l for the fourier transform of the spectrally shifted and low pass filtered input signal x (t).
In the presence of noise, the time domain representation of equation (3) is:
since x (t) has sparsity in the frequency domain,is a sparse vector, can be derived fromAre recovered from the low-speed sampled samples. The existence of noise will be reducedAccuracy of recovery. When the noise is strong to a certain degree, the signalWill be totally submerged in noise and cannot be recovered fromAnd (4) recovering. The modulated wideband converter itself has the effect of noise superposition, making it more sensitive to noise, as can be seen from equation (2). As shown in equation (2), the output signal of the modulated wideband converter is a superposition of different frequency spectrums of the input signal. In this process, noise on different frequency spectrums is also superimposed. Due to the sparsity of the frequency domain and the absence of noise, the signal-to-noise ratio of the output of the modulation broadband converter is far lower than that of the input as a result of the spectral superposition. The decrease in signal-to-noise ratio makes it difficult for a modulated wideband converter to function properly in many practical systems.
In order to solve this problem, a new signal processing technique, namely, Cyclic Feature Detection (CFD), must be introduced. The following function is defined:
cyclic autocorrelation function of signal x (t):
cyclic spectral density of signal x (t):
for a cyclostationary signal, the presence of a that is not zero causes the cyclic spectral density or cyclic autocorrelation function of the signal to be non-zero; while for stationary signals, including white noise, the cyclic spectral density or cyclic autocorrelation function is not zero only when α is equal to zero. This feature makes the detection of the cyclic characteristic very robust to noise. This property of the cyclic spectral density or cyclic autocorrelation function is used in the method according to the invention to combat noise in real systems. The effect is very pronounced.
Although the cyclic property detection is robust to noise, the technique cannot be simply combined with the modulated wideband converter to solve the noise superposition problem of the latter, because calculating the cyclic autocorrelation function or cyclic spectral density of the signal x (t) requires first recovering the signal x (t) from the output of the modulated wideband converter, and the noise superposition problem makes this recovery process difficult to implement.
The present invention provides a technique that effectively combines a modulated wideband converter with cyclic characteristic detection. First, the cyclic autocorrelation function and the cyclic spectral density function are extended between two signals:
signal x1(t) and x2Cyclic cross-correlation function between (t):
signal x1(t) and x2(t) cyclic cross spectral density:
in the above equations (7) and (8), the subscript x can be replaced with y/z/n and the like as well.
The technology provided by the invention does not directly recover the input signal x (t), but calculates a first cyclic cross correlation function or a first cyclic cross spectrum density of the output signal of the modulation broadband converter, then estimates a second cyclic cross correlation function or a second cyclic cross spectrum density of a certain deformation of the input signal x (t) from the first cyclic cross correlation function or the first cyclic cross spectrum density, and finally compares the second cyclic cross correlation function or the second cyclic cross spectrum density with a preset threshold value to obtain an estimation value of the occupied sub-band. Since recovery of the input signal x (t) is avoided, the technique is very robust to noise and can operate in very low signal-to-noise ratio environments.
Fig. 3 shows a flow chart of a wideband spectrum sensing method based on nyquist under-sampling and cyclic characteristic detection according to the present invention. As shown in the figure, the broadband spectrum sensing method based on the undersnyquist sampling and the cyclic characteristic detection according to the present invention includes:
a. converting an input signal into a first output signal through a Nyquist sampling module;
b. calculating, by a cyclic cross spectral density or cyclic cross correlation function generator, a first cyclic cross spectral density vector or a first cyclic cross correlation function vector of the first output signal;
c. calculating a second cyclic cross spectral density vector or a second cyclic cross correlation function vector of the input signal after the frequency spectrum shifting and the low-pass filtering according to the first cyclic cross spectral density vector or the first cyclic cross correlation function vector by using a sparse signal recovery technology; and
d. and thresholding the second cyclic cross spectral density vector or the second cyclic cross correlation function vector to obtain an estimate of the occupied sub-band.
In order to realize the above-mentioned broadband spectrum sensing method, it is necessary to bypass y containing noise signalsm[n]To recover x (t), but rather to take advantage of sparsitySignal recovery technique according to ym[n]Calculates a signal of the input signal after spectral shift and low-pass filtering, i.e. the signal in equation (4), by using the first cyclic cross spectral density vector or the first cyclic cross correlation function vectorOr a second cyclic cross spectral density vector or a second cyclic cross correlation function vector. Here, it is necessary to resort to the linear relationship to be established next.
In particular, ym(t) and ynThe cyclic cross spectral density between (t) is:
wherein,
is a conventional cross-correlation function. From the above equations (4), (9) and (10) it is possible to obtain:
definition ofWhereinIn the form of a matrix, the matrix is,is thatThe (i, j) th element of (a). Definition ofIs composed ofVectorized form. Similar definitionsAnd(11) the formula can be expressed in a matrix form as follows:
note that only when Zi(f) When not equal to 0 and i ═ j,is not equal to zero, so that when the input signal x (t) is sparse in the frequency domain,is a sparse vector and can be recovered by using a sparse signal recovery technologyIs estimated. And the noise vector in the formula (12)Is statistically zero. So as long as the observation time is long enough, the statistics are accurate enough,the recovery of (c) is not affected by noise.
Thus, the scheme bypasses y containing noise signalsm[n]To recover x (t) and the calculation is based on ym[n]The first cycle ofCross spectral density vector or first cyclic cross correlation function vector, and then estimating the signal of the input signal after spectrum shift and low-pass filtering from the first cyclic cross spectral density vector or the first cyclic cross correlation function vector by using sparse signal recovery technology (namely, the signal in the formula (4))) And subsequently comparing the second cyclic cross spectral density or the second cyclic cross correlation function with a predetermined threshold in the thresholding step. If the second cycle cross spectrum density or the second cycle cross correlation function corresponding to a certain sub-band is greater than a preset threshold value, the sub-band is identified as an occupied state; otherwise, the sub-band is identified as idle and can be used by the cognitive radio user. Expressed in terms of equations, we will decide that the ith sub-band is in the occupied state if:
as described above, the noise vector in equation (12)Is statistically zero, i.e. when the observation time is sufficiently long, the inventive solution will not be affected by noise. However, in practical systems, the observation time is limited, and therefore the noise vectorCannot be truly zero. The noise power depends on the observation time, so the performance of the broadband spectrum detection is also affected by the observation time. The shorter the observation time, the less sufficient the statistics, the larger the noise power, and thus the poorer the performance of the spectrum detection. In practical applications, it is necessary to properly balance the detection time and the detection accuracy.
Fig. 4 shows a schematic diagram of a broadband spectrum sensing apparatus based on nyquist under-sampling and cyclic characteristic detection according to the present invention. As shown in the figure, the broadband spectrum sensing device based on the undersnyquist sampling and the cyclic characteristic detection according to the invention comprises: a sub-nyquist sampling module 410 for converting an input signal into a first output signal;
a cyclic cross spectral density or cyclic cross correlation function generator 420 for calculating a first cyclic cross spectral density vector or a first cyclic cross correlation function vector of the first output signal;
a sparse signal recovery module 430, configured to calculate a second cyclic cross spectral density vector or a second cyclic cross correlation function vector of the input signal after the frequency spectrum shifting and low-pass filtering according to the first cyclic cross spectral density vector or the first cyclic cross correlation function vector by using a sparse signal recovery technique; and
a thresholding module 440 for estimating occupied subbands from the second vector of cyclic cross-spectral density or the second vector of cyclic cross-correlation functions.
The apparatus shown in fig. 4 is a virtual apparatus for executing the method flow shown in fig. 3, and those skilled in the art should understand that: the four parts of the device do not necessarily have to be separately arranged, but may also be located in a module capable of realizing the above functions.
The performance of the invention will be illustrated in the following two examples.
In the next two embodiments, the total bandwidth is 10GHz, and then the sampling frequency is at least 20GHz, in accordance with the conventional nyquist theorem. The bandwidth of each subband is set to B ═ 1GHz, i.e., ten total subbands, and only one subband is in an occupied state at a time.
Fig. 5 shows an effect diagram according to an embodiment of the invention.In this embodiment, the signal-to-noise ratio of the input signal is-10 dB (the signal-to-noise ratio of the output signal of the MWC drops by a factor of ten, i.e., -20dB, due to the noise superposition of the MWC). The number of channels of the MWC is set as M-2, and the sampling rate of each channel is fsAt 5GHz, the total sampling rate is 10GHz, which is only half of the conventional nyquist sampling rate. In the embodiment shown in fig. 5, when the observation time N is 100 OFDM symbols, the performance is very good, and the false alarm rate and the missed detection rate are both within 0.1.
Fig. 6 shows an effect diagram of another embodiment according to the invention. In this embodiment, the input signal is-3 dB, the number of channels of the MWC is set to M ═ 1, and the sampling rate per channel is fsAt 5GHz, the total sampling rate is thus 5GHz, which is only 25% of the conventional nyquist sampling rate. In the embodiment shown in fig. 6, better performance is also obtained when the observation time N is 100 OFDM symbols.
Of course, the sampling accuracy is higher if the observation time N is larger. As can be seen from the embodiments of fig. 5 and 6, the method according to the present invention can still work well with very low snr and has good performance.
Generally speaking, the invention provides a broadband spectrum sensing technology working under the under-nyquist sampling rate, and the technology has the advantages of low sampling rate, high accuracy, low computation complexity, robustness to factors such as hardware nonideal and the like, can normally work under the environment of low signal-to-noise ratio, and has strong practicability. In addition, the invention effectively solves the inherent noise superposition problem of the modulation broadband converter technology, so that the modulation broadband converter can be really applied to an actual broadband spectrum sensing system.
In the following detailed description of the preferred embodiments, reference is made to the accompanying drawings which form a part hereof. The accompanying drawings illustrate, by way of example, specific embodiments in which the invention may be practiced. The illustrated embodiments are not intended to be exhaustive of all embodiments according to the invention. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive. Furthermore, it will be obvious that the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. Several elements recited in the apparatus claims may also be implemented by one element. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (6)
1. A method (300) of wideband spectrum sensing based on nyquist-less sampling and cyclic characteristic detection, comprising:
a. an input signal is converted by a modulated wideband converter into a plurality of first output signals (310) output by different channels of the modulated wideband converter;
b. calculating a first vector of cyclic cross spectral densities or a first vector of cyclic cross correlation functions of the plurality of first output signals by a cyclic cross spectral density or cyclic cross correlation function generator (320);
c. calculating a second cyclic cross spectral density vector or a second cyclic cross correlation function vector (330) of the input signal after the frequency spectrum shifting and the low-pass filtering according to the first cyclic cross spectral density vector or the first cyclic cross correlation function vector by using a sparse signal recovery technology; and
d. comparing the second cyclic cross spectral density vector or the second cyclic cross correlation function vector with a predetermined threshold, and if the second cyclic cross spectral density vector or the second cyclic cross correlation function vector corresponding to a sub-band is greater than the predetermined threshold, identifying the sub-band as an occupied state; otherwise, the sub-band is identified as idle, thus resulting in an occupied sub-band (340).
2. The method of claim 1, wherein a signal-to-noise ratio of the input signal is below-3 dB.
3. A wideband spectrum sensing apparatus (400) based on sub-nyquist sampling and cyclic characteristic detection, comprising:
a modulated wideband converter (410) for converting an input signal into a plurality of first output signals output by different channels of the modulated wideband converter;
a cyclic cross spectral density or cyclic cross correlation function generator (420) for calculating a first cyclic cross spectral density vector or a first cyclic cross correlation function vector for the plurality of first output signals;
a sparse signal recovery module (430) configured to calculate a second cyclic cross spectral density vector or a second cyclic cross correlation function vector of the input signal after the spectral shifting and low-pass filtering according to the first cyclic cross spectral density vector or the first cyclic cross correlation function vector by using a sparse signal recovery technique; and
a thresholding module (440) for comparing the second cyclic cross spectral density vector or the second cyclic cross correlation function vector to a predetermined threshold and identifying a sub-band as occupied if the second cyclic cross spectral density vector or the second cyclic cross correlation function vector corresponding to the sub-band is greater than the predetermined threshold; otherwise, the sub-band is identified as an idle state, thereby obtaining an occupied sub-band.
4. The apparatus of claim 3, wherein the input signal has a signal-to-noise ratio below-3 dB.
5. A user equipment in a wireless communication network, characterized in that the user equipment comprises a wideband spectrum sensing apparatus (400) based on sub-nyquist sampling and cyclic characteristic detection according to claim 3 or 4.
6. A base station in a wireless communication network, characterized in that the base station comprises a wideband spectrum sensing apparatus (400) based on sub-nyquist sampling and cyclic characteristic detection according to claim 3 or 4.
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