CN102546061B - Self-adaptive time-frequency hole detection method based on wavelet transformation - Google Patents

Abstract

The invention discloses a self-adaptive time-frequency hole detection method based on wavelet transformation. A wavelet transformation technology is adopted to self-adaptively carry out time frequency domain subsection on time continuous signals or discrete-time signals and judge whether each time frequency resource block is idle or not. The method comprises the following steps of: (1) firstly carrying out time domain segmentation on cognition user monitoring and receiving signals to ensure the occupied frequency band of each main user to be constant in the segment; (2) carrying out Fourier transformation on receiving signals of each sub time interval in the existing method to obtain the signal frequency spectrum of each sub time interval, and carrying out wavelet transformation to the frequency spectrum to detect the border of each frequency range so as to realize frequency range division; and (3) synthesizing self-adaptive time and frequency domain segmentation results in the last two steps to comprehensively analyze each time frequency resource block and judge whether the time frequency resource block is idle or not. The method disclosed by the invention realizes time domain and frequency domain self-adaptive segmentation of signals, effectively solves the contradiction between high resolution of a frequency spectrum hole of the frequency spectrum and the quick and in-time detection requirement in frequency spectrum detection, and simultaneously, is used for estimating the energy distribution of time and frequency domains of main user signals. A simulation result verifies the effectiveness of the method.

Description

Self adaptation time-frequency air cavity detection method based on wavelet transformation

Technical field:

The present invention relates to a kind of time-frequency hole cognition technology that is applicable to cognition wireless electrical domain, it adopts the method for processing based on wavelet transform signal, two-dimentional Energy distribution to the time domain of wireless environment and frequency domain is carried out self-adapting detecting, with detection wireless environment time-frequency hole.

Background technology:

Wireless communication spectrum is a kind of limited precious resources, along with the fast development of information-intensive society economy, and the rare important bottleneck that day by day becomes mobile communication and wireless access wide band technology realization of frequency spectrum resource; What on the other hand, form sharp contrast with frequency spectrum resource shortage is but the extremely low of the existing availability of frequency spectrum.Under such background, cognitive radio technology, with its access, can improve the availability of frequency spectrum greatly, effectively solves the deficient problem of frequency spectrum resource, is considered to one of potential key technology of next generation mobile communication system.

Cognitive radio system, with in a kind of " chance mode " insertion authority frequency range, is not causing authorized user under the prerequisite of interference, utilizes dynamically its interim idle frequency range.Realize this pair of primary user almost the noiseless of " transparent " take, require cognitive user reliably, perception authorized user resumes and keep out of the way in time rapidly; Cognitive radio system requires cognitive user frequency spectrum hole detected reliably, rapidly and take on the other hand, cognitive radio system will be realized the object that high efficiency is utilized frequency spectrum, also should there is the ability of estimating wireless frequency spectrum hole characteristic, and adjust adaptively and send parameter (as data rate, transmission mode, transmission bandwidth etc.) based on frequency spectrum hole characteristic.Therefore, frequency spectrum perception technology is the key technology of cognitive radio system.

For cognitive radio frequency spectrum cognition technology and adaptive modulation technology, carried out a large amount of research work both at home and abroad, wherein the research for frequency spectrum perception technology roughly can be divided into following two large classes: (1) is collaborative to be detected: by processing a plurality of perception users' detection information, judge more accurately whether primary user exists, these class methods will depend on the centralized control of complexity or the detection information exchange that distributed control realizes a plurality of perception users conventionally; (2) whether primary user's transmitting terminal detects: by analysis, detectd in cognitive user reception signal and existed primary user's signal to judge the use state of frequency spectrum, these class methods can be divided into again following four classes: 1) energy measuring method: this class algorithm does not need to know the prior information of authorization user signal conventionally, can be readily applied to various Unknown Channel, detect fast, realize simple, but such algorithm relies on and requires accurate estimating noise power, and in reality, noise power estimation error can cause higher false alarm probability; 2) feature detection method: this class algorithm utilizes known primary user's signal characteristic (as wave character, cyclostationarity etc.) to take frequency spectrum situation to primary user and detects, can under strong interference environment, detect reliably frequency spectrum hole, but this class algorithm requires to know in advance the characteristic information of primary user's signal, and computation complexity is higher; 3) correlation detection algorithm: this class algorithm utilizes primary user's signal sample to have correlation this feature more much higher than noise sample value, realize the detection to authorized user, but this class algorithm is limited to the acute variation of signal correlation; 4) wavelet transformation frequency spectrum detection method: this class algorithm utilizes wavelet decomposition or WAVELET PACKET DECOMPOSITION can adjust neatly the advantage of frequency spectrum detection resolution, realizing multi-resolution spectrum detects, or the Characteristics of Mutation extractability that utilizes small echo to change, detect the sudden change that primary user took-discharged frequency spectrum, but this class algorithm has the shortcoming of higher algorithm complex conventionally.

At present, existing cognitive radio frequency spectrum sensing method is mainly for the detection in frequency domain " frequency spectrum hole " both at home and abroad, and less angle from time domain and frequency domain two dimension time frequency analysis detects " time-frequency hole ", causes being difficult to estimate the time domain specification of idle frequency spectrum.This class frequency spectrum detecting method is in order to obtain higher frequency resolution, need the sampling at long period interval, on the other hand, in order frequency spectrum hole and primary user to be detected in time, resume frequency spectrum hole, require shorter time sampling interval, therefore existing frequency spectrum detecting method, except having the described limitation of upper joint, is also limited to the contradiction between sampling interval long period and fast timely testing requirement.

Summary of the invention:

The object of the invention is to overcome the shortcoming of above-mentioned prior art, a kind of self adaptation time-frequency air cavity detection method based on wavelet transformation is provided, only different from frequency domain detection frequency spectrum hole from existing cognitive radio frequency spectrum sensing method, this programme adopts wavelet transform signal Feature Extraction Technology, realize time domain and the frequency domain adaptive segmentation of signal, effectively solve the contradiction between sampling interval long period and fast timely testing requirement, estimate the Energy distribution of the time-frequency domain of primary user's signal simultaneously.

In addition, in real network, traffic carrying capacity due to primary user in the microcosmic time period has certain statistical law, thereby frequency distribution characteristics also has certain statistical law at that time, obtain the time-frequency distributions characteristic of primary user's signal, can, better according to the statistical nature of each time-frequency piece, adjust neatly adaptively sense cycle, and time-frequency hole is predicted, thereby effectively reduce cognitive user access opportunity loss and do not keep out of the way in time the harmful interference loss causing primary user.

Therefore, significant based on researching and analysing of time-frequency two-dimensional angle for cognitive radio system.

The object of the invention is to solve by the following technical programs:

The time-frequency air cavity detection method the present invention relates to is divided into three steps: the first step, and first to cognitive user, monitoring receives signal Time Domain Piecewise, guarantees in this segmentation, and each primary user's band occupancy continues constant; Second step, to each reception signal at times, adopts existing method, carries out Fourier transform and obtains each signal spectrum at times, and this frequency spectrum is carried out to wavelet transformation to detect each frequency range edge, thereby realize frequency range, divides.The 3rd step, auto-adaptive time and frequency domain segmentation result in comprehensive first two steps, comprehensively analyze each time/frequency source block, whether judges the time/frequency source block free time.

Divide five parts to be introduced the method below, the first introducing system model of first, second portion and third part are introduced respectively time domain and frequency domain segmentation method, the 4th part realizes time-frequency air cavity detection according to the time-frequency piece power spectral density after segmentation, and the 5th part has also proposed a kind of improving one's methods to above-mentioned time-frequency detection method.

During following methods is introduced, the signal of employing is time-continuous signal, and the method can directly be generalized to the processing of discrete-time signal.

One. cognitive radio time-frequency sensory perceptual system model

For above 2 research contents, we adopt a wireless communication system with M potential cognitive user and N potential primary user, suppose that the frequency range that this system takies is [f ₀, f _n], and bandwidth is B, the continuous signal that makes m potential cognitive user cognitive user receive is expressed as

$r_m\left(t\right)=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\α}}_{n,m}{x}_n\left(t\right)+{\mathrm{\ω}}_m\left(t\right)---\left(1\right)$

Wherein, x _n(t) represent the signal that n potential primary user sends, and α _{n, m}represent the signal gain between n potential primary user and m potential cognitive user, ω _m(t) represent noise.It should be noted that, in introduction below, each cognitive user adopts identical signal processing method, for simplified characterization, in introduction below, will no longer distinguish each cognitive user, omits footnote m.For further easy analysis, we do following supposition to above-mentioned cognitive system, and analyze its supposition reasonability.

A1) because potential primary user takies corresponding band, there is temporal discontinuity, and the channel between primary user and cognitive user is quasistatic on this time period within each time period, i.e. α in this time period _nfor constant;

A2) n potential primary user of supposition taking frequency range in the duration, and the frequency band taking can not change

A3) noise in supposition reception signal is additive white Gaussian noise.

In wireless communication system, user's access base station process, by base station assigns up-downgoing frequency range and period, once complete communication can be divided into several shorter communication duration or time periods, in the corresponding time period, channel is approximate constant, and in the communication duration, frequency band is also constant, and receive noise in signal, is conventionally assumed to additive white Gaussian noise, therefore above-mentioned hypothesis is reasonable, conforms to practical communication system.

Two. receive signal time domain and adapt in vain segmentation

Based on assumed condition a1), in each primary user's band occupancy in constant time period, can be similar to and think that cognitive user reception average power signal is constant, utilize and receive average power signal at times, intend adopting wavelet transformation detection signal average power catastrophe point, to realize each detection at edge at times of signal, thereby time-domain signal is divided into a plurality of time period continuous signals, concrete time-domain signal adaptive segmentation scheme is as follows:

1) calculate and receive average power signal

Ask to received signal its average power, formula specific as follows:

$r^{\′}\left(t\right)=\frac{1}{T}{\∫}_t^{t+T}{\left|r\left(t\right)\right|}^2\mathrm{dt}---\left(2\right)$

In formula, mean circle time T can be chosen for 50 times of signal Transmission bit rate, to can obtain good average effect.

2) multi-scale wavelet transformation

The wavelet function that makes wavelet transformation adopt is ψ (t), and the extension function of this small echo can be expressed as

by Wavelet Analysis Theory, known, cognitive user is received to the wavelet transformation that signal carries out different scale, can regard the wavelet function ψ to different scale as _a(t) with signal r ' convolution (t), be simplified operation, can adopt the first spectral density Ψ to the two _a(ω), R (ω) multiplies each other, then passes through Ψ _a(ω) R (ω) inversefouriertransform obtains the wavelet transformation of different scale.

WT _ar′(t)＝F ^-1(Ψ _a(ω)R(ω)) (3)

3) wavelet transformation detects catastrophe point

From supposition above, the reception average power signal of cognitive user has similar step edge at the intersection of different periods, according to wavelet transformation character, at the edge moment of piecewise continuous signals { t _n, its wavelet transformation has local maximum, and the wavelet transformation local maximum that therefore can receive by detecting cognitive user average power signal is realized the detection at signal edge.Be that edge moment sequencal estimation can obtain by following formula:

${\hat{t}}_n={\max \mathrm{ima}}_t\left\{{\mathrm{WT}}_{a=2^j}{r}^{\′}\left(t\right)\right\},t\∈(t_0,t_N)---\left(4\right)$

Wherein, (t ₀, t _n) represent that cognitive user receives time period corresponding to signal, adopts two minutes yardsticks, i.e. a=2 in this project ^j, j=0,1,2 ...

On the other hand, the noise average power of time-domain signal is similar random pulses (because the average period adopting in average power signal calculating is shorter), characteristic by Wavelet Transform Modulus Maxima on Signal on multiple dimensioned, the Wavelet Transform Modulus Maxima on Signal at step edge does not increase and changes with wavelet scale, the wavelet transformation local maximum of spike signal increases and reduces with wavelet scale, therefore, the multi-scale wavelet transformation maximum of utilizing cognitive user to receive signal is carried out segmentation to the time of primary user's signal, has good anti-noise jamming characteristic.In order further to improve time slice edge, detect anti-interference and noise effect ability, edge constantly sequencal estimation can be improved to

${\hat{t}}_n={\max \mathrm{ima}}_t\left\{\underset{j=0}{\overset{J}{\mathrm{\Π}}}{\mathrm{WT}}_{a=2^j}{r}^{\′}\left(t\right)\right\},t\∈(t_0,t_N)---\left(5\right)$

In order further to improve the reliability of signal Time Domain Piecewise, intend considering to adopt the judgement thresholding that improves local extremum, but this thresholding determination methods can be brought certain hour segmentation error, the consideration of need to being compromised.

This thresholding b _tHcan be taken as function

average, be calculated as follows:

$b_{\mathrm{TH}}=\frac{1}{t_N-t_0}{\∫}_{t_0}^{t_N}\underset{j=0}{\overset{J}{\mathrm{\Π}}}{\mathrm{WT}}_{a=2^j}r\′\left(t\right)\mathrm{dt}---\left(6\right)$

Three. adaptive frequency domain segmentation

The Time Domain Piecewise of the reception signal based on above-mentioned, can think that it is continue and stablize constant that each primary user takies frequency spectrum situation in each time slice.In the present invention, adopt existing document [13] (document [13] be " Z.Tian and G. B Giannakis. " A wavelet approach to wideband spectrum sensing for cognitive radios; " Cognitive Radio Oriented Wireless Networks and Communications2006, Mykonos Island, Greece, Jun.2006. ") method in realizes frequency domain adaptive segmentation in each Time Domain Piecewise, and concrete grammar is as follows:

1) calculate frequency domain power spectrum

Suppose to p at times signal carry out frequency domain segmentation, wherein this at times time-domain signal can be expressed as:

$\begin{array}{cc}{r}^{\left(p\right)}\left(t\right)=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\α}}_n{x}_n\left(t\right)+{\mathrm{\ω}}_m\left(t\right)& t\∈[{\hat{t}}_p,{\hat{t}}_{p+1}]---\left(7\right)\end{array}$

The first step is asked each power spectrum of time domain at times:

2) multi-scale wavelet transformation

Further this power spectrum is carried out to frequency-domain small wave conversion,

$W_{s=2^j}{S}_r^{\left(p\right)}\left(f\right)=S_r^{\left(p\right)}\left(f\right)*{\mathrm{\φ}}_{s=2^j}\left(f\right)---\left(9\right)$

Wherein

for basic flexible wavelet function, for the first derivative of a certain low pass smooth function, as Gaussian function.

3) frequency domain catastrophe point detects

So the frequency spectrum segmentation edge sequence frequency of corresponding p time tick can be estimated to obtain by following formula:

${\hat{f}}_m^{\left(p\right)}={\max \mathrm{ima}}_f\left\{\underset{j=1}{\overset{J}{\mathrm{\Π}}}\right[W_{s=2^j}{r}^{\′}\left(t\right)\left]\right\},f\∈(f_0,f_N)---\left(10\right)$

Four. detect time-frequency hole

1) estimating noise power spectrum

Based on above-mentioned each time slice having obtained, the primary user of each sub-band, take situation, can obtain whole time period (t ₀, t _n) and bandwidth [f ₀, f _n] in time/frequency source block take situation.Further, for the rated output spectrum density according to the following formula of m sub-frequency bands time/frequency source block in occupied p time slice.

$P_n^{\left(p\right)}=\frac{1}{(f_{n+1}-f_n)}{\∫}_{f_n}^{f_{n+1}}{S}_r^{\left(p\right)}\left(f\right)\mathrm{df}---\left(11\right)$

Because noise is additive white Gaussian noise, therefore, noise is constant in whole frequency domain power spectral density, can be calculated and be estimated to obtain by following formula:

$N_{\mathrm{AWGN}}=\underset{n,p}{\min }{P}_n^{\left(p\right)}---\left(12\right)$

2) calculate each time-frequency block signal power spectrum

Average power signal spectrum density in p time slice in m sub-frequency bands time/frequency source block can be modified to

$P_n^{\′\left(p\right)}=P_n^{\left(p\right)}-N_{\mathrm{AWGN}}---\left(13\right)$

3) judge that whether time-frequency piece is idle

In theory, if some at times on the fast power spectral density of certain sub-frequency bands time-frequency be greater than Power Spectrum of White Noise density, this time/frequency source block takies for primary user, otherwise is the free time.Consider receive signal each at times the time interval limited, the noise average power spectrum density that this is on average obtained is not at times still desirable constant, but has the stochastic variable of certain variance.In order to suppress the fluctuation of time average noise power spectrum, disturb, a higher power spectral density thresholding P is set as required _thjudge whether each time/frequency source block is idle.

P _Th=αN _AWGN (14)

Wherein factor-alpha >1, can need to arrange according to computed reliability.

Five. time-frequency air cavity detection improves

At system acquisition frequency wider range, or in the wider situation of the frequency range that cognitive user need to detect, impact due to multipath channel, in the frequency range that can cause cognitive user reception signal to take in system, show as obvious frequency selectivity, and this frequency selectivity detects frequency domain separation formation interference for wavelet transformation; If system acquisition frequency wider range on the other hand, the average power of noise is larger, and some less sub-bands take or idle condition changes, make the change of average power of signal relatively little, that is to say, the interference of noise more easily makes to adopt the method for average power surge detection Time Domain Piecewise point produce erroneous judgement or fail to judge.

In order to overcome this time/frequency source block that detects the larger generation of frequency range due to system, detect error, the invention allows for a kind of time-frequency air cavity detection and improve one's methods, detailed process as shown in Figure 1:

Utilize WAVELET PACKET DECOMPOSITION can neatly signal decomposition be become to a plurality of signals that have compared with narrow bandwidth, reception signal on each decomposition frequency band is carried out respectively to above-mentioned time-frequency adaptive segmentation, the Time Domain Piecewise that finally each is decomposed to the signal in frequency range carries out comprehensively, can obtaining signal time-frequency air cavity detection more accurately.

Accompanying drawing explanation:

Fig. 1 is that the time-frequency air cavity detection based on WAVELET PACKET DECOMPOSITION improves block diagram;

Fig. 2 (a) is the time-frequency air cavity detection system block diagram of processing based on wavelet transform signal;

Fig. 2 (b) is for receiving signal filtering module frame chart;

Fig. 2 (c) is time-domain adaptive segmentation module block diagram;

Fig. 2 (d) is frequency domain adaptive segmentation module block diagram;

It during Fig. 2 (e), is frequency hole comprehensive detection module frame chart;

Fig. 3 is the actual figure that takies of running time-frequency resource;

Fig. 4 is signal Time Domain Piecewise simulation result;

Fig. 5 is signal frequency domain segmentation simulation result.

Embodiment:

Below in conjunction with accompanying drawing, the present invention is described in further detail:

Time-frequency hole self-adapting detecting method based on wavelet transformation is realized and can be divided into following four steps,

As shown in Figure 2:

The first step: the filtering of signal and sampling;

(1) time-domain signal is carried out to continuous sampling;

(2) according to cognitive user sampling bandwidth (intercepting frequency bandwidth) size, determine whether time-domain sampling signal is adopted to WAVELET PACKET DECOMPOSITION.

Second step: the time-domain adaptive segmentation of signal after filtering

(1) to sampling time-domain signal (or time-domain signal of process WAVELET PACKET DECOMPOSITION), the moving average power of signal is asked in sampling by formula (2);

(2) adopting the first derivative of a certain smooth function is wavelet function, and the moving average power signal in second step is carried out to multi-scale wavelet transformation;

(3) by formula (5), ask its peak value limit that surpasses the threshold value being provided by formula (6), the time point that this extreme point is corresponding is Time Domain Piecewise edge point.

The 3rd step: each frequency domain adaptive segmentation of signal at times

(1) ask respectively in the 4th step each power spectral density of corresponding time-domain signal at times;

(2) adopting the first derivative of a certain smooth function is wavelet function, respectively each power spectral density in the 5th step is carried out to multi-scale wavelet transformation;

(3) by formula (10), ask the peak value limit of multi-scale wavelet transformation function, the frequency that this extreme point is corresponding is frequency domain segmentation edge point;

The 4th step: estimate the spectral density in time/frequency source block, and judge whether the free time;

(1) according to the time-frequency domain segmentation in the 4th step and the 7th step, running time-frequency resource is divided into time/frequency source block, by formula (12), calculate additive white Gaussian noise power spectral density, and by formula (13), calculate respectively the available signal power spectrum density of each time/frequency source block;

According to the given power spectral density thresholding of formula (14), judge taking or idle condition of each time/frequency source block.

1) experiment simulation

Emulation adopts the ofdm system that sub-carrier number is 1024, subcarrier spacing is 1KHz, adopt QPSK modulation, suppose (corresponding 50 OFDM mark spaces at 50ms, each OFDM mark space is 1ms) in running time-frequency resource take situation: in 0-10 OFDM interval, system takies whole 1024 subcarriers, in 11-30 OFDM mark space, system takies 0-255 subcarrier and 513-767 subcarrier, in 31-50 OFDM mark space, system takies whole 0-255 subcarriers, as shown in Figure 3.The time-domain signal sample waveform of system is as shown in Fig. 4 (a).

In emulation, intending adopting the first derivative of Gaussian function is that edge detects wavelet function, and this wavelet function stretches and is

on each subcarrier, noise variance is taken as 0.1.

In this 50ms observation interval, shown in following Fig. 4 of signal time-domain adaptive segmentation result (b), visible, the time domain edge that average power signal based on wavelet transformation detects just corresponding actual frequency domain takies the time point that situation changes, realized well the Time Domain Piecewise of signal, if first time domain edge of estimating is the 11st OFDM symbol sampler starting point, the 31st of estimation point position, second time domain edge OFDM symbol sampler starting point.

Fig. 5 has provided the power spectrum signal of corresponding second Time Domain Piecewise and has adopted the power spectrum edge frequency estimated result of wavelet transformation, wherein Fig. 5 (a) has provided corresponding power spectrum density, peak point correspondence in Fig. 5 (b) power spectrum distortion point, be frequency domain segmentation edge frequency: the 256th sub-carrier frequency point, the 512nd sub-carrier frequency point, the 768th sub-carrier frequency point, 1024 subcarriers are divided into four frequency domain segmentations, take situation with the frequency domain of segmentation in second o'clock and conform to completely.

The above, it is only preferred embodiment of the present invention, not the present invention is done to any pro forma restriction, although the present invention discloses as above with preferred embodiment, yet not in order to limit the present invention, any those skilled in the art, do not departing within the scope of technical solution of the present invention, when can utilizing the method for above-mentioned announcement and technology contents to make a little change or being modified to the equivalent embodiment of equivalent variations, in every case be the content that does not depart from technical solution of the present invention, any simple modification of above embodiment being done according to technical spirit of the present invention, equivalent variations and modification, still belong in the scope of technical solution of the present invention.

Claims (5)

1. the self adaptation time-frequency air cavity detection method based on wavelet transformation, it is characterized in that, adopt wavelet transformation technique adaptively time-continuous signal or discrete-time signal to be carried out to time-frequency domain segmentation, and the free time that judges each time/frequency source block whether, comprises the steps:

(1) first to cognitive user, monitoring receives signal Time Domain Piecewise, guarantees in this segmentation, and each primary user's band occupancy continues constant;

(2) to each reception signal at times, adopt existing method, carry out Fourier transform and obtain each signal spectrum at times, this frequency spectrum is carried out to wavelet transformation to detect each frequency range edge, thereby realize frequency range, divide;

(3) auto-adaptive time and the frequency domain segmentation result in comprehensive first two steps, comprehensively analyzes each time/frequency source block, whether judges the time/frequency source block free time;

The method specifically comprises following five parts:

Cognitive radio time-frequency sensory perceptual system model, adopts a wireless communication system with M potential cognitive user and N potential primary user, supposes that the frequency range that this system takies is [f ₀, f _n], and bandwidth is B, the continuous signal that makes m potential cognitive user cognitive user receive is expressed as

$r_m\left(t\right)=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\α}}_{n,m}{x}_n\left(t\right)+{\mathrm{\ω}}_m\left(t\right)---\left(1\right)$

Wherein, x _n(t) represent the signal that n potential primary user sends, and α _n,mrepresent the signal gain between n potential primary user and m potential cognitive user, ω _m(t) represent noise; In introduction below, each cognitive user adopts identical signal processing method, for simplified characterization, in introduction below, will no longer distinguish each cognitive user, omits footnote m; We do following supposition to above-mentioned cognitive system, and analyze its supposition reasonability;

A1) because potential primary user takies corresponding band, there is temporal discontinuity, and the channel between primary user and cognitive user is quasistatic on this time period within each time period, i.e. α in this time period _nfor constant;

A2) n potential primary user of supposition taking frequency range in the duration, and the frequency band taking can not change;

A3) noise in supposition reception signal is additive white Gaussian noise;

In wireless communication system, user's access base station process, by base station assigns up-downgoing frequency range and period, once complete communication is divided into several shorter communication duration or time periods, in the corresponding time period, channel is approximate constant, and in the communication duration, frequency band is also constant, and receive noise in signal, is conventionally assumed to additive white Gaussian noise, therefore above-mentioned hypothesis is reasonable, conforms to practical communication system;

Receive the segmentation of signal time-domain adaptive: based on assumed condition a1), in each primary user's band occupancy in constant time period, be similar to and think that cognitive user reception average power signal is constant, utilize and receive average power signal at times, intend adopting wavelet transformation detection signal average power catastrophe point, to realize each detection at edge at times of signal, thereby time-domain signal is divided into a plurality of time period continuous signals;

Adaptive frequency domain segmentation: the Time Domain Piecewise of the reception signal based on above-mentioned, think that it is continue and stablize constant that each primary user takies frequency spectrum situation in each time slice;

Detect time-frequency hole

1) estimating noise power spectrum

Based on above-mentioned each time slice having obtained, the primary user of each sub-band, take situation, obtain whole time period (t ₀, t _n) and bandwidth [f ₀, f _n] in time/frequency source block take situation; Further, for occupied _pn sub-frequency bands time/frequency source block in individual time slice; Rated output spectrum density according to the following formula;

$P_n^{\left(p\right)}=\frac{1}{(f_{n+1}-f_n)}{\∫}_{f_n}^{f_{n+1}}{S}_r^{\left(p\right)}\left(f\right)\mathrm{df}---\left(11\right)$

[f in formula _n, f _n+1] represent the n sub-frequency bands in p time slice, f _nand f _n+1the start-stop Frequency point that represents respectively this sub-band,

the power spectrum that represents primary user's signal in p time slice;

Because noise is additive white Gaussian noise, therefore, noise is constant in whole frequency domain power spectral density, by following formula, is calculated and is estimated to obtain:

$N_{\mathrm{AWGN}}=\underset{n,p}{\min }{P}_n^{\left(p\right)}---\left(12\right)$

2) calculate each time-frequency block signal power spectrum

Average power signal spectrum density in p time slice in n sub-frequency bands time/frequency source block is modified to

$P_n^{\′\left(p\right)}=P_n^{\left(p\right)}-N_{\mathrm{AWGN}}---\left(13\right)$

3) judge that whether time-frequency piece is idle

In theory, if some at times on certain sub-frequency bands time-frequency power spectral density be greater than Power Spectrum of White Noise density, this time/frequency source block takies for primary user, otherwise is the free time; Consider receive signal each at times the time interval limited, the noise average power spectrum density that this is on average obtained is not at times still desirable constant, but has the stochastic variable of certain variance; In order to suppress the fluctuation of time average noise power spectrum, disturb, a higher power spectral density thresholding P is set as required _thjudge whether each time/frequency source block is idle;

P _Th=αN _AWGN （14）

Factor-alpha >1 wherein, needs to arrange according to computed reliability:

Time-frequency air cavity detection improves

At system acquisition frequency wider range, or in the wider situation of the frequency range that cognitive user need to detect, impact due to multipath channel, in the frequency range that can cause cognitive user reception signal to take in system, show as obvious frequency selectivity, and this frequency selectivity detects frequency domain separation formation interference for wavelet transformation; If system acquisition frequency wider range on the other hand, the average power of noise is larger, and some less sub-bands take or idle condition changes, make the change of average power of signal relatively little, that is to say, the interference of noise more easily makes to adopt the method for average power surge detection Time Domain Piecewise point produce erroneous judgement or fail to judge.

2. self adaptation time-frequency air cavity detection method as claimed in claim 1, it is characterized in that: the method adopts pin average power to received signal to carry out wavelet transformation, the adaptive time domain segmentation that achieves a butt joint and collect mail number, guarantees that, in each time slice, the spectrum structure of signal is constant.

3. self adaptation time-frequency air cavity detection method as claimed in claim 1, is characterized in that: the method adopts WAVELET PACKET DECOMPOSITION or other filtering receiving handling method to carry out to received signal frequency-division section filtering reception & disposal.

4. self adaptation time-frequency air cavity detection method as claimed in claim 1, is characterized in that: described time-domain signal adaptive segmentation is as follows:

1) calculate and receive average power signal

R (t) asks its average power to received signal, formula specific as follows:

$r^{\′}\left(t\right)=\frac{1}{T}{\∫}_t^{t+T}{\left|r\left(t\right)\right|}^2\mathrm{dt}---\left(2\right)$

In formula, mean circle time T is chosen for 50 times of signal Transmission bit rate, to can obtain good average effect;

2) multi-scale wavelet transformation

The wavelet function that makes wavelet transformation adopt is ψ (t), and the extension function of this small echo is expressed as

by Wavelet Analysis Theory, known, cognitive user is received to the wavelet transformation that signal carries out different scale, regard the extension function ψ to the small echo of different scale as _a(t) with the average power r ' of signal convolution (t), be simplified operation, adopt the first spectral density Ψ to the two _a(ω), R (ω) multiplies each other, then passes through Ψ _a(ω) R (ω) inversefouriertransform obtains the wavelet transformation of different scale:

WT _ar′(t)=F ^-1(Ψ _a(ω)R(ω)) （3）

3) wavelet transformation detects catastrophe point

From supposition above, the reception average power signal of cognitive user has similar step edge at the intersection of different periods, according to wavelet transformation character, at the edge moment of piecewise continuous signals { t _n, its wavelet transformation has local maximum, and the wavelet transformation local maximum that therefore receives average power signal by detecting cognitive user is realized the detection at signal edge, and edge moment sequencal estimation obtains by following formula:

${\hat{t}}_n=\max {\mathrm{ima}}_t\left\{{\mathrm{WT}}_{a=2^j}{r}^{\′}\left(t\right)\right\},t\∈(t_0,t_N)---\left(4\right)$

Wherein, (t ₀, t _n) represent that cognitive user receives time period corresponding to signal, adopts two minutes yardsticks, i.e. a=2 in this project ^j, j=0,1,2 ...

5. self adaptation time-frequency air cavity detection method as claimed in claim 1, is characterized in that: described adaptive frequency domain segmentation in accordance with the following steps:

1) calculate frequency domain power spectrum

Suppose to p at times signal carry out frequency domain segmentation, wherein this at times time-domain signal be expressed as:

$r^{\left(p\right)}\left(t\right)=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\α}}_n{x}_n\left(t\right)+{\mathrm{\ω}}_m\left(t\right),t\∈[{\hat{t}}_p,{\hat{t}}_{p+1}]---\left(7\right)$

The first step is asked each power spectrum of time domain at times:

2) multi-scale wavelet transformation

Further this power spectrum is carried out to frequency-domain small wave conversion,

$W_{s=2^j}{S}_r^{\left(p\right)}\left(f\right)=S_r^{\left(p\right)}\left(f\right)*{\mathrm{\φ}}_{s=2^j}\left(f\right)---\left(9\right)$

Wherein for basic flexible wavelet function, it is the first derivative of a certain low pass smooth function;

3) frequency domain catastrophe point detects

So the frequency spectrum segmentation edge sequence frequency of corresponding p time tick is estimated to obtain by following formula:

${\hat{f}}_m^{\left(p\right)}=\max {\mathrm{ima}}_f\left\{\underset{j=1}{\overset{J}{\mathrm{\Π}}}\right[W_{s=2^j}{r}^{\′}\left(t\right)\left]\right\},f\∈(f_0,f_N)---\left(10\right).$

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