CN113709750A - Frequency spectrum layering cooperative cognition method for Internet of things environment - Google Patents
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Abstract
The invention discloses a frequency spectrum layering cooperative cognition method for an Internet of things environment, which comprises the following steps: step one, collecting a time domain signal x (t) in a continuous broadband frequency spectrum to further obtain a power spectrum of the x (t) so as to obtain a frequency band of the whole frequency spectrum; step two, performing multi-scale wavelet product transformation on the power spectrum of x (t) obtained by the multi-tap spectrum estimation method, and determining the frequency edge f of each sub-bandnUsing fnDividing the entire spectrum as a dividing point to obtain divided sub-bands; step three, cooperative secondary user SUs pair utilization fnSensing the divided sub-frequency bands, and sending a local sensing result and the detection statistic of the time domain signal energy to a fusion center FC; step four, the fusion center adopts a hard decision mechanism to carry out preliminary decision on the use condition of the frequency spectrum according to the local sensing result in the step three, and further adopts a soft decision mechanism to carry out decision according to the time domain signal energy detection statisticAnd (6) determining. The method improves the accuracy of determining the idle frequency spectrum through a soft-hard combined double-decision mechanism.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a frequency spectrum layering cooperative cognition method for an Internet of things environment.
Background
Frequency bands are a valuable resource and current wireless systems are characterized by static spectrum allocation, fixed radio functionality, and limited network coordination between mobile devices, resulting in a large amount of unused radio spectrum. The methods can be generalized into two implementation ways, one is to divide the sensing frequency band into a plurality of narrow bands, and then to judge each narrow band, and the sensing time of the method is too long. The other approach is to estimate the frequency edge of each primary user signal sub-band for the broadband sampled signal, thereby obtaining the spectrum hole. Here, the main focus is on the broadband spectrum sensing task, and only a small amount of narrow-band BPFs are needed. The cognitive radio network aims to know the change of the surrounding environment, and in order to ensure that normal communication of a primary user is not interfered, a secondary user needs to regularly sense a frequency spectrum hole and reliably detect the existence state of a primary user signal. How to reliably and quickly find free spectrum and utilize opportunity is the key of the spectrum sensing technology. Wavelet transformation can characterize local regularity of signals, but wavelet collaborative detection has inaccuracy on irregular edges of frequency spectrum. A soft-hard combined fusion mechanism is provided on the basis of the traditional soft-fusion and hard-fusion independent fusion mechanism, and the spectrum hole can be more accurately found by combining an improved dual-threshold energy detection algorithm.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide a frequency spectrum layering cooperative cognition method facing the environment of the Internet of things.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a frequency spectrum layering cooperative cognition method for an Internet of things environment, which comprises the following steps:
acquiring a time domain signal x (t) in a continuous broadband spectrum, carrying out N-point equal-interval sampling on the x (t) to obtain a discrete time sequence x (N '), and obtaining a power spectrum of the x (t) by adopting a multi-tap spectrum estimation method on the discrete time sequence x (N') to obtain a frequency band of the whole spectrum; wherein N 'represents discrete time, and N' is the number of sampling points;
step two, performing multi-scale wavelet product transformation on the power spectrum of x (t) obtained by the multi-tap spectrum estimation method, and determining the frequency edge f of each sub-band by using the modulo maximum of the first derivative of the formula sub-obtained after the multi-scale wavelet product transformationnUsing fnDividing the entire spectrum as a demarcation point to obtain divided sub-channels;
step three, cooperative secondary user SUs pair utilization fnSensing the divided sub-channels, and sending a local sensing result and the detection statistic of the time domain signal energy to a fusion center FC;
and step four, the fusion center adopts a hard decision mechanism to carry out preliminary decision on the use condition of the frequency spectrum according to the local sensing result in the step three, and further adopts a soft decision mechanism to carry out decision according to the time domain signal energy detection statistic.
As a further optimization scheme of the frequency spectrum layering cooperative cognition method for the environment of the Internet of things, the method comprises the following steps of firstly, obtaining a power spectrum of x (t) by adopting a multi-tap spectrum estimation method; the method comprises the following specific steps:
step 1-1, sampling x (t) received in a broadband range at equal intervals of N 'points to obtain a discrete time sequence x (N');
step 1-2, obtaining the frequency spectrum X of the received signal by adopting a multi-tap spectrum estimation methodk(f)；
wherein ,
is Slepian orthogonal sequence, m is an imaginary unit, and f is frequency;
step 1-3, using the spectrum X of X (t)k(f) Obtaining a power spectrum S of x (t)x(f)；
wherein ,ρkAnd K is the degree of freedom which can control the variance of the multi-tap spectrum estimation.
As a further optimization scheme of the frequency spectrum layering cooperative cognition method for the environment of the Internet of things, the frequency edge f of each sub-band is determined in the second stepnThe method specifically comprises the following steps:
step 2-1, adopting a plurality of groups of different scale factors S, S ═ S1,S2,S3…SJScale factor S at jth scalej＝2jJ is more than or equal to J and is more than or equal to 1, and J is the total number of the scales; determining the frequency edge of the wavelet transform of the jth scale at the nth position
wherein 
f∈(f0,fN)；f0For the start frequency of a continuous broadband spectrum, fNCut-off frequency, V, for a continuous broadband spectrumjFor the function subspace spanned by the j-th scale, n is 1, 2, 3 …N, where N is all | V's over the entire continuous spectrumjSx(f) The number of local maxima of l;
step 2-2, obtaining a plurality of groups of different frequency edges by multi-scale wavelet product
Performing recording, recording
Where n is the same and j is not simultaneously using max { V }jSx(f) The number of occurrences Q of the same value; if Q/J is more than or equal to P and P is more than or equal to 50% and less than 100%, the frequency edge of the nth position is determined as
Where n is the same and j is not simultaneously using max { V }jSx(f) The value with the largest occurrence of the same value among all the obtained values is recorded as fn。
As a further optimization scheme of the frequency spectrum layering cooperative cognition method for the environment of the Internet of things, the third step is as follows: sus pair of cooperative sub-users according to fnSensing the divided sub-channels, sending a local sensing result and time domain signal energy detection statistics to a fusion center FC, and recording time domain signal energy detection statistics of the g-th sub-user in a certain section of frequency band to be detected as ygDetecting a statistic y from the time-domain signal energy of the g-th secondary usergAnd a determined first threshold lambdaLA second threshold lambdaHComparing the magnitudes, a first threshold lambdaLLess than a second threshold lambdaH(ii) a Independent decision is made on the usage of the spectrum:
case a), detection statistic y of time-domain signal energy of the g-th secondary userg＞λHJudging the existence of a master user by the g-th secondary user, and sending a local sensing result of the g-th secondary user and the detection statistic of the time domain signal energy of the g-th secondary user to the fusion center;
case b), detection statistic y of time-domain signal energy of the g-th secondary userg＜λLJudging the existence of a master user by the g-th secondary user, and sending a local sensing result of the g-th secondary user and the detection statistic of the time domain signal energy of the g-th secondary user to the fusion center;
case c), detection statistic λ of time-domain signal energy of the g-th secondary userL＜yg＜λHAnd when the perception of the g-th secondary user fails, the local perception result of the g-th secondary user is not sent to the fusion center, but the detection statistic of the time domain signal energy of the g-th secondary user is sent to the fusion center.
As a further optimization scheme of the frequency spectrum layering cooperative cognition method for the environment of the Internet of things, the fourth step is as follows:
step 4-1, after receiving the local sensing results of all secondary users, the fusion center firstly adopts a majority criterion to judge whether a primary user exists on the detected frequency spectrum, and if so, sensing is finished; if no fusion center exists, adopting an improved equal gain combination EGC algorithm to carry out next judgment;
step 4-2, recording the time domain signal energy detection statistics as Y after the data fusion center receives the time domain signal energy detection statistics uploaded by each secondary usertThrough YtAnd a fixed threshold λEGCDetermining a new threshold value lambdaNEWComparison of YtAnd a newly determined threshold value lambdaNEW；
Step b1) determining a new threshold value λNEW；
Determining a new threshold value lambdaNEWThe method comprises the following steps:
using variance of noise
Leading out a noise uncertainty coefficient rho;
w(g)the weight coefficient is the detection quantity of the time domain signal energy of the G-th secondary user, and G represents the total number of the secondary users;
λNEWthe values are specifically as follows:
λNEW＝ρλEGCwhen Y ist≥λEGCAnd rho < 1
λNEW＝λEGCRho when Yt≥λEGCAnd rho is more than or equal to 1
λNEW＝ρλEGCWhen Y ist＜λEGCAnd rho < 1
λNEW＝λEGCRho when Yt<λEGCAnd rho is more than or equal to 1
Namely, it is
λNEW＝min[(λEGC/ρ)，(ρλEGC)]When Y ist≥λEGC
λNEW＝max[(λEGC/ρ)，(ρλEGC)]When Y ist＜λEGC
Step b2)
When Y ist＞λNEWThe fusion center judges that a master user exists;
when Y ist＜λNEW，
Considering the influence of SNR, according to the difference of SNR in the channel, the weight coefficient omega of the g-th secondary user is correspondingly distributedg；
SNRgThe signal-to-noise ratio in the g-th secondary user channel;
further, the average value of the detection statistics of the time domain signal energy of all the secondary users is obtained
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) the method can rapidly divide the whole continuous broadband into a plurality of sub-frequency bands to be detected;
(2) the method improves the accuracy of determining the idle frequency spectrum through a soft and hard combined dual-judgment mechanism.
Drawings
Fig. 1 is a diagram illustrating a step of obtaining a frequency edge by utilizing multi-scale wavelet product transform of a power spectrum obtained by multi-tap spectrum estimation, and further dividing a whole continuous broadband into a plurality of sub-bands to be detected.
FIG. 2 is a diagram of a cooperative sensing and data fusion architecture.
Fig. 3 is a flow chart of a spectrum layering cooperative cognition method oriented to an internet of things environment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
The invention discloses a spectrum layering cooperative cognition method for an Internet of things environment, which is characterized by collecting time domain signals in a continuous broadband spectrum, quickly determining the frequency edge of a whole spectrum sub-band according to the process shown in figure 1 and dividing the whole spectrum into a plurality of sub-bands.
As shown in fig. 2, the cooperative sensing and data fusion architecture diagram is shown, secondary users perform local sensing on divided sub-bands by using an improved dual-threshold detection algorithm, each secondary user sends a local sensing result to a fusion center, and the fusion center judges whether the sub-bands are idle according to a soft-hard combined decision mechanism.
Fig. 3 is a complete flowchart of a spectrum layered cooperative cognition method oriented to an internet of things environment, and the method includes the following steps:
step 301: determining a section of broadband spectrum with known starting and cut-off frequencies, collecting time domain signals of a main user in the spectrum, collecting time domain signals in a continuous broadband spectrum, carrying out N' point equal interval sampling on the continuous signals, and obtaining a power spectrum of a received signal by adopting a multi-tap spectrum estimation method.
Step 302: because the local modulus maximum value of the wavelet transform first derivative of the received signal power spectrum represents the singularity of the received signal power spectrum, the multi-scale wavelet product transform is carried out on the received signal power spectrum obtained by multi-tap spectrum estimation, and the frequency edge is jointly determined by the modulus maximum value of the first derivative of the formula after the multi-scale wavelet product transform and the multi-tap spectrum estimation. The continuous wideband spectrum is divided into a plurality of sub-bands.
Step 303: all secondary users perform local perception on the divided sub-bands, whether the sub-bands are occupied by the primary user or not is judged through an improved energy detection method, the divided sub-bands are subjected to local perception through dual-threshold energy detection by using cooperative secondary users SUs, local judgment results and detection statistics of time domain signal energy are sent to a Fusion Center (FC) through a report channel, and the detection statistics of the g-th secondary time domain signal energy in a certain section of frequency band to be detected are detected as ygAnd independently judging the use condition of the frequency spectrum according to the detection statistic:
case a), detection statistic y of time-domain signal energy of the g-th secondary userg＞λHThen, the g-th secondary user judges that a main user exists, and the judgment result uses Mg1, sending a local sensing result of the g-th secondary user and a detection statistic of time domain signal energy of the g-th secondary user to a fusion center;
case b), detection statistic y of time-domain signal energy of the g-th secondary userg＜λLWhen the current secondary user is detected to be the primary user, judging whether the primary user exists or not, if so, judging whether the primary user exists or not, if not, judging whether the primary user exists or not, judging whether the judgment result exists, if not, judging whether the judgment result exists, judging whether the primary user exists or not, if not, judging whether the judgment result exists, judging whether the judgment result is represented by 0 Mg, and sending the local perception result of the g-th secondary user and the detection statistic of the time domain signal energy of the g-th secondary user and sending the detection statistic of the local perception result of the g-th secondary user and the time domain signal energy of the g-th secondary user to a fusion center;
case c), detection statistic λ of time-domain signal energy of the g-th secondary userL＜yg＜λHWhen the perception of the g-th secondary user fails, the local perception result of the g-th secondary user is not sent to the fusion center,but the detection statistics of the time domain signal energy of the g-th secondary user are sent to the fusion center.
Step 304: the fusion center firstly uses a hard decision mechanism to carry out preliminary decision on the use condition of the frequency spectrum, then uses a soft decision mechanism to carry out leak repairing decision, and adopts the half-number criterion and the improved equal gain ratio to combine and determine the frequency spectrum cavity; the judging step comprises the following steps:
step a), after receiving the sensing result of each node, the fusion center firstly adopts the majority criterion to carry out judgment. And (3) carrying out summation statistics on the reported results: msum＝∑MgWhen the frequency spectrum main user exists H1 sensing is finished, the frequency spectrum main user is detected to be more than or equal to J/2; msum＝∑MgAnd (5) the fusion center adopts an improved Equal Gain Combination (EGC) algorithm to carry out the next judgment.
Step b), the data fusion center receives the perception statistics uploaded by each perception node to obtain judgment statistics YtBy deciding on the statistic YtAnd a fixed threshold λEGCDetermining a new threshold value lambdaNEWComparison of YtAnd a newly determined threshold value lambdaNEW；
Step b1) determining a new threshold value λNEW；
Determining a new threshold value lambdaNEWThe method comprises the following steps:
using variance of noise
Leading out a noise uncertainty coefficient rho;
w(g)the weight coefficient is the detection quantity of the time domain signal energy of the G-th secondary user, and G represents the total number of the secondary users;
λNEWthe values are specifically as follows:
λNEW＝ρλEGCwhen Y ist≥λEGCAnd rho < 1
λNEW＝λEGCRho when Yt≥λEGCAnd rho is more than or equal to 1
λNEW＝ρλEGCWhen Y ist＜λEGCAnd rho < 1
λNEW＝λEGCRho when Yt＜λEGCAnd rho is more than or equal to 1
Namely, it is
λNEW＝min[(λEGC/ρ),(ρλEGC)]When Y ist≥λEGC
λNEW＝max[(λEGC/ρ),(ρλEGC)]When Y ist＜λEGC
Step b2)
When Y ist＞λNEWThe fusion center judges that a master user exists;
when Y ist＜λNEW,
Considering the influence of SNR, according to the difference of SNR in the channel, the weight coefficient omega of the g-th secondary user is correspondingly distributedg；
SNRgThe signal-to-noise ratio in the g-th secondary user channel;
further, the average value of the detection statistics of the time domain signal energy of all the secondary users is obtained
Due to the sudden change of the primary user signal, the strength of the primary user signal received by the sensing node may suddenly decrease to a small strength at a certain detection moment. In this case, the decision statistic obtained after the EGC performs weight summation is also lower than the threshold, so that an erroneous judgment that the primary user does not exist is obtained. In order to avoid the false detection, an improved energy detection algorithm is adopted, because the current sensing period has a certain correlation with all sensing periods, the detection performance of the fusion center can be improved, and the complexity of the algorithm is not increased by the improvement.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (5)
1. A frequency spectrum layered cooperative cognition method for the environment of the Internet of things is characterized by comprising the following steps:
acquiring a time domain signal x (t) in a continuous broadband spectrum, carrying out N-point equal-interval sampling on the x (t) to obtain a discrete time sequence x (N '), and obtaining a power spectrum of the x (t) by adopting a multi-tap spectrum estimation method on the discrete time sequence x (N') to obtain a frequency band of the whole spectrum; wherein N 'represents discrete time, and N' is the number of sampling points;
step two, performing multi-scale wavelet product transformation on the power spectrum of x (t) obtained by the multi-tap spectrum estimation method, and determining the frequency edge f of each sub-band by using the modulo maximum of the first derivative of the formula sub-obtained after the multi-scale wavelet product transformationnUsing fnDividing the entire spectrum as a demarcation point to obtain divided sub-channels;
step three, cooperative secondary user SUs pair utilization fnSensing the divided sub-channels, and sending a local sensing result and the detection statistic of the time domain signal energy to a fusion center FC;
and step four, the fusion center adopts a hard decision mechanism to carry out preliminary decision on the use condition of the frequency spectrum according to the local sensing result in the step three, and further adopts a soft decision mechanism to carry out decision according to the time domain signal energy detection statistic.
2. The Internet of things environment-oriented spectrum layering cooperative cognition method according to claim 1, characterized in that, in the first step, a multi-tap spectrum estimation method is adopted to obtain a power spectrum of x (t); the method comprises the following specific steps:
step 1-1, sampling x (t) received in a broadband range at equal intervals of N 'points to obtain a discrete time sequence x (N');
step 1-2, obtaining the frequency spectrum X of the received signal by adopting a multi-tap spectrum estimation methodk(f)；
wherein ,
is Slepian orthogonal sequence, m is an imaginary unit, and f is frequency;
step 1-3, using the spectrum X of X (t)k(f) Obtaining a power spectrum S of x (t)x(f)；
wherein ,ρkAnd K is the degree of freedom which can control the variance of the multi-tap spectrum estimation.
3. The Internet of things environment-oriented spectrum layering cooperative cognition method according to claim 2, characterized in that in the second step, the frequency edge f of each sub-band is determinednThe method specifically comprises the following steps:
step 2-1, adopting a plurality of groups of different scale factors S, S ═ S1，S2，S3…SJScale factor S at jth scalej＝2jJ is not less than J and not less than 1, J isThe total number of scales; determining the frequency edge of the wavelet transform of the jth scale at the nth position
wherein 
f0For the start frequency of a continuous broadband spectrum, fNCut-off frequency, V, for a continuous broadband spectrumjFor the function subspace spanned by the j-th scale, N is 1, 2, 3 … N, where N is all | V's in the entire continuous spectrumjSx(f) The number of local maxima of l;
step 2-2, obtaining a plurality of groups of different frequency edges f by multi-scale wavelet productn jRecord is made, record fn jWhere n is the same and j is not simultaneously using max { V }jSx(f) The number of occurrences Q of the same value; if Q/J is more than or equal to P, P is more than or equal to 50 percent<100%, the frequency edge of the nth position is determined as
Where n is the same and j is not simultaneously using max { V }jSx(f) The value with the largest occurrence of the same value among all the obtained values is recorded as fn。
4. The internet of things environment-oriented spectrum layering cooperative cognition method according to claim 1, characterized in that the third step is as follows: sus pair of cooperative sub-users according to fnSensing the divided sub-channels, sending a local sensing result and time domain signal energy detection statistics to a fusion center FC, and recording time domain signal energy detection statistics of the g-th sub-user in a certain section of frequency band to be detected as ygDetecting a statistic y from the time-domain signal energy of the g-th secondary usergAnd a determined first threshold lambdaLA second threshold lambdaHComparing the magnitudes, a first threshold lambdaLLess than a second threshold lambdaH(ii) a Independent decision for spectrum usage:
Case a), detection statistic y of time-domain signal energy of the g-th secondary userg＞λHJudging the existence of a master user by the g-th secondary user, and sending a local sensing result of the g-th secondary user and the detection statistic of the time domain signal energy of the g-th secondary user to the fusion center;
case b), detection statistic y of time-domain signal energy of the g-th secondary userg＜λLJudging the existence of a master user by the g-th secondary user, and sending a local sensing result of the g-th secondary user and the detection statistic of the time domain signal energy of the g-th secondary user to the fusion center;
case c), detection statistic λ of time-domain signal energy of the g-th secondary userL＜yg＜λHAnd when the perception of the g-th secondary user fails, the local perception result of the g-th secondary user is not sent to the fusion center, but the detection statistic of the time domain signal energy of the g-th secondary user is sent to the fusion center.
5. The Internet of things environment-oriented spectrum layering cooperative cognition method according to claim 4, characterized in that the fourth step is as follows:
step 4-1, after receiving the local sensing results of all secondary users, the fusion center firstly adopts a majority criterion to judge whether a primary user exists on the detected frequency spectrum, and if so, sensing is finished; if no fusion center exists, adopting an improved equal gain combination EGC algorithm to carry out next judgment;
step 4-2, recording the time domain signal energy detection statistics as Y after the data fusion center receives the time domain signal energy detection statistics uploaded by each secondary usertThrough YtAnd a fixed threshold λEGCDetermining a new threshold value lambdaNEWComparison of YtAnd a newly determined threshold value lambdaNEW；
Step b1) determining a new threshold value λNEW；
Determining a new threshold value lambdaNEWThe method comprises the following steps:
using variance of noise
Leading out a noise uncertainty coefficient rho;
w (G) is a weight coefficient of the time domain signal energy detection quantity of the G-th secondary user, and G represents the total number of the secondary users;
λNEWthe values are specifically as follows:
λNEW＝ρλEGCwhen Y ist≥λEGCAnd rho < 1
λNEW＝λEGCRho when Yt≥λEGCAnd rho is more than or equal to 1
λNEW＝ρλEGCWhen Y ist＜λEGCAnd rho < 1
λNEW＝λEGCRho when Yt＜λEGCAnd rho is more than or equal to 1
Namely, it is
λNEW＝min[(λEGC/ρ)，(ρλEGC)]When Y ist≥λEGC
λNEW＝max[(λEGC/ρ)，(ρλEGC)]When Y ist＜λEGC
Step b2)
When Y ist＞λNEWThe fusion center judges that a master user exists;
when Y ist＜λNEW，
Considering the influence of SNR, according to the difference of SNR in the channel, the weight coefficient omega of the g-th secondary user is correspondingly distributedg；
SNRgThe signal-to-noise ratio in the g-th secondary user channel;
further, time domain signals of all secondary users are obtainedMean value of energy detection statistic
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