CN116318478A - Energy efficiency-based normalized unmanned aerial vehicle frequency spectrum sensing method and system - Google Patents
Energy efficiency-based normalized unmanned aerial vehicle frequency spectrum sensing method and system Download PDFInfo
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Abstract
The invention relates to an unmanned aerial vehicle spectrum sensing technology, in particular to an energy efficiency-based normalized unmanned aerial vehicle spectrum sensing method and system, wherein the method comprises the following steps: the cognitive unmanned aerial vehicle perceives the frequency band, perceived data is reported to the central unmanned aerial vehicle, the central unmanned aerial vehicle carries out average and normalization processing judgment on the perceived data, if the judgment result is idle, the perceived frequency band is accessed for communication, otherwise, the next frame begins to perceive the frequency band again; classifying the spectrum sensing result of the unmanned aerial vehicle, and calculating the average throughput sum of each frame of the cognitive unmanned aerial vehicle network according to the classification result of the spectrum sensing; and defining the energy efficiency as the reciprocal of energy consumed per bit under the unit bandwidth, calculating the energy efficiency, and adjusting the perception time of the unmanned aerial vehicle according to the energy efficiency. The invention can realize high-performance and low-consumption spectrum sensing of the frequency band under the condition of high dynamic change of background noise.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle spectrum sensing, in particular to an energy efficiency-based normalized unmanned aerial vehicle spectrum sensing method and system.
Background
As wireless communication devices increase, limited wireless spectrum resources are increasingly scarce. The cognitive radio technology enables different users to use the same spectrum resource, namely spectrum sharing. Spectrum sensing is to find out idle spectrum resources called as "spectrum holes" in multiple dimensions such as time domain, frequency domain and space domain as precisely as possible for allocation and utilization in complex and changeable electromagnetic environments.
During the unmanned aerial vehicle's mission, the spectrum sensing algorithm needs to be robust against the effects of noise power and signal-to-noise ratio uncertainty due to the changing environment. Furthermore, the introduction of spectrum sensing technology will lead to further increase of perceived energy consumption of the unmanned aerial vehicle in case of energy limitation. The miniature rotorcraft typically operates in a battery powered mode, and limited battery energy has become a major limiting factor in the deployment of the drone.
The existing normalized cooperative spectrum sensing algorithm has the capability of adapting to high dynamic change of background noise, but the energy efficiency problem in a plurality of unmanned aerial vehicle cooperative scenes is not considered. By optimizing the setting of parameters such as sensing time, fusion threshold and the like, the spectrum sensing with low energy consumption and high detection performance under the condition of high dynamic change of background noise is realized.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an energy efficiency-based normalized unmanned aerial vehicle frequency spectrum sensing method and system, which are suitable for a cooperative sensing scene of a plurality of unmanned aerial vehicles, take energy efficiency and detection probability as optimization targets, design a reasonable cooperative sensing strategy, and reduce resource expenditure while improving anti-noise performance.
The invention aims at realizing the energy efficiency-based normalized unmanned aerial vehicle frequency spectrum sensing method by the following technical scheme, which comprises the following steps:
the cognitive unmanned aerial vehicle perceives the frequency bands, the perceiving data are reported to the central unmanned aerial vehicle one by one, the central unmanned aerial vehicle carries out average and normalization processing judgment on the perceiving data, if the judgment result is idle, the perceived frequency bands are accessed for communication, otherwise, the next frame starts to perceive the frequency bands again;
classifying the spectrum sensing result of the unmanned aerial vehicle, and calculating the average throughput sum of each frame of the cognitive unmanned aerial vehicle network according to the classification result of the spectrum sensing;
and defining the energy efficiency as the reciprocal of energy consumed per bit under the unit bandwidth, calculating the energy efficiency, and adjusting the perception time of the unmanned aerial vehicle according to the energy efficiency.
Preferably, the frequency band sensing process comprises the steps of:
designing a cognitive unmanned aerial vehicle network, wherein the designed cognitive unmanned aerial vehicle network comprises N cognitive unmanned aerial vehicles and a central unmanned aerial vehicle, and the cognitive unmanned aerial vehicle carries out frequency spectrum sensing on a circle taking a main user transmitter as a center;
filtering the received signals at the sensing nodes of each cognitive unmanned aerial vehicle to obtain signals in a sensing frequency range; sampling the signals in the sensing frequency range to obtain discrete samples of the signals in the continuous sensing frequency range;
performing periodic chart power spectrum estimation on the discrete samples acquired in the sensing frequency range to obtain spectral line intensity of the discrete samples;
taking half of spectral line intensity for summation and segmentation, and representing a power spectrum; uploading the power spectrums obtained by sampling to a central unmanned aerial vehicle by N cognitive unmanned aerial vehicles to obtain an average value of periodic spectrum estimation of all unmanned aerial vehicles;
uniformly dividing all the unmanned aerial vehicle periodic patterns into a plurality of groups to obtain spectral line intensity of each group; normalizing according to the spectral line intensity of each group and the sum of the estimated average values of all the unmanned aerial vehicle periodic patterns to obtain normalized spectrums;
presetting false alarm probability of a single-segment normalized signal, solving a decision threshold under preset learning setting, and obtaining detection probability of the single-segment normalized signal according to the decision threshold;
and the central unmanned plane adopts a fusion strategy to make a final judgment so as to obtain the false alarm probability and the detection probability.
The system of the invention is realized by adopting the following technical scheme: an energy efficiency based normalized unmanned aerial vehicle spectrum sensing system, comprising:
the cognitive unmanned aerial vehicle network comprises N cognitive unmanned aerial vehicles and a central unmanned aerial vehicle, the cognitive unmanned aerial vehicle perceives the frequency bands, perceived data are reported to the central unmanned aerial vehicle one by one, the central unmanned aerial vehicle carries out average and normalization processing judgment on the perceived data, if the judgment result is idle, the perceived frequency bands are accessed for communication, otherwise, the next frame begins to perceive the frequency bands again;
the sensing result classification module is used for classifying the spectrum sensing result of the unmanned aerial vehicle and calculating the average throughput sum average energy consumption of each frame of the cognitive unmanned aerial vehicle network according to the spectrum sensing classification result;
and the energy efficiency feedback module is used for defining the energy efficiency as the reciprocal of energy consumed by each bit under the unit bandwidth, calculating the energy efficiency and adjusting the perception time of the unmanned aerial vehicle according to the energy efficiency.
Preferably, the cognitive unmanned aerial vehicle network filters the received signals at the perception nodes of each cognitive unmanned aerial vehicle respectively to obtain signals in a perception frequency range; sampling the signals in the sensing frequency range to obtain discrete samples of the signals in the continuous sensing frequency range;
performing periodic chart power spectrum estimation on the discrete samples acquired in the sensing frequency range to obtain spectral line intensity of the discrete samples;
taking half of spectral line intensity for summation and segmentation, and representing a power spectrum; uploading the power spectrums obtained by sampling to a central unmanned aerial vehicle by N cognitive unmanned aerial vehicles to obtain an average value of periodic spectrum estimation of all unmanned aerial vehicles;
uniformly dividing all the unmanned aerial vehicle periodic patterns into a plurality of groups to obtain spectral line intensity of each group; normalizing according to the spectral line intensity of each group and the sum of the estimated average values of all the unmanned aerial vehicle periodic patterns to obtain normalized spectrums;
presetting false alarm probability of a single-segment normalized signal, solving a decision threshold under preset learning setting, and obtaining detection probability of the single-segment normalized signal according to the decision threshold;
and the central unmanned plane adopts a fusion strategy to make a final judgment so as to obtain the false alarm probability and the detection probability.
The invention realizes the spectrum sensing with low energy consumption and high detection performance under the condition of high dynamic change of background noise; compared with the prior art, the following technical effects are achieved:
1. according to the invention, the energy efficiency and the detection probability are used as optimization targets, a reasonable collaborative perception strategy is designed, the influence of the fusion threshold k and the perception time tau of the unmanned aerial vehicle in the center on the optimization targets is researched, and then the optimal parameters are selected, so that the purpose of green energy conservation is achieved on the premise of ensuring the detection performance, the anti-noise performance is improved, and the resource expense is reduced.
2. The invention can realize high-performance and low-consumption spectrum sensing of the frequency band under the condition of high dynamic change of background noise; the sampling sequence is subjected to normalization processing to resist the influence of high dynamic noise on spectrum sensing performance, and meanwhile, the input dimension can be reduced to a certain extent, and the calculated amount is reduced; the soft-combining cooperative spectrum sensing mode is adopted, and compared with single-user spectrum sensing, the mode can overcome the influence of multipath weakness and shadow effect on sensing results.
3. According to the invention, energy efficiency and detection probability are used as optimization targets, a reasonable collaborative perception strategy is designed, the influence of the perception time of the unmanned aerial vehicle and the segmentation number of the normalization algorithm on the optimization targets is researched, and then the optimal parameters are selected, so that the purpose of green energy conservation is achieved on the premise of ensuring the detection performance.
4. The invention can ensure the perception accuracy and reduce unnecessary resource expenditure, and has practical significance for spectrum perception in the unmanned plane scene because most unmanned planes are in a battery power supply mode and the background noise in the task execution process is highly dynamic.
Drawings
Fig. 1 is a network model diagram of a cognitive unmanned aerial vehicle, provided by an embodiment of the present invention, where (a) is a 3D diagram and (b) is a 2D diagram;
FIG. 2 is a graph comparing the obtained energy efficiency with the relation between the sensing time and the segmentation number and with the energy detection algorithm under the condition of taking 1 (1-out-of-N) in N according to the embodiment of the invention;
FIG. 3 is a graph showing the relationship between the obtained energy efficiency and the sensing time and the segmentation number and the comparison with the energy detection algorithm under the condition of taking 2 (2-out-of-N) in N according to the embodiment of the invention;
FIG. 4 is a graph showing the energy efficiency of the sensing method and the energy detection algorithm according to the probability of single decision false alarm;
FIG. 5 is a graph of perceived time versus number of segments for an optimization scenario according to an embodiment of the present invention;
FIG. 6 is a graph of energy efficiency versus number of segments for an optimization scenario according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The embodiment discloses a normalized unmanned aerial vehicle frequency spectrum sensing method based on energy efficiency, a cognitive unmanned aerial vehicle network model is shown in fig. 1, and comprises a plurality of cognitive unmanned aerial vehicles and a central unmanned aerial vehicle, and the method comprises the following steps:
s1, sensing the frequency bands by the cognitive unmanned aerial vehicle, reporting sensing data to the central unmanned aerial vehicle one by one, carrying out average and normalization processing judgment on the sensing data by the central unmanned aerial vehicle, accessing the sensing frequency bands to carry out communication if a judgment result is idle, for example, entering a data transmission stage of a cognitive user, otherwise, waiting until the next frame starts to sense the frequency bands again.
In the sensing stage, all the cognitive unmanned aerial vehicles perform sensing operation simultaneously; in the reporting stage, all the cognitive unmanned aerial vehicles report the perception data to the central unmanned aerial vehicle in sequence, and finally, whether the cognitive user can access the perceived frequency band for communication is determined according to the judgment result of the central unmanned aerial vehicle.
In this embodiment, the frequency band sensing process includes the following steps:
(1) The cognitive unmanned aerial vehicle network is designed, the designed cognitive unmanned aerial vehicle network mainly comprises N cognitive unmanned aerial vehicles UAVs and a central unmanned aerial vehicle FC formation networking, and as shown in fig. 1, the cognitive unmanned aerial vehicle carries out frequency spectrum sensing on a circle taking a main user transmitter PU as a center. Assuming that the channel is a line of sight (LoS) channel, the channel gain h ij (r ij ) Expressed as:
wherein r is ij Representing the distance between node i and node j; PL (PL) ij (r ij ) Representing the path loss between node i and node j, the expression is:
wherein c represents the speed of light, f represents the carrier frequency, L LoS The loss is added to the average of the link. The signals received by the drone may be written as:
where w (t) is Gaussian white noise, s (t) is a signal sent by a main user, h PU (t) is the channel gain between the signal sent by the main user and the unmanned plane, H 0 Representing that the primary user does not occupy the frequency band, H 1 Indicating that the primary user is occupying the band of communications.
In this step, it is assumed that the flying heights and speeds of all the cognitive unmanned aerial vehicles are equal, and the distance between the cognitive unmanned aerial vehicles is negligible with respect to the distance of the unmanned aerial vehicle to the main user. Furthermore, since the flight time(s) of the drone is much higher than the channel coherence time (ms), the system performance of the instantaneous channel implementation cannot be studied. Therefore, this embodiment focuses mainly on the average statistics of the channel, but only on large-scale fading in the channel.
(2) Filtering the received signals at the sensing nodes of each cognitive unmanned aerial vehicle to obtain signals x in the range of the sensing frequency band n (t), wherein n=1, 2, …, N represents the number of the drone; sampling the signals in the sensing frequency range to obtain discrete samples x of the signals in the continuous sensing frequency range n (m),m＝0,1,…,M-1。
(3) Performing periodic chart power spectrum estimation on discrete samples acquired in the sensing frequency range:
obtaining the spectral line intensity of the discrete sample. Wherein the periodic spectrum estimation is performed on the sampled signal by taking a time average to obtain a flatter power spectrum.
(4) Due to the sampled signal, i.e. discrete samples x n (M) is a real signal whose power spectrum is symmetrical, so that only half of the spectral line intensity is needed for addition and segmentation, i.e. only M/2P are needed n (k) The power spectrum can be represented. For a signal received by the drone, its distribution can be expressed as:
therefore, there are:
p when the primary user is not present avg (k) Mean and variance of (a) are respectively
P when the primary user exists avg (k) Mean and variance of>
Uploading the power spectrums obtained by sampling to a central unmanned aerial vehicle by N cognitive unmanned aerial vehicles to obtain average values of periodic spectrum estimation of N (i.e. all) unmanned aerial vehicles:
(5) Uniformly dividing all the unmanned aerial vehicle periodic patterns into a plurality of groups to obtain spectral line intensity of each group; and carrying out normalization processing according to the spectral line intensity of each group and the sum of the estimated average values of all the unmanned aerial vehicle periodic patterns to obtain a normalized spectrum.
The sum of all spectral line intensities (i.e. the average of all unmanned aerial vehicle periodic map estimates) is defined as P all I.e.
Uniformly dividing all the unmanned aerial vehicle periodic patterns into U groups, wherein the spectral line intensity of each group is marked as P seg (u), then:
P seg (u) and P all The ratio can be expressed as
u＝0,1,…,N-1，/>
The normalized spectrum.
In the present embodiment, a random variable X (u) =p is set seg (u) and y=p all . According to the definition of the central poleIn theory, when the number of sampling points is large enough, the random variables X (u) and Y approach a Gaussian distribution. Thus, the mean and variance of X (u) are respectively
Mean and variance of Y are +.>
Under the condition that the main user does not use the frequency band, the central unmanned aerial vehicle averages the frequency band signals detected by each received cognitive unmanned aerial vehicle and then carries out normalization processing to obtain values, and the distribution of the values is irrelevant to noise power, so that the normalized frequency spectrum sensing algorithm is more robust under the condition that the noise level is highly changed.
(6) The false alarm probability of the single-segment normalized signal is preset, the decision threshold under the preset training setting is obtained according to the Neyman-Pearson criterion, and the detection probability of the single-segment normalized signal can be obtained according to the decision threshold.
The false alarm probability and the detection probability of the single-section normalized signal are respectively as follows:
the false alarm probability of a given single-stage signal is
The threshold gamma can be calculated as:
wherein the method comprises the steps of
Phi (·) is the error function, phi -1 (. Cndot.) is the inverse of the error function.
(7) And the central unmanned aerial vehicle makes a final judgment according to a k (k-out-of-N) fusion criterion in the N to obtain the false alarm probability and the detection probability.
In this embodiment, the decision result obtained by each segment adopts a k-out-of-N fusion strategy, so as to obtain the false alarm probability Q F And detection probability Q D The method comprises the following steps of:
where k is the fusion threshold, u=1, 2, …, U, l=k, 1, …, U-1.
S2, classifying the spectrum sensing result of the unmanned aerial vehicle, and calculating the average throughput sum average energy consumption of each frame of the cognitive unmanned aerial vehicle network according to the spectrum sensing classification result.
The spectrum sensing results of the unmanned aerial vehicle are divided into four types: the unmanned aerial vehicle accurately detects that the sensing frequency band is idle; the unmanned aerial vehicle accurately detects that the sensing frequency band is occupied; the unmanned aerial vehicle has the condition of missing detection, and the sensing frequency band is idle by error detection; the unmanned aerial vehicle presents the condition of false alarm, and the perception frequency channel is occupied in the false detection.
When the unmanned aerial vehicle accurately detects that the sensing frequency band is occupied and the unmanned aerial vehicle generates false alarm, and the sensing frequency band is erroneously detected to be occupied, the cognitive user does not send signals, and the throughput is 0 at the moment. When the unmanned aerial vehicle accurately detects the conditions that the sensing frequency band is idle and the unmanned aerial vehicle has missed detection, the throughput of the unmanned aerial vehicle is far greater than that of the unmanned aerial vehicle due to the influence of the interference signals of the main user, and the throughput of the unmanned aerial vehicle can be ignored when the energy efficiency is calculated.
The average throughput and the average energy consumption of each frame of the cognitive unmanned aerial vehicle network are calculated according to four frequency spectrum sensing results, wherein the average throughput and the average energy consumption of each frame of the cognitive unmanned aerial vehicle network are as follows:
E＝NτP s +Nt r P r +NT(P hov +P fly )+(T-τ-Nt r )P U [π 0 (1-Q F )+π 1 (1-Q D )]
where τ represents the sensing time, t r The report time of a single unmanned aerial vehicle is represented, and T represents the time of each frame; pi 0 Representing the probability that the primary user of the channel is not occupied, Q F And Q D The false alarm probability and the detection probability are respectively determined by the central unmanned aerial vehicle according to the k (k-out-of-N) taking criterion in N, and h cs And (t) represents the large-scale channel gain between the drone and the ground receiver. P (P) u Is the transmitting power of unmanned aerial vehicle, P s Is the perceived power of the unmanned aerial vehicle, P r Is the transmission power of the unmanned aerial vehicle. P (P) hov And P fly Representing the flying power and the hovering power of the unmanned aerial vehicle respectively.
S3, defining the energy efficiency as the reciprocal of the energy consumed per bit under the unit bandwidth, and obtaining the energy efficiency eta EE Is a display expression of (a):
calculating energy efficiency according to the expression; the unmanned aerial vehicle perception time is adjusted according to energy efficiency, so that both perception and energy efficiency are optimized.
In order to verify the excellent performance of the normalized sensing method under the high dynamic noise background, the normalized sensing method is compared with the existing energy detection algorithm, and the normalized sensing method is concretely as follows:
(1) The parameters were set as shown in the following table:
| parameters (parameters) | Value of | Parameters (parameters) | Value of | Parameters (parameters) | Value of |
| R/m | 340 | T/s | 0.1 | π 1 | 0.3 |
| H/m | 60 | N | 8 | L LoS | 3 |
| f s /kHz | 300 | d/m | 5 | t r /ms | 0.1 |
| P r /mW | 10 | P U /W | 6 | P f | 0.01 |
| P S /mW | 40 | P P /W | 10 | v/m·s -1 | 10 |
| P fly /W | 5 | P zero /W | 0 | f c /GHz | 2.4 |
| m tot /kg | 0.6 | r p /m | 0.2 | α 0 | π/3 |
(2) According to the parameters given in the table, simulation is performed to obtain fig. 2 and fig. 3, and it can be seen from the graph that the judgment mode of taking 2 (2-out-of-N) in N adopted by the normalized sensing method has better effect than the judgment mode of taking 1 (1-out-of-N) in N adopted by the existing energy detection algorithm, so that the fusion threshold is set to 2 for simulation later.
(3) In order to further observe whether the normalized sensing method can be applied to the problem of energy efficiency optimization in a noise high-dynamic scene, the signal to noise ratio of signals received by the unmanned aerial vehicle is randomly set to be from 5dB to 10dB, and the obtained result is shown in fig. 4.
(4) From the previous simulation results, it can be derived that: the sensing time tau and the segmentation number U both have an effect on the energy of the normalization algorithm. The simulation results in the optimal perception time and energy efficiency under different segmentation numbers as shown in fig. 5 and 6 respectively.
Example 2
Based on the same inventive concept as embodiment 1, this embodiment provides an energy efficiency-based normalized unmanned aerial vehicle spectrum sensing system, including:
the cognitive unmanned aerial vehicle network comprises N cognitive unmanned aerial vehicles and a central unmanned aerial vehicle, the cognitive unmanned aerial vehicle perceives the frequency bands, perceived data are reported to the central unmanned aerial vehicle one by one, the central unmanned aerial vehicle carries out average and normalization processing judgment on the perceived data, if the judgment result is idle, the perceived frequency bands are accessed for communication, otherwise, the next frame begins to perceive the frequency bands again;
the sensing result classification module is used for classifying the spectrum sensing result of the unmanned aerial vehicle and calculating the average throughput sum average energy consumption of each frame of the cognitive unmanned aerial vehicle network according to the spectrum sensing classification result;
and the energy efficiency feedback module is used for defining the energy efficiency as the reciprocal of energy consumed by each bit under the unit bandwidth, calculating the energy efficiency and adjusting the perception time of the unmanned aerial vehicle according to the energy efficiency.
The cognitive unmanned aerial vehicle network filters the received signals at the perception nodes of each cognitive unmanned aerial vehicle respectively to obtain signals in a perception frequency range; sampling the signals in the sensing frequency range to obtain discrete samples of the signals in the continuous sensing frequency range;
performing periodic chart power spectrum estimation on the discrete samples acquired in the sensing frequency range to obtain spectral line intensity of the discrete samples;
taking half of spectral line intensity for summation and segmentation, and representing a power spectrum; uploading the power spectrums obtained by sampling to a central unmanned aerial vehicle by N cognitive unmanned aerial vehicles to obtain an average value of periodic spectrum estimation of all unmanned aerial vehicles;
uniformly dividing all the unmanned aerial vehicle periodic patterns into a plurality of groups to obtain spectral line intensity of each group; normalizing according to the spectral line intensity of each group and the sum of the estimated average values of all the unmanned aerial vehicle periodic patterns to obtain normalized spectrums;
presetting false alarm probability of a single-segment normalized signal, solving a decision threshold under preset learning setting, and obtaining detection probability of the single-segment normalized signal according to the decision threshold;
and the central unmanned plane adopts a fusion strategy to make a final judgment so as to obtain the false alarm probability and the detection probability.
In this embodiment, a more detailed implementation process of implementing spectrum sensing through each module is referred to embodiment 1, and is not repeated.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made in the above embodiments by those skilled in the art without departing from the spirit and principles of the invention.
Claims (9)
1. The energy efficiency-based normalized unmanned aerial vehicle frequency spectrum sensing method is characterized by comprising the following steps of:
the cognitive unmanned aerial vehicle perceives the frequency bands, the perceiving data are reported to the central unmanned aerial vehicle one by one, the central unmanned aerial vehicle carries out average and normalization processing judgment on the perceiving data, if the judgment result is idle, the perceived frequency bands are accessed for communication, otherwise, the next frame starts to perceive the frequency bands again;
classifying the spectrum sensing result of the unmanned aerial vehicle, and calculating the average throughput sum of each frame of the cognitive unmanned aerial vehicle network according to the classification result of the spectrum sensing;
and defining the energy efficiency as the reciprocal of energy consumed per bit under the unit bandwidth, calculating the energy efficiency, and adjusting the perception time of the unmanned aerial vehicle according to the energy efficiency.
2. The spectrum sensing method according to claim 1, wherein the process of frequency band sensing comprises the steps of:
designing a cognitive unmanned aerial vehicle network, wherein the designed cognitive unmanned aerial vehicle network comprises N cognitive unmanned aerial vehicles and a central unmanned aerial vehicle, and the cognitive unmanned aerial vehicle carries out frequency spectrum sensing on a circle taking a main user transmitter as a center;
filtering the received signals at the sensing nodes of each cognitive unmanned aerial vehicle to obtain signals in a sensing frequency range; sampling the signals in the sensing frequency range to obtain discrete samples of the signals in the continuous sensing frequency range;
performing periodic chart power spectrum estimation on the discrete samples acquired in the sensing frequency range to obtain spectral line intensity of the discrete samples;
taking half of spectral line intensity for summation and segmentation, and representing a power spectrum; uploading the power spectrums obtained by sampling to a central unmanned aerial vehicle by N cognitive unmanned aerial vehicles to obtain an average value of periodic spectrum estimation of all unmanned aerial vehicles;
uniformly dividing all the unmanned aerial vehicle periodic patterns into a plurality of groups to obtain spectral line intensity of each group; normalizing according to the spectral line intensity of each group and the sum of the estimated average values of all the unmanned aerial vehicle periodic patterns to obtain normalized spectrums;
presetting false alarm probability of a single-segment normalized signal, solving a decision threshold under preset learning setting, and obtaining detection probability of the single-segment normalized signal according to the decision threshold;
and the central unmanned plane adopts a fusion strategy to make a final judgment so as to obtain the false alarm probability and the detection probability.
3. The spectrum sensing method according to claim 2, wherein performing periodogram power spectrum estimation on discrete samples acquired within the sensing frequency band range is:
wherein the discrete samples are x n (m),m＝0,1,…,M-1。
4. A spectrum sensing method according to claim 3, wherein the average value of the unmanned aerial vehicle periodic map estimates is:
where n=1, 2, …, N represents the number of the drone.
5. The spectrum sensing method of claim 2, wherein the spectral line intensity of each group is:
the normalized spectrum is:
wherein P is all The sum of the average values is estimated for all unmanned aerial vehicle periodic patterns.
6. The spectrum sensing method according to claim 2, wherein for the signal received by the unmanned aerial vehicle, the distribution condition is expressed as:
x(n):
therefore, there are:
mean value P of unmanned aerial vehicle periodic map estimation when main user does not exist avg (k) Mean and variance of (a) are respectively
P when the primary user exists avg (k) Mean and variance of>
Let X (u) =p be a random variable seg (u) and y=p all The mean and variance of the random variable X (u) are respectively
The mean and variance of the random variable Y are +.>
The false alarm probability and the detection probability of the single-section normalized signal are respectively as follows:
the false alarm probability of a given single-stage signal is
The threshold γ is calculated as:
wherein the method comprises the steps of
Phi (·) is the error function, phi -1 (. Cndot.) is the inverse of the error function.
7. The spectrum sensing method of claim 2, wherein the false alarm probability Q F And detection probability Q D The method comprises the following steps of:
where k is the fusion threshold, P f For the false alarm probability of a single segment normalized signal,
for the detection probability of the single-segment normalized signal, u=1, 2, …, U, l=k, 1, …, U-1.
8. Energy efficiency-based normalized unmanned aerial vehicle frequency spectrum sensing system, characterized by comprising:
the cognitive unmanned aerial vehicle network comprises N cognitive unmanned aerial vehicles and a central unmanned aerial vehicle, the cognitive unmanned aerial vehicle perceives the frequency bands, perceived data are reported to the central unmanned aerial vehicle one by one, the central unmanned aerial vehicle carries out average and normalization processing judgment on the perceived data, if the judgment result is idle, the perceived frequency bands are accessed for communication, otherwise, the next frame begins to perceive the frequency bands again;
the sensing result classification module is used for classifying the spectrum sensing result of the unmanned aerial vehicle and calculating the average throughput sum average energy consumption of each frame of the cognitive unmanned aerial vehicle network according to the spectrum sensing classification result;
and the energy efficiency feedback module is used for defining the energy efficiency as the reciprocal of energy consumed by each bit under the unit bandwidth, calculating the energy efficiency and adjusting the perception time of the unmanned aerial vehicle according to the energy efficiency.
9. The spectrum sensing system of claim 8, wherein the cognitive unmanned aerial vehicle network filters the received signals at the sensing nodes of each cognitive unmanned aerial vehicle to obtain signals within a sensing frequency range; sampling the signals in the sensing frequency range to obtain discrete samples of the signals in the continuous sensing frequency range;
performing periodic chart power spectrum estimation on the discrete samples acquired in the sensing frequency range to obtain spectral line intensity of the discrete samples;
taking half of spectral line intensity for summation and segmentation, and representing a power spectrum; uploading the power spectrums obtained by sampling to a central unmanned aerial vehicle by N cognitive unmanned aerial vehicles to obtain an average value of periodic spectrum estimation of all unmanned aerial vehicles;
uniformly dividing all the unmanned aerial vehicle periodic patterns into a plurality of groups to obtain spectral line intensity of each group; normalizing according to the spectral line intensity of each group and the sum of the estimated average values of all the unmanned aerial vehicle periodic patterns to obtain normalized spectrums;
presetting false alarm probability of a single-segment normalized signal, solving a decision threshold under preset learning setting, and obtaining detection probability of the single-segment normalized signal according to the decision threshold;
and the central unmanned plane adopts a fusion strategy to make a final judgment so as to obtain the false alarm probability and the detection probability.
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