CN115664563A - Passive cooperative spectrum sensing method based on energy characteristic geometric symmetry - Google Patents
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Abstract
The invention discloses a passive cooperative spectrum sensing method based on energy characteristic geometric symmetry, and belongs to the technical field of wireless communication. The method designs detection statistics by using the symmetry of the received signal energy at the cognitive user in a geometric space, does not depend on prior information such as signals, channels and the instantaneous signal-to-noise ratio of each cognitive user, avoids bandwidth and power loss caused by the fact that the cognitive user transmits energy information to a fusion center, and is low in complexity. Compared with the sensing method mentioned in the technical background, the method has higher detection probability and can effectively solve the problem of hiding the terminal.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a cooperative spectrum sensing method in a cognitive radio network.
Background
The electromagnetic spectrum belongs to strategic natural resources, is an important resource of an electromagnetic space, is an important medium of wireless information transmission, is a core operational element in modern war, and is an important strategic resource for determining the victory or defeat of the war. With the rapid development of equipment informatization and networking, a battlefield electromagnetic environment is increasingly complex, various electronic devices such as radars, communication, electronic countermeasures and the like in the environment are densely distributed, electromagnetic countermeasures of both enemies and my parties are fierce, and electromagnetic spectrum resources face the following serious challenges: (1) The development process of the high-frequency bandwidth resource is slow, and the unallocated bandwidth resource is very limited. (2) Constrained by the static spectrum allocation strategy, the authorized spectrum can only be used by the authorized user, and the spectrum utilization rate is low. (3) The electromagnetic environment and the radiation source are increasingly complex, the interference phenomenon is more and more, and the electromagnetic environment and the spectrum situation of a battlefield need to be accurately sensed and mastered in real time. The cognitive radio technology is introduced into the electromagnetic environment of a battlefield, so that the utilization efficiency of frequency spectrum is greatly improved, and the method has great significance for relieving the current situation of scarce frequency spectrum resources. In cognitive radio, spectrum sensing is a key prerequisite for implementing dynamic spectrum access, and the aim is to enable secondary users to reuse spectrum licensed to primary users. In this regard, a cognitive user is required to accurately detect the presence of a primary user signal to identify the occupancy state of an authorized frequency band.
Research on spectrum sensing methods is carried out by many research institutes at home and abroad, including local spectrum sensing and cooperative spectrum sensing. The local spectrum sensing is mainly that a single cognitive user confirms the occupation state of a main user on the current spectrum by using a received signal. In an actual scene, the problem of hiding a terminal is caused by deep fading, shading and the like, and the perception accuracy of the technology in the scene is low. The cooperative sensing technology monitors the occupation state of a main user frequency spectrum by utilizing a plurality of cognitive users and a fusion center in a cooperative (passive cooperation) mode. The technology utilizes the spatial diversity among cognitive users, has higher perception precision and reliability, can effectively solve the problem of hidden terminals, and is more attractive. The Shate King science and technology university provides a cooperative sensing algorithm based on Equal gain combination, the algorithm firstly calculates the received signal energy of each cognitive user, then feeds the energy vector of the cognitive user back to a fusion center, and finally the fusion center performs Equal weight addition on the energy of each cognitive user and makes a decision (D.Hamza, S.Aissa, and G.Aniba, equal gain combining for cooperative sensing in coherent radio networks, IEEE trans.Wireless Commun, vol.13, no.8, pp.4334-4345, and Aug.2014.). Under the assumption that the main user signal is a gaussian signal and the signal-to-noise ratio at each cognitive user is known, ningbo university proposes an energy-based soft fusion algorithm by using the NP criterion, which is heavily dependent on the instantaneous signal-to-noise ratio information at the cognitive user and is difficult to realize in the actual scene (J.Tong, M.jin, Q.Guo and Y.Li, cooperative spread sensing: A blind and soft fusion detector, IEEE trans.Wireless Commun, vol.17, no.4, pp.2726-2737, apr.2018.). The two cooperative spectrum sensing algorithms both feed back the energy at the cognitive user to the fusion center, and require higher bandwidth and power loss. Therefore, it is of great value to research a cooperative sensing method which is low in implementation complexity and does not depend on prior information such as signals, channels, signal-to-noise ratio and the like.
Disclosure of Invention
In order to solve the problems, the invention provides a method for sensing a passive cooperative spectrum based on energy characteristic geometric symmetry, which comprises the steps of firstly establishing a cooperative sensing model suitable for simultaneous existence of large and small scale fading, then respectively determining an energy mean value by using a received signal at each cognitive user, secondly feeding the energy mean value back to a fusion center, constructing a detection statistic based on the energy characteristic geometric symmetry, finally comparing the detection statistic with a threshold for judgment, judging whether a master user signal exists, and further deducing an occupation state authorized to a master user frequency band.
The technical scheme of the invention is as follows:
a passive cooperative spectrum sensing method based on energy feature geometric symmetry comprises the following steps:
step 1: establishing a cooperative perception model suitable for simultaneous existence of large and small scale fading;
the application scene of the perception model is as follows: the system comprises a master user, N cognitive users and a fusion center; the signal received by the nth cognitive user at time k is denoted as r n (k)；
Wherein H 1 Indicating the presence of a primary user signal, H 0 Indicating that a primary user signal is absent; s n (k) Represents the primary user signal, w, received by the nth cognitive user at time k n (k) Is additive complex gaussian noise; g n Representing a large-scale fading, h, between the primary user and the nth cognitive user n (k) Representing a small-scale fade between the primary user and the nth cognitive user.
For large-scale fading g between a master user and an nth cognitive user n And is distributed according to a log-normal distribution,
wherein f is gn (x) Represents the large-scale fading channel distribution, mu and sigma, between the primary user and the nth cognitive user 2 Respectively representing the mean and the variance of ln x;
small-scale fading channel h between master user and nth cognitive user n (k) Expressed as:
h n (k)＝Aexp(j2πθ)
where θ represents the small-scale fading channel h n (k) Obey a uniform distribution of [0,1 ], the amplitude A obeys a Nakagami-m distribution f A (x) Is shown as
Wherein m and omega respectively represent Nakagami-m channel parameters and average power, and Gamma (·) is a Gamma function;
and 2, step: calculating the average value of the energy of the received signal of each cognitive user;
the average energy E of the received signal at the nth cognitive user in the K sampling points n As shown in the drawing, it is shown that,
wherein, | · | represents an absolute value operator;
calculating the expectation of the average energy of the received signals of each cognitive user,
where E (-) represents the fetch desired operation;
and step 3: constructing a detection statistic;
when the received signal does not contain the main user signal, the energy of the received signal presents a symmetrical characteristic in a geometric space, and a detection statistic is designed by utilizing the characteristic and expressed as:
and 4, step 4: detecting and judging;
the detection statistics designed above are utilized to carry out detection judgment,
wherein gamma is 2 For judging threshold, the detection statistic T and the judgment threshold gamma are used 2 And comparing, judging that the master user signal exists when the detection statistic is larger than the threshold, otherwise, judging that the master user signal does not exist.
The invention provides a passive cooperative spectrum sensing method based on energy characteristic geometric symmetry, which designs detection statistics by using the symmetry of received signal energy at cognitive users in a geometric space, does not depend on prior information such as signals, channels and instantaneous signal-to-noise ratios of each cognitive user, avoids bandwidth and power loss caused by energy information transmission from the cognitive users to a fusion center, and realizes low complexity. Compared with the sensing method mentioned in the technical background, the method has higher detection probability and can effectively solve the problem of hiding the terminal.
Drawings
FIG. 1 is a processing flow diagram of a passive cooperative spectrum sensing method based on geometric symmetry of energy features;
FIG. 2 is a diagram of a cooperative spectrum sensing scenario;
FIG. 3 is a scatter diagram of received signal energy of three cognitive users, and FIG. 3 (a) is H 0 Receiving a signal energy scatter diagram by the next three cognitive users; FIG. 3 (b) is H 1 Receiving a signal energy scatter diagram by the next three cognitive users;
FIG. 4 is a graph of detection probability as a function of signal-to-noise ratio (SNR);
fig. 5 is a graph showing the variation of the detection probability with the number of hidden terminals.
Detailed Description
The invention is described in further detail below with reference to the figures and the detailed description.
A passive cooperative spectrum sensing method based on energy feature geometric symmetry comprises the following steps:
step 1: establishing cooperative perception model suitable for simultaneous existence of large and small scale fading
As shown in fig. 2, the cooperative sensing network includes a primary user, N cognitive users, and a fusion center. A master user sends a signal s, and a signal received by the nth cognitive user at the moment k is represented as
Wherein s is n (k) Represents the primary user signal, w, received by the nth cognitive user at time k n (k) Is additive complex Gaussian noise, h n Is a small-scale fading channel between a main user and an nth cognitive user and is expressed as
h n (k)＝Aexp(j2πθ)
The phase theta of the small scale fading follows a uniform distribution of [0, 1), the amplitude A follows a Nakagami-m distribution,
where m and Ω represent the Nakagami-m channel parameters and the average power, respectively, and Γ (·) is a Gamma function.
g n For a large-scale fading channel between a master user and an nth cognitive user, the statistical modeling is logarithmic-normal distribution,
where μ and σ 2 Respectively representing the mean and variance of ln x.
Step 2: calculating the mean value of the energy of the received signal of each cognitive user
Sampling the received signal of each cognitive user to obtain K sampling points, wherein the average energy of the received signal at the nth cognitive user is expressed as,
the mean (expected) of the energy of the received signal of each cognitive user is expressed as
Taking three cognitive users as an example, under the two conditions of existence and nonexistence of main user signals, the scatter diagram of the received signal energy vector is shown in fig. 3, and it can be seen that when only noise exists in the received signals and no main user signal exists, the three-dimensional energy vector scatter diagram presents a geometric symmetry characteristic similar to a sphere, and when the main user signals exist, the energy vector scatter diagram is distributed radially and irregularly.
And step 3: constructing detection statistics
Based on the analysis result of the step 2, the geometric symmetry characteristic of the energy vector is represented by using a spherical equation, and then the detection statistic is designed and expressed as
With the noise at each cognitive user satisfying the independent equal distribution characteristic, the detection statistics can be further expressed as
And 4, step 4: detection decision
By utilizing the designed detection statistic, the detector is constructed,
wherein, γ 2 Is a decision threshold. According to false alarm probability P fa Size, execution 100/P fa In the secondary experiment, the detection statistics of the non-target condition are obtained through calculation and are arranged in a descending order, and the 100 th detection statistics are taken as a judgment threshold. And comparing the detection statistic with a decision threshold, when the detection statistic is larger than the threshold, judging that a master user signal exists, namely the master user uses the current authorized frequency band for communication, otherwise, judging that the master user signal does not exist, namely the current frequency band is in an idle state, and enabling the cognitive user to use the current frequency band for communication.
Example (b):
setting parameters: the number of the cognitive users is 15, the number of sampling points of each cognitive user in a perception time slot is 100, a signal sent by a master user is a Gaussian signal, a small-scale fading channel obeys Nakagami-m distribution with parameters and average channel gain being, and a large-scale fading channel obeys log-normal distribution with the average value and the variance being. The signal-to-noise ratio SNR is-22 dB to-6 dB, and the false alarm probability is 0.01.
The comparison method comprises the following steps: the existing method 1 is an energy-based optimal combined cooperative sensing algorithm designed by using a likelihood ratio detection criterion; existing method 2 is a perceptual method designed based on the energy mean and variance of all cognitive users.
The first embodiment is as follows:
it is assumed. Fig. 4 shows the variation of the detection probability with SNR according to the prior art and the present invention. As shown in fig. 4, the present invention has a higher detection probability than prior art 2, and although lagged behind prior art 1, the present invention does not rely on the instantaneous signal-to-noise ratio at each cognitive user.
Example two:
for the hidden terminal problem in the cognitive user, fig. 5 shows the detection probability of the prior art and the present invention changes with the number of hidden terminals, where the number of hidden terminals gradually increases from zero to 15. The result shows that the detection probability of the prior art and the detection probability of the invention are both reduced along with the increase of the number of the hidden terminals, but the invention has higher detection probability.
Simulation results show that: in the absence of a hidden terminal scene, the perceptual performance of the method is poorer than that of the existing method 1, but the existing method 1 depends on the instantaneous average signal-to-noise ratio of each cognitive user, which is difficult to obtain in practical application. Compared with the prior information, the method does not depend on the prior information, and has better perception performance in a multi-hidden terminal scene than the prior method 1.
According to simulation results, aiming at a perception scene with simultaneous large and small scale fading, the cooperative perception method based on the geometric symmetry of the energy vector provided by the invention not only can effectively perceive the signal of the main user, but also has more stable performance in a hidden terminal scene, and the correctness and the effectiveness of the method are verified.
Claims (1)
1. A passive cooperative spectrum sensing method based on energy feature geometric symmetry comprises the following steps:
step 1: establishing a cooperative sensing model suitable for simultaneous existence of size scale fading;
the application scene of the perception model is: the system comprises a master user, N cognitive users and a fusion center; the signal received by the nth cognitive user at time k is denoted as r n (k)；
Wherein H 1 Indicating the presence of a primary user signal, H 0 Indicating that a main user signal does not exist; s n (k) Represents the primary user signal, w, received by the nth cognitive user at time k n (k) Is additive complex gaussian noise; g n Representing a large-scale fading, h, between the primary user and the nth cognitive user n (k) Representing a small-scale fade between the primary user and the nth cognitive user.
For large-scale fading g between a master user and an nth cognitive user n And is distributed according to a log-normal distribution,
wherein the content of the first and second substances,
represents the large-scale fading channel distribution, mu and sigma, between the primary user and the nth cognitive user 2 Respectively representing the mean and variance of lnx;
small-scale fading channel h between master user and nth cognitive user n (k) Expressed as:
h n (k)＝Aexp(j2πθ)
where θ represents the small-scale fading channel h n (k) Obey a uniform distribution of [0,1 ], the amplitude A obeys a Nakagami-m distribution f A (x) Is shown as
Wherein m and omega respectively represent Nakagami-m channel parameters and average power, and Gamma (·) is a Gamma function;
step 2: calculating the average value of the energy of the received signal of each cognitive user;
the average energy E of the received signals at the nth cognitive user in the K sampling points n As indicated by the general representation of the,
wherein, | · | represents an absolute value operator;
calculating the expectation of the average energy of the received signals of each cognitive user,
where E (-) represents the fetch desired operation;
and 3, step 3: constructing a detection statistic;
when the received signal does not contain a main user signal, the energy of the received signal presents a symmetrical characteristic in a geometric space, and the characteristic is used for designing a detection statistic expressed as:
and 4, step 4: detecting and judging;
the detection statistics designed above is utilized to carry out detection judgment,
wherein gamma is 2 For judging threshold, the detection statistic T and the judgment threshold gamma are used 2 And comparing, judging that the master user signal exists when the detection statistic is larger than the threshold, and otherwise, judging that the master user signal does not exist.
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