CN115549827B - Blind spectrum sensing method, system, computer equipment and terminal - Google Patents
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Abstract
The invention belongs to the technical field of wireless communication, and discloses a blind spectrum sensing method, a blind spectrum sensing system, computer equipment and a terminal, wherein a cognitive user is toThe signals received by the receiving antennas are expressed as an observation matrixThe method comprises the steps of carrying out a first treatment on the surface of the Based on an observation matrixConstruction of detection statisticsThe method comprises the steps of carrying out a first treatment on the surface of the Calculating false alarm probabilityAnd detecting a threshold valueThe method comprises the steps of carrying out a first treatment on the surface of the Computing detection statistics of received signalsAnd is connected with a detection threshold valueAnd comparing to judge whether the main user signal exists. The invention utilizes the difference of the cross-correlation absolute values of the received data of different receiving antennas of the MIMO system, and realizes the blind spectrum sensing of the MIMO system by estimating the correlation accumulation between the received signals under the condition of no prior information of the main user, thereby improving the detection performance of the spectrum sensing and reducing the spectrum sensing time. Compared with the prior algorithm, the blind spectrum sensing method has better detection performance, and correctly detects when the signal-to-noise ratio is more than or equal to-13 dB and the false alarm probability is equal to 0.1The probability of detection reaches more than 90%.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a blind spectrum sensing method, a blind spectrum sensing system, computer equipment and a terminal.
Background
Currently, cognitive radio technology is considered as an effective way to alleviate the shortage of spectrum resources. The main idea of the cognitive radio is that under the condition that the main user does not occupy the authorized spectrum resource, the cognitive user occupies the spectrum resource in an opportunistic access mode, so that the utilization rate of the spectrum resource is effectively improved. The spectrum sensing technology can effectively detect idle spectrum resources, provides conditions for opportunistic access of the cognitive user on one hand, and can avoid harmful interference of the cognitive user to the main user on the other hand. Therefore, research on application of spectrum sensing technology in different scenes is urgently needed to solve the problem of demand of mass access devices for spectrum resources.
Regarding spectrum sensing technology, spectrum sensing methods can be classified into a training sequence-based spectrum sensing method and a blind spectrum sensing method according to information whether a cognitive user knows a primary user transmission signal. The frequency spectrum sensing method based on the training sequence mainly comprises a matched filtering type method and a maximum correlation entropy type method. The frequency spectrum sensing method based on the training sequence requires the prior information of the known main user of the cognitive user, and is greatly limited in the practical application scene. The blind spectrum sensing method comprises an energy detection method and a cyclostationary characteristic detection method. The energy detection method is characterized in that the detection performance of the cognitive user is limited by a signal-to-noise ratio wall due to the uncertainty of a channel between a main user and the cognitive user, and the defect is particularly obvious in a multi-antenna system. According to the cyclostationary feature detection method, the cyclostationary feature of the main user signal is destroyed due to the influence of channel multipath fading, so that the performance of the cyclostationary feature detection algorithm is seriously degraded. The blind spectrum sensing method applied to the MIMO system comprises a maximum-minimum characteristic value detection method and a covariance absolute value method. Similar to both methods are the mean and square root methods of eigenvalue extrema. The method is a detection method based on the characteristic value or other characteristics of the covariance matrix of the received signal, the method does not need to know the transmission information of the main user, and blind spectrum sensing of the MIMO system is realized by utilizing the difference of the covariance matrix of the received signal of each antenna of the cognitive user and the characteristic value thereof under two scenes of occupation of the spectrum by the main user and idle spectrum. However, these methods are based on accurate covariance matrix, so that the required sample size is large and the sensing period is long. Therefore, there is a need to design a new blind spectrum sensing method and system.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) The prior frequency spectrum sensing method based on the training sequence requires the prior information of the known main user of the cognitive user, and is greatly limited in the practical application scene.
(2) The existing energy detection method is characterized in that the detection performance of the cognitive user is limited by a signal-to-noise ratio wall due to the uncertainty of a channel between a main user and the cognitive user, and the defect is particularly obvious in a multi-antenna system.
(3) Due to the influence of channel multipath fading, the original cyclostationary characteristic of a main user signal of the conventional cyclostationary characteristic detection method is destroyed, so that the performance of the cyclostationary characteristic detection algorithm is seriously degraded.
(4) The existing blind spectrum sensing method suitable for the MIMO system is based on an accurate covariance matrix, so that the required sample size is large, and the sensing period is long.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a blind spectrum sensing method, a blind spectrum sensing system, computer equipment and a blind spectrum sensing terminal, and particularly relates to a blind spectrum sensing method, a blind spectrum sensing system, a blind spectrum sensing computer equipment and a blind spectrum sensing terminal of a MIMO system based on statistical cross-correlation.
The invention is realized in such a way that a blind spectrum sensing method comprises:
constructing an observation matrix, and calculating false alarm probability, a detection threshold value and detection statistics; comparing the detection statistic with a detection threshold value, and judging whether the main user signal exists or not; and the difference of the cross-correlation absolute values of the data received by different receiving antennas of the MIMO system is utilized, and the blind spectrum sensing of the MIMO system is realized by estimating the correlation accumulation between the received signals under the condition of no prior information of the main user.
Further, the blind spectrum sensing method includes the steps of:
step one, the cognitive user willThe signals received by the individual receiving antennas are denoted as observation matrix +.>The method comprises the steps of carrying out a first treatment on the surface of the The cognitive user realizes the capability of acquiring the information of the surrounding electromagnetic environment where the cognitive user is located.
Step two, based on the observation matrixConstruction of detection statistics->The method comprises the steps of carrying out a first treatment on the surface of the The cognitive user establishes analysis capability of electromagnetic environment information and self-demand related knowledge.
Step three, calculating the false alarm probabilityAnd detection threshold->The method comprises the steps of carrying out a first treatment on the surface of the The cognitive user establishes detection standards based on information analysis and own requirements.
Step four, calculating the detection statistic of the received signalAnd is +.>And comparing to judge whether the main user signal exists. And the cognitive user judges whether the main user occupies the channel according to the detection standard.
Further, the cognitive user in the step one willThe signals received by the receiving antennas are expressed as an observation matrixComprising the following steps:
based on a spectrum sensing system of a main user and a cognitive user, the number of antennas of the main user and the cognitive user is respectively and />And->；/> and />Representing the absence and presence of the primary user, respectively, +.>Time->Signals received by the individual receiving antennas->Expressed as:
；
wherein ,representing +.>The number of transmitting antennas is->A signal vector transmitted at a moment;representation->Time->Noise vectors received by the receiving antennas; />Indicate->The number of receiving antennas is->A reception noise signal at the moment, and->Is zero in mean value; />Representing the transmission matrix between the transmit antennas and the receive antennas:
；
wherein ,representing the->The first receive antenna and the main user>Responses between the transmit antennas.
When receivingObservation vectors, observation matrix for received signal->Expressed as:
；
wherein ,representation->A matrix of signals transmitted by the transmit antennas at different times,representation->The noise matrices received by the receiving antennas at different times.
Further, the observation matrix in the second stepConstruction of detection statistics->Comprising the following steps:
；
wherein ,indicate->The number of receiving antennas is->Receive signal at various moments>；Indicate->The number of receiving antennas is->Conjugation of the received signal at each instant.
Further, calculating the false alarm probability in the third stepAnd detection threshold->Comprising the following steps:
false alarm probabilityExpressed as:
；
wherein ,；/>is the variance of gaussian white noise.
At a given false alarm probabilityWhen the detection threshold is obtained>The method comprises the following steps:
；
wherein ,is->Is the inverse of (a).
Further, the step four is to calculate the detection statistics of the received signalAnd is +.>Comparing, determining whether the primary user signal is present includes:
calculating detection statistics of the received signal:
；
will detect statisticsAnd detection threshold->Comparing when->When the primary user signal exists, the primary user signal is judged to occupy the channel; otherwise, the primary user signal is judged to be absent, and the channel is not occupied.
Another object of the present invention is to provide a blind spectrum sensing system to which the blind spectrum sensing method is applied, the blind spectrum sensing system comprising:
the cognitive user data acquisition module is used for the cognitive user to acquireThe signals received by the individual receiving antennas are denoted as observation matrix +.>；
A detection statistic constructing module for constructing a detection statistic based on the observation matrixConstruction of detection statistics->；
The detection threshold value calculation module is used for calculating the false alarm probabilityAnd detection threshold->；
A main user signal judging module for calculating the detection statistic of the received signalAnd is +.>And comparing to judge whether the main user signal exists.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the blind spectrum sensing method.
It is a further object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the blind spectrum sensing method.
Another object of the present invention is to provide an information data processing terminal, which is used for implementing the blind spectrum sensing system.
In combination with the above technical solution and the technical problems to be solved, please analyze the following aspects to provide the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty of solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows:
the invention constructs the detection statistic of spectrum sensing by utilizing the difference of the cross-correlation absolute values of the received data of different receiving antennas of the MIMO system. In the detection statistics, the cognitive user does not need to know prior information of the main user, blind spectrum sensing of the MIMO system is achieved, and the detection performance of spectrum sensing is remarkably improved by estimating correlation accumulation between received signals. The method avoids using the statistical information of the received signals as the basis of signal detection, can realize accurate detection of the signals under fewer samples, improves the reliability of the system and reduces the frequency spectrum sensing time.
Based on the difference of cross-correlation absolute values of different receiving antenna data of the MIMO system, the invention constructs the detection statistic based on the statistical cross-correlation absolute values, can realize blind spectrum sensing under the condition of unknown main user information, and has better performance under the condition of lower signal-to-noise ratio. Compared with the existing algorithm, the blind spectrum sensing method of the MIMO system based on the statistical cross correlation has better detection performance, and when the signal to noise ratio is more than or equal to-13 dB and the false alarm probability is equal to 0.1, the correct detection probability reaches more than 90%; in practical application, the frequency spectrum sensing performance can be further improved by increasing the number of samples or the number of receiving antennas.
Secondly, the technical scheme is regarded as a whole or from the perspective of products, and the technical scheme to be protected has the following technical effects and advantages:
according to the blind spectrum sensing method of the MIMO system based on statistical cross-correlation, provided by the invention, the blind spectrum sensing of the MIMO system is realized by estimating the correlation accumulation between received signals under the condition of no prior information of a main user by utilizing the difference of cross-correlation absolute values of received data of different receiving antennas of the MIMO system, the detection performance of spectrum sensing is obviously improved, and the spectrum sensing time is reduced.
Thirdly, as inventive supplementary evidence of the claims of the present invention, the following important aspects are also presented:
the technical scheme of the invention solves the technical problems that people are always desirous of solving but are not successful all the time: the invention solves two technical problems: firstly, in a Gaussian white noise environment, a frequency spectrum sensing method based on a training sequence requires a cognitive user to know prior information of a main user, but in practical application, the technical problem of unknown information of the main user is solved; secondly, the method of the invention solves the technical problem that a large number of samples are needed as statistical information in the sensing process to cause long sensing period by estimating correlation accumulation between received signals under the condition of no prior information of a main user by utilizing the difference of cross-correlation absolute values of the received data of different receiving antennas of the MIMO system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a blind spectrum sensing method provided by an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating comparison of detection performance of different spectrum sensing methods under different SNR environments provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram showing comparison of detection performance of different spectrum sensing methods under different sample numbers according to an embodiment of the present invention;
fig. 4 is a comparison diagram of detection performance of different spectrum sensing methods under different numbers of receiving antennas according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Aiming at the problems existing in the prior art, the invention provides a blind spectrum sensing method, a blind spectrum sensing system, computer equipment and a terminal, and the invention is described in detail below with reference to the accompanying drawings.
1. The embodiments are explained. In order to fully understand how the invention may be embodied by those skilled in the art, this section is an illustrative embodiment in which the claims are presented for purposes of illustration.
As shown in fig. 1, the blind spectrum sensing method provided by the embodiment of the invention includes the following steps:
s101, the cognitive user willThe signals received by the individual receiving antennas are denoted as observation matrix +.>；
S102, based on the observation matrix XK) Construction of detection statistics；
S103, calculating false alarm probabilityAnd detection threshold->；
S104, calculating the detection statistic of the received signalAnd is +.>And comparing to judge whether the main user signal exists.
As a preferred embodiment, the blind spectrum sensing method provided by the embodiment of the present invention specifically includes the following steps:
step 1, the cognitive user willThe signals received by the individual receiving antennas are denoted as observation matrix +.>。
Consider a spectrum sensing system of a primary user and a cognitive user, the number of antennas of the primary user and the cognitive user being respectively and />, and />。/> and />Representing the absence and presence of the primary user, respectively, +.>Time->Signals received by the individual receiving antennas->Expressed as:
；
wherein ,representing +.>The number of transmitting antennas is->A signal vector transmitted at a moment;representation->Time->Noise vectors received by the receiving antennas; />Indicate->The number of receiving antennas is->Time of day reception noise signal,/->Is zero in mean value; />Representing the transmission matrix between the transmit antennas and the receive antennas:
；
wherein ,representing the->The first receive antenna and the main user>Responses between the transmit antennas.
When receivingObservation vectors, observation matrix for received signal->Expressed as:
；
wherein ,representation->A matrix of signals transmitted by the transmit antennas at different times,representation->The noise matrices received by the receiving antennas at different times.
Step 2, based on the observation matrixConstruction of detection statistics->。
；
wherein ,indicate->The number of receiving antennas is->The received signal at each moment in time,；/>indicate->The number of receiving antennas is->Conjugation of the received signal at each instant.
Step 3, calculating the false alarm probabilityAnd detection threshold->。
False alarm probabilityExpressed as:
；
wherein ,；/>variance of Gaussian white noise. Given a false alarm probability->When a detection threshold is available>The method comprises the following steps:
；
wherein ,is->Is the inverse of (a).
And step 4, calculating detection statistics of the received signals, comparing the detection statistics with a detection threshold value, and judging whether the main user signals exist or not.
Calculating detection statistics of the received signal, i.e.。
Will detect statisticsAnd detection threshold->Comparing when->When the primary user signal exists, the primary user signal is judged to occupy the channel; otherwise, the primary user signal is judged to be absent, and the channel is not occupied.
The spectrum sensing system provided by the embodiment of the invention comprises the following components:
the cognitive user data acquisition module is used for the cognitive user to acquireThe signals received by the individual receiving antennas are denoted as observation matrix +.>；
A detection statistic constructing module for constructing a detection statistic based on the observation matrixConstruction of detection statistics->；
The detection threshold value calculation module is used for calculating the false alarm probabilityAnd detection threshold->；
A main user signal judging module for calculating the detection statistic of the received signalAnd is +.>And comparing to judge whether the main user signal exists.
2. Application example. In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
In order to prove the creativity and technical value of the technical scheme of the invention, the technical scheme of the invention is simulated and verified in an application scene. In the simulation, the parameters were set as follows: the primary user transmitting signal is BPSK; channel parameters are randomly generated and obey [0,1 ]]Uniform division of the upper partCloth; signal to noise ratio（/>: signal-to-Noise Ratio) is defined as；/>The method comprises the steps of carrying out a first treatment on the surface of the The simulation times of Monte Carlo experiments are 10000 times. In the present invention, the operation characteristic (ROC: receiver Operating Characteristic) curve of the receiver and the detection probability are used as the index of the detection performance evaluation. As can be seen from fig. 2, 3 and 4, the method of the present invention has optimal detection performance under the conditions of the same signal-to-noise ratio and the same number of samples.
3. Evidence of the effect of the examples. The embodiment of the invention has a great advantage in the research and development or use process, and has the following description in combination with data, charts and the like of the test process.
The technical effects of the present invention will be described in detail with reference to simulation experiments. In order to evaluate the performance of the present invention, a Monte Carlo simulation verification was performed. In the simulation, the parameters were set as follows: the primary user transmitting signal is BPSK; channel parameters are randomly generated and obey [0,1 ]]Uniformly distributed on the upper part; signal to noise ratio（/>: signal-to-Noise Ratio) is defined as；/>The method comprises the steps of carrying out a first treatment on the surface of the The simulation times of Monte Carlo experiments are 10000 times. In the present invention, the operation characteristic (ROC: receiver Operating Characteristic) curve of the receiver and the detection probability are used as the index of the detection performance evaluation.
To verify the effectiveness of the method of the invention, the method of the invention was analyzed in comparison with the other two methods. In the simulation process of figure 2 of the drawings,. As can be seen from fig. 2, the method of the present invention has the highest detection performance under the same signal-to-noise ratio. In the simulation of fig. 3, +.>，/>，/>. As can be seen from fig. 3, the detection probability of the method of the present invention is significantly higher than that of the other two methods with the same number of samples. In the simulation process of fig. 4, +.>，/>. As can be seen from fig. 4, the detection probability of the existing sensing method increases with the increase of the number of receiving antennas; the method of the present invention has the best detection performance compared to the other two methods.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (6)
1. A method of blind spectrum sensing, the method comprising the steps of:
step one, a cognitive user represents signals received by N receiving antennas as an observation matrix X (K);
step two, constructing a detection statistic T based on an observation matrix X (K);
step three, calculating false alarm probability P f And detecting a threshold value eta;
step four, calculating the detection statistic T of the received signal, comparing with a detection threshold value eta, and judging whether the main user signal exists or not;
the step one of the cognitive user representing the signals received by the N receiving antennas as an observation matrix X (K) includes:
based on a spectrum sensing system of a main user and a cognitive user, the number of antennas of the main user and the cognitive user is M and N respectively, and M is less than or equal to N; h 0 and H1 Indicating the absence and presence of the primary user, respectively, the signal x (k) received by the N receiving antennas at the kth time is expressed as:
wherein s (k) represents signal vectors transmitted by M transmitting antennas of the main user at the moment k; w (k) = [ w ] 1 (k)，w 2 (k)，…，w N (k)] T Representing noise vectors received by N receiving antennas at k time points; w (w) i (k) Representing the received noise signal of the ith receive antenna at time k, i=1, …, N, and w i (k) Is zero in mean value; h denotes a transmission matrix between the transmit antennas and the receive antennas:
wherein ,hij Representing the response between the i-th receive antenna of the cognitive user and the j-th transmit antenna of the primary user, i=1, 2, …, N; j=1, 2, …, M;
when K observation vectors are received, the received signal is represented by an observation matrix X (K) as:
wherein S (K) = [ S (1), S (2), … S (K) ] represents a signal matrix transmitted by the transmitting antenna at K different times, and W (K) = [ W (1), W (2), …, W (K) ] represents a noise matrix received by the receiving antenna at K different times;
in the fourth step, the detection statistic T of the received signal is calculated, and compared with the detection threshold η, and the determining whether the primary user signal exists includes:
calculating detection statistics of the received signal:comparing the detection statistic T with a detection threshold value eta, and judging that the main user signal exists and occupies a channel when T is more than eta; otherwise, judging that the main user signal does not exist and the channel is not occupied; wherein x is i (k) Representing the received signal at the kth time for the ith receive antenna, i=1, …, N-1, k=1, …, K; />Indicating the conjugation of the received signal of the first receive antenna at the kth instant, li+1, …, N.
2. The method of blind spectrum sensing according to claim 1, wherein constructing the detection statistic T based on the observation matrix X (K) in the second step includes:
wherein ,xi (k) Representing the received signal at the kth time for the ith receive antenna, i=1, …, N-1, k=1, …, K;the conjugate of the received signal at the kth time by the kth receive antenna is shown with l=i+1, …, N.
3. The blind spectrum sensing method of claim 1 wherein said calculating a false alarm probability P in step three f And detecting a threshold value eta comprises:
false alarm probability P f Expressed as:
wherein ,σ 2 is the variance of gaussian white noise;
at a given false alarm probability P f When the detection threshold eta is obtained as follows:
wherein ,Q-1 (. Cndot.) is the inverse of Q (. Cndot.).
4. A blind spectrum sensing system applying the blind spectrum sensing method of any of claims 1 to 3, characterized in that the blind spectrum sensing system comprises:
the cognitive user data acquisition module is used for enabling a cognitive user to represent signals received by N receiving antennas as an observation matrix X (K);
the detection statistic constructing module is used for constructing a detection statistic T based on the observation matrix X (K);
the detection threshold value calculation module is used for calculating the false alarm probability P f And detecting a threshold value eta;
the main user signal judging module is used for calculating the detection statistic T of the received signal, comparing the detection statistic T with the detection threshold value eta and judging whether the main user signal exists or not;
the cognitive user representing signals received by the N receiving antennas as an observation matrix X (K) comprises:
based on a spectrum sensing system of a main user and a cognitive user, the number of antennas of the main user and the cognitive user is M and N respectively, and M is less than or equal to N; h 0 and H1 Indicating the absence and presence of the primary user, respectively, the signal x (k) received by the N receiving antennas at the kth time is expressed as:
where s (k) represents the signal transmitted by M transmit antennas of the primary user at time kA number vector; w (k) = [ w ] 1 (k)，w 2 (k)，…，w N (k)] T Representing noise vectors received by N receiving antennas at j time; w (w) i (k) Representing the received noise signal of the ith receive antenna at time k, i=l, …, N, and w i (k) Is zero in mean value; h denotes a transmission matrix between the transmit antennas and the receive antennas:
wherein ,hij Representing the response between the i-th receive antenna of the cognitive user and the j-th transmit antenna of the primary user, i=1, 2, …, N; j=1, 2, …, M;
when K observation vectors are received, the received signal is represented by an observation matrix X (K) as:
wherein S (K) = [ S (1), S (2), …, S (K) ] represents the signal matrix transmitted by the transmitting antenna at K different times, and W (K) = [ W (1), W (2), …, W (K) ] represents the noise matrix received by the receiving antenna at K different times;
calculating a detection statistic T of the received signal, comparing with a detection threshold value eta, and judging whether the main user signal exists or not comprises:
calculating detection statistics of the received signal:comparing the detection statistic T with a detection threshold value eta, and judging that the main user signal exists and occupies a channel when T is more than eta; otherwise, judging that the main user signal does not exist and the channel is not occupied; wherein x is i (k) Representing the received signal at the kth time for the ith receive antenna, i=1, …, N-1, k=1, …, K; />The conjugate of the received signal at the kth time by the kth receive antenna is shown with l=i+1, …, N.
5. A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the blind spectrum sensing method of any of claims 1 to 3.
6. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the blind spectrum sensing method of any of claims 1 to 3.
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| Publication number | Priority date | Publication date | Assignee | Title |
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