CN104954089B - A Spectrum Sensing Method Based on Multi-antenna Instantaneous Power Comparison - Google Patents

Abstract

The invention discloses a kind of frequency spectrum sensing method being compared based on multiple antennas instantaneous power, it utilizes many reception antennas to receive the continuous radiofrequency signal of time domain；Then to each time domain, continuous radiofrequency signal carries out down coversion, time-domain sampling obtains the baseband signal of corresponding time domain discrete；Then calculate the instantaneous power of the time domain sampling point in each time-domain sampling moment in the baseband signal of each time domain discrete, then by all instantaneous powers order from small to large to all instantaneous powers again assignment；Then calculate statistic of test；Finally by the size of comparison test statistic and decision threshold, determine whether there is other radio communication services and take frequency range, realize frequency spectrum perception；Advantage is that frequency spectrum perception is functional, and overcomes energy measuring method and require accurately to predict the defect of noise power, also overcomes the shortcoming that relativity of time domain between multiple antenna receiving signal for the eigenvalue detection method is relatively low or uncorrelated time-frequency spectrum perception was lost efficacy.

Description

A Spectrum Sensing Method Based on Multi-antenna Instantaneous Power Comparison

technical field

The invention relates to a spectrum sensing technology in a cognitive radio system, in particular to a spectrum sensing method based on multi-antenna instantaneous power comparison.

Background technique

With the rapid growth of wireless communication services, people's demand for spectrum resources continues to increase, and the phenomenon of lack of spectrum resources becomes more and more serious. On the one hand, the rapid development of wireless communication services and the continuous emergence of various systems, protocols, and networks have made more people need to use radio spectrum; on the other hand, many frequency bands have been allocated to authorized users, and non-authorized users can only use Those unlicensed frequency bands are very scarce, and the low utilization rate of spectrum resources in the existing fixed spectrum resource allocation strategy is one of the main reasons for this phenomenon. The cognitive radio (Cognitive Radio, CR) technology can effectively improve the utilization rate of spectrum resources, and is one of the main solutions to realize the dynamic allocation of spectrum resources. Spectrum sensing is an important part of cognitive radio technology. It can effectively prevent wireless communication services using cognitive radio technology from interfering with other wireless communication services in the same frequency band. The performance of spectrum sensing is directly related to the performance of wireless communication services. quality.

The existing spectrum sensing methods mainly include energy detection method, cyclic feature detection method, covariance matrix detection method, eigenvalue detection method and so on. Among these methods, the energy detection method needs to know the noise power, and the cyclic feature detection method needs to know the cyclic frequency of the authorized user signal. When the noise power and the cyclic frequency of the licensed user signal are unknown, the spectrum sensing performance of energy detection method and cyclic feature detection method will be severely degraded. In the case of multiple antennas, the covariance matrix detection method and the eigenvalue detection method can use the correlation between received signals to realize spectrum sensing, but in practical applications, in order to obtain diversity gain, the correlation between received signals is relatively low. Low or even irrelevant, at this time the spectrum sensing performance of the two methods is poor.

Contents of the invention

The technical problem to be solved by the present invention is to provide a spectrum sensing method based on multi-antenna instantaneous power comparison, which has good spectrum sensing performance.

The technical solution adopted by the present invention to solve the above technical problems is: a spectrum sensing method based on multi-antenna instantaneous power comparison, which is characterized in that it includes the following steps:

①Assume that there are M receiving antennas in the cognitive radio system that are used to receive continuous radio frequency signals in the time domain, where M represents the total number of receiving antennas in the cognitive radio system, and M≥2;

② Perform down-conversion processing on the time-domain continuous radio frequency signals received by each receiving antenna to obtain the time-domain continuous baseband signals corresponding to the time-domain continuous radio frequency signals received by each receiving antenna; then each receiving antenna receives The time-domain continuous baseband signal corresponding to the time-domain continuous radio frequency signal is subjected to time-domain sampling processing, and the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by each receiving antenna is obtained, and the i-th receiving antenna The sampling value of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by the antenna is denoted as x _i,n , where i=1,2, ...,M, n=1,2,...,N, N represents the number of sampling points for time-domain sampling processing, N≥15;

③Calculate the instantaneous power of the time-domain sampling point at each time-domain sampling moment in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by each receiving antenna, and calculate the time-domain sampling point received by the i-th receiving antenna The instantaneous power of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal corresponding to the continuous radio frequency signal is denoted as r _i,n , r _i,n =(|x _i,n |) ² , where the symbol "||" is the absolute value symbol;

Then sort the instantaneous power of the time-domain sampling points at all time-domain sampling moments in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by the M receiving antennas in ascending order, and then sort the values A total of M×N positive integers from 1 to M×N are assigned one by one to the M×N instantaneous powers sorted in ascending order, and the time-domain continuous radio frequency signal received by the i-th receiving antenna corresponds to The reassigned value of the instantaneous power of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal is denoted as r _i,n ';

④ Calculate the test statistic, denoted as T,

⑤ According to the false alarm probability P _f and the total number M of receiving antennas in the cognitive radio system, calculate the decision threshold, denoted as λ, the value of λ is the upper P _f quantile of the chi-square distribution with M-1 degrees of freedom value, where 0≤P _f ≤1;

⑥Comparing the test statistic T with the decision threshold λ, if T≥λ, it is determined that other wireless communication services are occupying the frequency band; if T<λ, it is determined that other wireless communication services are not occupying the frequency band.

Compared with the prior art, the present invention has the advantages of:

1) The method of the present invention only utilizes the false alarm probability and the total number of antennas in the cognitive radio system when calculating the decision threshold, and does not need to know the noise power, so the method of the present invention effectively overcomes the need to know the noise in the existing energy detection method Power flaws.

2) The method of the present invention first performs down-conversion processing on the received signal to obtain a time-domain continuous baseband signal, then performs time-domain sampling to the time-domain continuous baseband signal to obtain a time-domain discrete baseband signal, and then calculates the time-domain discrete baseband signal The instantaneous power of each time-domain sampling point, and re-assign all the instantaneous power after sorting from small to large, and finally calculate the test statistic according to the instantaneous power value after the re-assignment, which makes the correlation between the received signals of the method of the present invention When the correlation is low or uncorrelated, it still has good spectrum sensing performance, so the method of the present invention effectively overcomes the problem of poor spectrum sensing when the correlation between received signals is low or uncorrelated in the existing eigenvalue detection method. shortcoming.

Description of drawings

Fig. 1 is the overall realization block diagram of the inventive method;

Fig. 2 is a schematic diagram of spectrum sensing performance comparison between the method of the present invention and the energy detection method and the eigenvalue detection method.

detailed description

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

A spectrum sensing method based on multi-antenna instantaneous power comparison proposed by the present invention, its overall implementation block diagram is shown in Figure 1, which includes the following steps:

①Assume that there are M receiving antennas in the cognitive radio system, all of which are used to receive continuous radio frequency signals in the time domain, where M represents the total number of receiving antennas in the cognitive radio system, and M≥2.

In Figure 1, x ₁ (t) represents the time domain continuous radio frequency signal received by the first receiving antenna, x ₂ (t) represents the time domain continuous radio frequency signal received by the second receiving antenna, x _M (t) Indicates the time-domain continuous radio frequency signal received by the Mth receiving antenna.

② Utilize the existing down-conversion processing technology to perform down-conversion processing on the time-domain continuous radio frequency signals received by each receiving antenna, and obtain the time-domain continuous baseband signals corresponding to the time-domain continuous radio frequency signals received by each receiving antenna ; Then utilize existing time-domain sampling processing technology to carry out time-domain sampling processing to the time-domain continuous baseband signal corresponding to the time-domain continuous radio frequency signal received by each receiving antenna, and obtain the time-domain continuous baseband signal received by each receiving antenna The time-domain discrete baseband signal corresponding to the radio frequency signal, the time-domain sampling point of the nth time-domain sampling moment in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by the i-th receiving antenna The sampling values are denoted as x _i,n , where i=1, 2,..., M, n=1, 2,..., N, N represents the number of sampling points for time-domain sampling processing, and N≥15.

Here, during time-domain sampling processing, the sampling periods may be at equal or unequal intervals.

③Calculate the instantaneous power of the time-domain sampling point at each time-domain sampling moment in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by each receiving antenna, and calculate the time-domain sampling point received by the i-th receiving antenna The instantaneous power of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal corresponding to the continuous radio frequency signal is denoted as r _i,n , r _i,n =(|x _i,n |) ² , where the symbol "||" is the absolute value symbol;

Then sort the instantaneous power of the time-domain sampling points at all time-domain sampling moments in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by the M receiving antennas in ascending order, and then sort the values A total of M×N positive integers from 1 to M×N are assigned one by one to the M×N instantaneous powers sorted in ascending order, and the time-domain continuous radio frequency signal received by the i-th receiving antenna corresponds to The reassigned value of the instantaneous power of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal is denoted as r _i,n '.

④ Calculate the test statistic, denoted as T,

⑤ According to the false alarm probability P _f and the total number M of receiving antennas in the cognitive radio system, calculate the decision threshold, denoted as λ, the value of λ is the upper P _f quantile of the chi-square distribution with M-1 degrees of freedom value, where 0≤P _f ≤1.

⑥Comparing the test statistic T with the decision threshold λ, if T≥λ, it is determined that other wireless communication services are occupying the frequency band; if T<λ, it is determined that other wireless communication services are not occupying the frequency band.

The feasibility and effectiveness of the spectrum sensing method of the present invention will be further illustrated below through computer simulation.

Assume that there are M=4 receiving antennas in the cognitive radio system, the number of sampling points for time-domain sampling processing is N=100, and assuming that the channels on the receiving antennas are independent of each other and obey the Nakagami distribution, and the signal-to-noise ratio is set to -10dB. Fig. 2 has provided the spectrum sensing performance comparison of the detection probability of the method of the present invention and the energy detection method and the eigenvalue detection method as the false alarm probability changes, wherein the energy detection method does not know the noise power, but knows two upper limits of the noise power, These two upper bounds correspond to noise uncertainties of 0.1dB and 0.2dB, respectively. As can be seen from Figure 2, when the false alarm probability is 0.1 and the noise uncertainty increases from 0.1dB to 0.2dB, the detection probability of the energy detection method drops to 0.40 from 0.58, and the method of the present invention does not know the noise power and In the case of the upper limit of the noise power, the detection probability of 0.73 can still be reached; in addition, when the false alarm probability is 0.1, the detection probability of the method of the present invention can reach 0.73, while the detection probability of the eigenvalue detection method can only reach 0.28.

Claims (1)

1. A spectrum sensing method based on multi-antenna instantaneous power comparison, characterized in that it comprises the following steps: ① There are M receiving antennas in the cognitive radio system that are used to receive continuous radio frequency signals in the time domain, where M represents the total number of receiving antennas in the cognitive radio system, and M≥2; ② Perform down-conversion processing on the time-domain continuous radio frequency signals received by each receiving antenna to obtain the time-domain continuous baseband signals corresponding to the time-domain continuous radio frequency signals received by each receiving antenna; then each receiving antenna receives The time-domain continuous baseband signal corresponding to the time-domain continuous radio frequency signal is subjected to time-domain sampling processing, and the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by each receiving antenna is obtained, and the i-th receiving antenna The sampling value of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by the antenna is denoted as x _i,n , where i=1,2, ...,M, n=1,2,...,N, N represents the number of sampling points for time-domain sampling processing, N≥15; ③Calculate the instantaneous power of the time-domain sampling point at each time-domain sampling moment in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by each receiving antenna, and calculate the time-domain sampling point received by the i-th receiving antenna The instantaneous power of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal corresponding to the continuous radio frequency signal is denoted as r _i,n , r _i,n =(|x _i,n |) ² , where the symbol "||" is the absolute value symbol; Then sort the instantaneous power of the time-domain sampling points at all time-domain sampling moments in the time-domain discrete baseband signal corresponding to the time-domain continuous radio frequency signal received by the M receiving antennas in ascending order, and then sort the values A total of M×N positive integers from 1 to M×N are assigned one by one to the M×N instantaneous powers sorted in ascending order, and the time-domain continuous radio frequency signal received by the i-th receiving antenna corresponds to The reassigned value of the instantaneous power of the time-domain sampling point at the nth time-domain sampling moment in the time-domain discrete baseband signal is denoted as r _i,n '; ④ Calculate the test statistic, denoted as T, ⑤ According to the false alarm probability P _f and the total number M of receiving antennas in the cognitive radio system, calculate the decision threshold, denoted as λ, the value of λ is the upper P _f quantile of the chi-square distribution with M-1 degrees of freedom value, where 0≤P _f ≤1; ⑥Comparing the test statistic T with the decision threshold λ, if T≥λ, it is determined that other wireless communication services are occupying the frequency band; if T<λ, it is determined that other wireless communication services are not occupying the frequency band.