Compressive spectrum sensing method for observing matrix optimization
A technology of compressed spectrum sensing and observation matrix, applied in the field of computer communication, can solve the problems of difficult to complete real-time broadband detection, difficult to achieve from the perspective of technical cost, etc.
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Embodiment 1
[0032] Such as figure 1 As shown, the cognitive radio system of the present invention includes a primary user and a secondary user, and within the entire wide frequency range, the primary user can arbitrarily select a certain frequency point for communication. In order to facilitate research, the present invention usually divides the entire frequency range into several sub-frequency bands, from figure 1 It can be seen that the black sub-band means occupied, and the blank space is a spectrum hole, and the way the main user occupies the sub-band appears randomly. Since the amplitudes of each sub-band are not equal, the occupied sub-bands are sparsely distributed in the whole wide frequency range, and the perceived signal is compressible in the frequency domain. exist figure 1 In , the dotted arrow represents the secondary user's perception of the primary user, and the solid line represents the communication between the secondary users.
[0033] Suppose the signal to be tested i...
Embodiment 2
[0047] Such as image 3 As shown, the present invention provides an observation matrix optimized compressed spectrum sensing method, which is used for spectrum sensing in cognitive radio systems, assuming that the total bandwidth to be detected by cognitive users is B Hz, which can be continuously and evenly divided into L non-overlapping sub-bands, and the frequency of the edge position of each sub-band is f 0 ... f L+1 , and have f i j (i* (m-n)], use F -1 Denotes the inverse Fourier transform, W -1 Represents the wavelet inverse transform matrix, and Z is the amplitude difference at the edge position of the spectrum.
[0048] Method flow:
[0049] Step 1: Generate dimension M 0 ×N random matrix Φ 0 , the matrix and the inverse Fourier transform matrix F -1 And the wavelet inverse transform matrix W -1 Multiplied together, the compressed sensing matrix Ψ can be obtained 0 = Φ 0 f -1 W -1 , the signal to be measured is at Ψ 0 The projection on r is y =Ψ 0 Z, w...
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