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- elude band by band sweeping

- reduced the sensing time

- mitigate interference

- Computational complexity

- requires prior knowledge

- memory issue


- reducing two-dimension cyclic feature

- can be applied on sparse and non-sparse signals.

- robustness against noise uncertainty and interference

- Computational complexity

- block size affects the sensing time performance

[38] [39] [50]

- fast algorithm

- easy to implement

- requires more measurements to have perfect reconstruction

- lacks provable quality of reconstruction


- sparsity constraints due to the use of overcomplete method

- the unique solution is conditioned by complex Gaussian matrix M 2 ( 2 N 1 ) 1 and the selection of M is subject to the auto- and cross correlation between the row of sensing matrix used


- prior sparse level is not required

- sequential measurements are used to fasten detection process

- quality of reconstruction performance is subject to complexity


- using mixed l1/l2 norm denoising operator with LASSO algorithm increase detection performance.

- require information of the spectrum boundaries between different PU as a priori information


- localized transmitting CRs

- sensitive to changes in the system

- single hop with low overhead message between neighbors

- battery issue


- Robust to error

- comparable detection performance compare to MBPDN algorithm

- Priori knowledge of subband locations


- complexity reduction

- improved detection accuracy

- Priori knowledge is required