| Article Ref. | Advantages | Disadvantages |
| [44] | - elude band by band sweeping - reduced the sensing time - mitigate interference | - Computational complexity - requires prior knowledge - memory issue |
| [46] | - 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 |
| [50] | - sparsity constraints due to the use of overcomplete method | - the unique solution is conditioned by complex Gaussian matrix 1 and the selection of M is subject to the auto- and cross correlation between the row of sensing matrix used |
| [51] | - prior sparse level is not required - sequential measurements are used to fasten detection process | - quality of reconstruction performance is subject to complexity |
| [52] | - 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 |
| [53] | - localized transmitting CRs - sensitive to changes in the system - single hop with low overhead message between neighbors | - battery issue |
| [56] | - Robust to error - comparable detection performance compare to MBPDN algorithm | - Priori knowledge of subband locations |
| [57] | - complexity reduction - improved detection accuracy | - Priori knowledge is required |