The robustness to uncertainty of noise power is one of main challenges to spectrum sensing technique. Since the occurrence of noise power uncertainty causes the detection performance of spectrum sensing techniques significantly degrade. In this paper, we propose two novel schemes of twostage spectrum sensing for cognitive radio under environment as noise power uncertainty. The two-stage spectrum sensing technique combines two conventional spectrum sensing techniques to perform spectrum sensing by exploiting their individual advantages. The proposed two-stage spectrum sensing scheme exploits the merits of ED, MME and CAV techniques to determine the existence of the primary user. The ED performs spectrum sensing within a short time and offers a reliable detection at high SNRs condition. MME and CAV are robust to noise power uncertainty. Due to the combination of these techniques, the proposed schemes offer much more reliable detection when the uncertainty of noise power occurs. Even though the proposed technique takes the longest time in sensing period among two-stage spectrum sensing techniques, it is worth using this period of time to protect the primary user from harmful interference caused by the secondary user.

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In this paper, we propose a new scheme of an adaptive energy detection, multi-slot double constraints adaptive energy detection (MDCAED), with an objective to improve the detection performance of our previous work, double constraints adaptive energy detection (DCAED). MDCAED exploits multiple mini-slot technique, which achieves the diversity reception concept, to increase the ability to distinguish noise from the PU since the effect of diversity reception increases the received SNR. MDCAED performs spectrum sensing by spitting a sensing slot into a multiple mini-slot. Each mini-slot is performed spectrum sensing using DCAED and the final decision is made by using Kof-N rule. The decision threshold is adapted on the SNR of each mini-slot using DCAED. Therefore, by exploiting the multiple mini-slot concept together with DCAED, is reduced while is improved. Although, the detection performance is improved, the average sensing time of MDCAED slightly increases compared to DCAED since the system threshold needs to adapt more than once. Nevertheless, the average sensing time of MDCAED still achieves the spectrum sensing requirement.

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Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.