A review on spectrum sensing for cognitive radio: challenges and solutions <s> Data Fusion. <s> Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signals from licensed primary radios to avoid harmful interference. However, due to the effects of channel fading/shadowing, individual cognitive radios may not be able to reliably detect the existence of a primary radio. In this paper, we propose an optimal linear cooperation framework for spectrum sensing in order to accurately detect the weak primary signal. Within this framework, spectrum sensing is based on the linear combination of local statistics from individual cognitive radios. Our objective is to minimize the interference to the primary radio while meeting the requirement of opportunistic spectrum utilization. We formulate the sensing problem as a nonlinear optimization problem. By exploiting the inherent structures in the problem formulation, we develop efficient algorithms to solve for the optimal solutions. To further reduce the computational complexity and obtain solutions for more general cases, we finally propose a heuristic approach, where we instead optimize a modified deflection coefficient that characterizes the probability distribution function of the global test statistics at the fusion center. Simulation results illustrate significant cooperative gain achieved by the proposed strategies. The insights obtained in this paper are useful for the design of optimal spectrum sensing in cognitive radio networks. <s> BIB001 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Data Fusion. <s> Spectrum sensing is a fundamental problem in cognitive radio. As a result, it has been reborn as a very active research area in recent years despite its long history. Although various sensing methods have been proposed, their robustness at very low signal-to-noise ratio (SNR) and uncertain noise/interference environment is generally not satisfactory. In this paper, the concept of robust sensing is discussed first. Subsequently the cooperative covariance and eigenvalue based detections are proposed for robust spectrum sensing. It is proved mathematically that under some conditions the proposed methods are robust to uncertain and unpredictable noise and interference. The performances of the methods are also verified by simulations. <s> BIB002
If the raw data from all receivers are sent to a central processor, the previously discussed methods for multi-antenna sensing can be directly applied. However, communication of raw data may be very expensive for practical applications. Hence, in many cases, users only send processed/compressed data to the central processor. A simple cooperative sensing scheme based on the energy detection is the combined energy detection. For this scheme, each user computes its received source signal (including the noise) energy as T ED,i = (1/N) N−1 n=0 |x i (n)| 2 and sends it to the central processor, which sums the collected energy values using a linear combination (LC) to obtain the following test statistic: where g i is the combining coefficient, with g i ≥ 0 and If there is no information on the source signal power received by each user, the EGC can be used, that is, g i = 1/M for all i. If the source signal power received by each user is known, the optimal combining coefficients can be found BIB001  . For the low-SNR case, it can be shown  that the optimal combining coefficients are given by where σ 2 i is the received source signal (excluding the noise) power of user i. A fusion scheme based on the CAV is given in BIB002 , which has the capability to mitigate interference and noise uncertainty.