THE current wireless communication networks adopt fixed spectrum access strategy. The Federal Communications Commission have found that this fixed spectrum access strategy utilizes the available frequency bands inefficiently .A promising approach of addressing this problem is Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty to deploy a cognitive radio (CR) network. One of the key characteristics of a CR network is its ability to discern the nature of the surrounding radio environment. This is performed by the spectrum sensing (signal detection) part of a CR network. The most common spectrum sensing algorithms for CR networks are matched filter, energy and cyclostationary based algorithms. Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty If the characteristics of the primary user such as modulation scheme, pulse shaping filter and packet format are known perfectly, matched filter is the optimal signal detection algorithm as it maximizes the received Signal-to-Noise Ratio (SNR). This algorithm has two major drawbacks: The first drawback is it needs dedicated receiver to detect each signal characteristics of a primary user. The second drawback is it requires perfect synchronization between the transmitter and receiver which is impossible to achieve. Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty This is due to the fact that, in general, the primary and secondary networks are administered by different operators. Energy detector does not need any information about the primary user and it is simple to implement. However, energy detector is very sensitive to noise variance uncertainty, and there is an SNR wall below which this detector can not guarantee a certain detection performan. Cyclostationary based detection algorithm is robust against noise variance uncertainty and it can reject the effect of adjacent channel interference. However, the computational complexity of this detection algorithm is high, and large number of samples are required to exploit the cyclostationarity behavior of the received signal . On the other hand, this Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty algorithm is not robust against cyclic frequency offset which can occur due to clock and timing mismatch between the transmitter and receiver . In , Eigenvalue decomposition (EVD)-based spectrum sensing algorithm has been proposed. This algorithm is robust against noise variance uncertainty but its computational complexity is high. Furthermore, for single antenna receiver, this algorithm is sensitive to adjacent channel interference signal, and for multi-antenna receiver, this algorithm requires a channel covariance matrix different from a scaled identity.