Over the last two decades, huge efforts have been devoted to developing efficient wireless communication technologies that can deal with the ever growing user demands. One of the most critical challenges related to the growth of wireless networks is the usage and management of the electromagnetic radio spectrum as this is the most precious and limited resource. For this reason, cognitive radio (CR) systems which can support dynamic access to the available spectrum has been suggested as the key enabling technology to increase spectrum utilization to face the huge expansion of wireless systems. Cognitive network (CN) consists of elements, which through learning and reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end performance. The Network can learn from these adaptations and use them to make future decisions. This thesis aims to clearly investigate and study the MAC layer sensing schemes in Cognitive Radio Networks. Two sensing approaches, reactive and proactive, are examined and compared via two performance metrics, achieved spectrum utilization factor and idle channel search delay. Matlab simulations were carried out and the results show that the proactive sensing in uncongested radio environments is better to be used as it is idle channel search delay is much less than reactive sensing. With the increase of congestion in the radio environment, using of reactive sensing becomes more desirable to decrease idle channel search delay.