Cooperative Sensing And Maximizing Throughput In Cognitive Radio Networks

In modern era, the scarcity and low utilization of spectrum resources is the main status quo in the field of communications. Cognitive radio, as an original technology, improves the use of spectrum resources greatly. Spectrum sensing is an important part of the cognitive radio with an opportunistic spectrum access. The better the performance of spectrum sensing for a cognitive radio system is, the higher the throughput of corresponding system can achieve.First, the backgrounds, concepts, research status of cognitive radio and its applications are briefly introduced. Based on a detailed introduction of energy sensing among the spectrum sensing in the second chapter, the paper maximizes the system throughput by using cooperative sensing and searching the sensing time under the simple cases that there is only one primary user and a single channel in cognitive radio. After analyzing the effect of parameters of cognitive radio on the maximum achievable throughput, a conclusion is drawn that too many secondary users participating in cooperative sensing can make the throughput decreased when the reported delay can not be neglected and multiple secondary users report sensing results only through a public channel.Secondly, to maximize the throughput of cognitive radio, we use cooperative sensing, and put forward different system model, and formulate the throughput maximization problems depending on different actual environments under the condition of multi channel in chapter three and chapter four, respectively. For multiple secondary users to parallel sensing multiple primary users, in order to seek the optimal allocation between the secondary users and primary user channels, solution algorithms with different performance are proposed according to the different thoughts. We determine the optimal allocation and the optimal sensing time between the multiple secondary users and primary user channels, so as to maximize the throughput of cognitive radio. Moreover, we made a comparison and analysis about the performance that different algorithms on maximizing the system throughput. Suboptimal algorithm is close to the exhaustive algorithm on maximum throughput. However, compared with exhaustive algorithm, the suboptimal algorithm has a very low complexity.Particularly, in chapter four, we use the thought of random position between primary users and secondary users to formulate the throughput maximization problem in practical scenario,an average interference power constraint is applied, which making the system model more complicated. Similarly, we study and analyze the factors that restrict the maximum throughput of the system in multi channel models, such as fusion strategy, group assignment, reported delay, sensing time, interference power, etc.