Joint Detection and Transmission for Dynamic Spectrum Access in Cognitive Radio Networks
Date Issued
2011
Date
2011
Author(s)
Tsai, Jiun-Shian
Abstract
In this thesis, we investigate the problem of joint optimization between spectrum detection and transmission in cognitive radio networks. We first formulate the joint optimization problem over the detection threshold and transmission power in the conventional hard sensing model that needs to explicitly determine the state of the primary user. We then propose an algorithm to solve the joint optimization problem. Besides the hard sensing model, we also consider the soft sensing model in the joint optimization framework, where the secondary user does not need to explicitly determine the state of the primary user after sensing. Instead, in soft sensing the secondary user determines the transmission power based on the received spectrum sensing metric. We formulate the problem and propose an algorithm for solving the joint optimization problem in soft sensing. Evaluation results show that soft sensing can achieve better average throughput than hard sensing. It, however, suffers from a higher complexity compared to hard sensing. To strike a balance between the performance of hard sensing and complexity of soft sensing, we propose the concept of multi-level sensing in cognitive radio networks. In the unified sensing model, conventional hard sensing can be considered as a special case of two-level sensing and soft sensing can be considered as multi-level sensing with possibly infinite number of levels. We formulate an optimization problem and propose an algorithm for solving the problem. Evaluation results show that multi-level sensing can indeed improve the average throughput while reducing the system complexity. Finally, we leverage the framework of multi-level sensing for studying the impact of the estimation inaccuracy for different sensing models. We find that multi-level sensing can reduce performance difference between soft sensing and hard sensing when inaccurate estimation exists.
Subjects
Cognitive Radio Networks
Joint Optimization
Type
thesis
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