Kernel-Based Dynamic Spectrum Access in Cognitive Radio Networks
Date Issued
2010
Date
2010
Author(s)
Lin, Po-Chiang
Abstract
Wireless spectrum is a limited and valuable resource for communications. In accordance with the fixed spectrum allocation strategies adopted by most regulators nowadays, wireless spectrum is known to be underutilized in spacial, temporal, and spectral domains. The dynamic spectrum access (DSA) of cognitive radio networks (CRNs) provides the capability to improve the spectrum efficiency by allowing secondary users to access the spectrum opportunistically without interfering primary users. The challenge is to maximize the utilities of the secondary users while protecting the primary users.
In dynamic spectrum access, there exist two main problems. The first one is to allocate the available channels to the secondary users appropriately. The second one is to assign the transmission power to the assigned channels of secondary users. Due to the fact that the channel allocation and the power control problems would affect the aggregated interference of the primary users and the performance of the cognitive radio networks, it is important to design an optimal channel allocation and power control method.
In order to achieve optimal channel allocation and power control, the knowledge of all channel gains are necessary. Conventional channel estimation methods require a transmitter and a receiver to tune to the same channel and estimate the channel gain by sending a pilot signal. These methods are thus time-consuming and inefficient for the dynamic spectrum access in cognitive radio networks. Moreover, wireless channels are known to be affected by the small-scale fading. A one-time sample of a channel gain is thus noisy, and the small-scale fading would lead to channel gain estimation errors. In this dissertation we propose a kernel-based channel gain estimation method. In this method we adopt the support vector regression (SVR) to build the knowledge between the location information of each transmitter-receiver pair and the corresponding channel gain. Such a machine-learning method is noise-resistant. It provides an effective and efficient method to estimate the channel gain. We perform a real-world experiment to measure the GSM signals, and use the measurement to evaluate the performance of the proposed kernel-based channel gain estimation method. Experiment results show that with sufficient training data, the proposed method could efficiently estimate channel gains and achieve the root mean square error as low as 2 dB.
Previous works about the channel allocation and power control problem usually model the problem as a mixed integer programming problem. However, such problem formulation is NP-hard in general. In this dissertation we analyze the relationship between the channel allocation and the power control, and thus re-formulate the problem as a nonlinear programming problem. With the estimated channel gains, such problem formulation would be solved much more efficiently. We solve the dynamic spectrum access problem by an interior point DSA optimization algorithm. This algorithm could obtain the optimal solution in polynomial time. Simulation results show that the interior point DSA optimization algorithm outperforms other existing algorithms.
Subjects
cognitive radio networks
dynamic spectrum access
optimization
machine learning
Type
thesis
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