Research On Adaptive Transmission In Cognitive Radio Based On Cognitive Engine

With the advancements in wireless communications, there is a definite need for an intelligent wireless system that is aware of its environment and able to accordingly change its mode of operation. In this context, the cognitive radio comes into being. A cognitive-radio system represents a potential solution for future wireless communication systems. Its objective is to improve spectrum usage efficiency and minimize the problem of spectrum over-crowdedness. The operation of a cognitive-radio system is mainly divided into two tasks. In the first task, a cognitive-radio device searches and identifies any part of the spectrum that is not occupied. The second task consists of achieving an optimal mode of communication by adjusting the transmitter parameters adaptively. Under the assumption that the idle spectrum has been detected, this dissertation designs the cognitive engine, to enable the cognitive radio to complete the second task. This dissertation focuses on the adaptive transmission in cognitive radio, and studies the design of channel cognitive engine and cognitive decision engine based on channel estimation, channel classification, artificial intelligence and link adaptation techniques to achieve the optimal configuration of radio spectrum resource and efficient, reliable adaptive transmission.Firstly, the decision process of cognitive decision engine in cognitive radio can be modeled as a multi-objective optimization problem. Thus the multi-objective optimization theory are described and analyzed. This dissertation analyzes the multi-objective optimization issue in the context of cognitive radio based on studying the basic representation for multi-objective optimization, convex space, concave space and Pareto optimization edge. Then it can be concluded that the artificial intelligence algorithms and adaptive transmission techniques need to be used to resolve the above mentioned multi-objective optimization issue. The adaptive modulation and coding technique is focused on based on the study of the basic theory of adaptive transmission and physical layer adaptive techniques.Secondly, a novel channel cognitive engine is proposed to sense the external channel state, which can not only estimate channel transfer function, but also classify the channel state. The improved least squares algorithm is used by this engine for channel estimate. The above mentioned algorithm can improve the precision by designing the threshold and processing the coarse estimate and fine estimate of the channel transfer function. The channel classification is processed by hidden Markov model and binary chaotic particle swarm optimization algorithm given by this dissertation. The hidden Markov model is trained by the binary chaotic particle swarm optimization algorithm offline, while the hidden Markov model based algorithm is used for channel classification online. Simulation results and analysis show that under the conditions of the time-varying channels in mountainous area, the proposed channel cognitive engine can accurately estimate and classify the channel and provide the basis for the cognitive engine to make decision.Thirdly, According to the characteristics of the cognitive OFDM system, a chaotic particle swarm optimization algorithm based decision engine is designed. The model of cognitive OFDM system is given, and then the input and output of the cognitive engine are analyzed. This dissertation proposes a binary chaotic particle swarm algorithm. Then a cognitive decision engine based on it is designed. According to different communication modes, this engine can adjust the weighting factors for optimization along different optimization directions, which can resolve the issue of deciding the optimal transmission scheme effectively with the requirements of different communication services in cognitive OFDM systems. Simulation results show that the proposed cognitive decision engine, which has higher fitness value and stronger robustness, is better than the other existing engines.Finally, according to the characteristics of the SC-FDE system and the time-varying channels in mountainous area, two cognitive decision engines are designed for slow time-varying channels and fast time-varying channels respectively. This dissertation presents a new adaptive algorithm and then proposes a decision engine based on the above algorithm for slow time-varying channels. Moreover, a threshold adjustment and adaptive modulation and coding based cognitive decision engine for fast time-varying channels. Theory and simulation analysis show that the proposed cognitive decision engines can decide the optimal transmission scheme reasonably under the complex conditions of the time-varying channels in mountainous area cooperated with channel cognitive engine and cognitive radio knowledge base, while they have an advantage in terms of the computational complexity and project realization. Thus, the proposed cognitive decision engines ensure the SC-FDE cognitive systems with high reliability and efficient adaptive transmission.