Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.
In cognitive radio (CR) networks, Learning Engine has considerable significance on dynamic spectrum access (DSA) and implementation of cognitive function. In this paper, a cross-layer learning engine design scheme is proposed by jointly considering physical-layer dynamic channel selection, modulation and coding scheme, data-link layer frame length in CR networks, with the purpose to maximize system throughput and simultaneously meet heterogeneous Quality of Service (QoS) requirements. The wireless fading channel is modeled as a continuous state space Markov decision process (MDP) and the licensed network activity is abstracted as a finite-state one. We introduce Q-learning algorithm to realize the function of learning from state space and adapt wireless environment. And meanwhile a large scale Qfunction approximator based on support vector machine (SVM) is employed to effectively reduce storage requirement and decrease the operation complexity. A cross-layer learning engine communication platform is realized by using Matlab simulator. the simulation results demonstrate that while lacking system prior knowledge, the learning engine can effectively achieve configuration function by system cross-layer learning approach, and furthermore, it can converge to the best—i.e., realize reconfiguration function in CR networks while meeting users’ QoS.