Browsing by Author "Lu, Yingqi"
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- ItemOpen AccessCooperative Sensing Algorithm and Machine Learning Technique in Cognitive Radio Network(2016-01-05) Lu, Yingqi; Fattouche, Michael; Sesay, Abu; Bartley, NormanThis thesis investigates spectrum sensing for Cognitive Radio. First, we deal with the hidden node problem and the selection of sensing frequency in a P2P CR system. Different from existing models where TSU executes sensing and transmitting periodically, a novel detecting model is proposed to consider simultaneous sensing and transmitting. At RSU, BER estimation is applied to detect whether a PU is active or not. Simulation results show that i) simultaneous sensing improves spectrum utilization compared with periodical sensing; ii) The BER estimation improves the probability of detection and spectrum utilization. The second challenge related to decision fusion in cooperative sensing. We propose novel schemes based on machine learning and introduced probability vector to improve system performance. Simulation results demonstrate that the new schemes are better than the traditional ones. The probability vector can shorten the training duration and the classification delay compared with energy vector.
- ItemOpen AccessCooperative Spectrum Sensing Algorithm in Cognitive Radio By Simultaneous Sensing and BER Measurements(EURASIP Journal on Wireless Communications and Networking, 2016-05-09) Lu, Yingqi; Wang, Donglin; Fattouche, MichelThis paper considers spectrum utilization, the probability of detection in cognitive radio (CR) model based on cooperative spectrum sensing with both simultaneous adaptive sensing and transmission at a transmitting secondary user (TSU), and the bit error rate (BER) detection with variation checking at a receiving user (RSU). In this paper, a novel detecting model is proposed in the being considered scenario for the full-duplex TSU's simultaneous sensing and transmitting. A spectrum sensing scheme with an adaptive sensing window is designed to improve the spectrum utilization with a high SNR. At RSU, the BER variation is used further to detect whether a PU is active or not. Data fusion based on the proposed adaptive sensing scheme and the BER detection is processed for better decision on the spectrum holes. Simulation results show that i) simultaneous spectrum sensing with an adaptive window improves the spectrum utilization compared with a periodical sensing; ii) cooperative spectrum sensing with the BER-assisted detection improves the probability of detection and spectrum utilization compared with the single simultaneous sensing at TSU.