In this paper,we consider a cognitive radio system with energy harvesting,in which the secondary user operates in a saving-sensing-transmitting(SST) fashion.We investigate the tradeoff between energy harvesting,channe...In this paper,we consider a cognitive radio system with energy harvesting,in which the secondary user operates in a saving-sensing-transmitting(SST) fashion.We investigate the tradeoff between energy harvesting,channel sensing and data transmission and focus on the optimal SST structure to maximize the SU's expected achievable throughput.We consider imperfect knowledge of energy harvesting rate,which cannot be exactly known and only its statistical information is available.By formulating the problem of expected achievable throughput optimization as a mixed-integer non-linear programming one,we derive the optimal saveratio and number of sensed channels with indepth analysis.Simulation results show that the optimal SST structure outperforms random one and performance gain can be enhanced by increasing the SU's energy harvesting rate.展开更多
In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by on...In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by one before transmission.By leveraging prediction based on correlation between the licensed channels,we propose a novel spectrum sensing strategy,to decide which channel is the best choice to sense in order to reduce the sensing time overhead and further improve the SU's achievable throughput.Since the correlation coefficients between the licensed channels cannot be exactly known in advance,the spectrum sensing strategy is designed based on the model-free reinforcement learning(RL).The experimental results show that the proposed spectrum sensing strategy based on reinforcement learning converges and outperforms random sensing strategy in terms of long-term statistics.展开更多
基金supported by National Nature Science Foundation of China(NO.61372109)
文摘In this paper,we consider a cognitive radio system with energy harvesting,in which the secondary user operates in a saving-sensing-transmitting(SST) fashion.We investigate the tradeoff between energy harvesting,channel sensing and data transmission and focus on the optimal SST structure to maximize the SU's expected achievable throughput.We consider imperfect knowledge of energy harvesting rate,which cannot be exactly known and only its statistical information is available.By formulating the problem of expected achievable throughput optimization as a mixed-integer non-linear programming one,we derive the optimal saveratio and number of sensed channels with indepth analysis.Simulation results show that the optimal SST structure outperforms random one and performance gain can be enhanced by increasing the SU's energy harvesting rate.
基金supported by National Nature Science Foundation of China(NO.61372109)
文摘In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by one before transmission.By leveraging prediction based on correlation between the licensed channels,we propose a novel spectrum sensing strategy,to decide which channel is the best choice to sense in order to reduce the sensing time overhead and further improve the SU's achievable throughput.Since the correlation coefficients between the licensed channels cannot be exactly known in advance,the spectrum sensing strategy is designed based on the model-free reinforcement learning(RL).The experimental results show that the proposed spectrum sensing strategy based on reinforcement learning converges and outperforms random sensing strategy in terms of long-term statistics.