Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideratio...Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideration for the sensing overhead and the transmission gain. To solve those problems,an optimized parallel cooperative spectrum sensing strategy based on iterative KuhnMunkres( KM) algorithm was proposed. To maximize the total system profit,it considers the tradeoff between the sensing overhead and the transmission gain. Iterative KM algorithm was applied to obtaining the optimal assignment,which indicated when and which channels secondary users should sense. Furthermore,the required detection probability was introduced to avoid unnecessary waste when the accuracy met the system requirement. Monte Carlo simulations show that the proposed strategy can obtain higher total system profit with fewer cooperative secondary users.展开更多
With the increase of wireless devices and new applications,highly dense small cell base stations(SBS)have become the main means to overcome the speed bottleneck of the radio access network(RAN).However,the highly-dens...With the increase of wireless devices and new applications,highly dense small cell base stations(SBS)have become the main means to overcome the speed bottleneck of the radio access network(RAN).However,the highly-dense deployment of SBSs greatly increases the cost of network operation and maintenance.In this paper,a base station sleep strategy combining traffic aware and high-low frequency resource allocation is proposed.To reduce the service level agreement(SLA)default caused by base station sleep,Long Short-Term Memory(LSTM)algorithm is introduced to predict the traffic flow,based on the predict result,the SBSs sleep and frequency resource allocation are introduced to increase the energy efficiency of the network.Moreover,this paper improves the decision-making efficiency by introducing Kuhn Munkres algorithm(KM)and genetic algorithm(GA).Simulation results show that the proposed strategy can greatly reduce the energy consumption of small cells and the occurrence of SLA default rate.展开更多
基金Young Scientists Fund of the National Natural Science Foundation of China(No.61101141)Fundamental Research Funds for the Central Universities of China(No.HEUCF130807)Heilongjiang Province Natural Science Foundation for the Youth,China(No.QC2012C070/F010106)
文摘Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideration for the sensing overhead and the transmission gain. To solve those problems,an optimized parallel cooperative spectrum sensing strategy based on iterative KuhnMunkres( KM) algorithm was proposed. To maximize the total system profit,it considers the tradeoff between the sensing overhead and the transmission gain. Iterative KM algorithm was applied to obtaining the optimal assignment,which indicated when and which channels secondary users should sense. Furthermore,the required detection probability was introduced to avoid unnecessary waste when the accuracy met the system requirement. Monte Carlo simulations show that the proposed strategy can obtain higher total system profit with fewer cooperative secondary users.
文摘With the increase of wireless devices and new applications,highly dense small cell base stations(SBS)have become the main means to overcome the speed bottleneck of the radio access network(RAN).However,the highly-dense deployment of SBSs greatly increases the cost of network operation and maintenance.In this paper,a base station sleep strategy combining traffic aware and high-low frequency resource allocation is proposed.To reduce the service level agreement(SLA)default caused by base station sleep,Long Short-Term Memory(LSTM)algorithm is introduced to predict the traffic flow,based on the predict result,the SBSs sleep and frequency resource allocation are introduced to increase the energy efficiency of the network.Moreover,this paper improves the decision-making efficiency by introducing Kuhn Munkres algorithm(KM)and genetic algorithm(GA).Simulation results show that the proposed strategy can greatly reduce the energy consumption of small cells and the occurrence of SLA default rate.
