Research On Power Control And Energy Efficiency Strategy In Cognitive Radio Network

With quick upgrading of wireless communication technology,full-speed development of communication services and wide use of emerging mobile terminal devices,wireless communication technology has entered a new ear of repaid development.Facing with shortage of spectrum resources caused by incredible demand growth in radio frequency,cognitive radio technology comes into use as the times require.This dissertation focusing on improving the network energy efficiency in the cognitive radio networks model sharing hierarchical spectrum to research on and optimize energy efficiency within overlay spectrum sharing mode and underlay spectrum sharing mode respectively.For solving the problem of cognitive radio networks energy optimization in underlay spectrum sharing mode,first,by modeling and analyzing cognitive wireless network model,the network energy efficiency function and related constraints are constructed on the premise of no effect on the normal communication of primary user and ensuring the basic communication of the cognitive user.Second,to minimize the minimum transmit power of cognitive users in cognitive wireless networks,a dual-improvement particle swarm optimization(DIPSO)power control algorithm is proposed based on particle swarm optimization(PSO).Simulation results show that the DIPSO algorithm proposed in this paper yields smaller total transmit power of the cognitive users in the network and higher transmission rate.Therefor,the proposed algorithm is efficient in improving network energy efficiency and communication energy saving.As for optimization of energy efficiency in overlay spectrum sharing mode,the author applies Hidden Markov Model to establish a communication mechanism of “channel prediction-preferential perception-accurate access”,with which the blind random perception could be converted into targeted perception,and to improve channel access accuracy and cognitive user throughput.By reducing redundant energy consumption of SU(secondary user)during channel perception stage,the network energy efficiency is improved.Simulations were performed under ideal channel perception scenarios and non-ideal channel perception scenarios respectively,and results show that with 91.7% prediction accuracy,HMM channel model adopted in this paper has higher prediction accuracy and improves network energy efficiency evidently as well,and with more remarkable advantages under non-ideal channel perception circumstance.