Research On Efficient Spectrum Sensing And Resource Allocation Algorithm In Cognitive IoV Networks

With the increase of vehicles,access equipment and the emergence of a large number of new wireless network services,the Internet of Vehicle(Io V)demands for wireless spectrum resources is increasing,which makes the existing spectrum resources allocated fixedly to Io V seriously scarce.To solve this problem,Cognitive Radio(CR)technology is introduced in Io V to build a Cognitive Internet of Vehicle to achieve on-demand dynamic allocation of spectrum resources and improve spectrum utilization.This thesis aims at three major problems,including the possibility of errors when transmitting cooperative spectrum sensing result and high transmission energy consumption in cognitive Io V,the imbalance between system throughput and the algorithm complexity of resource allocation strategy,the delay of the channel transient state estimation due to vehicle movement and the interference of SU to PU,studies the algorithms of multi-user spectrum sensing and resource allocation in cognitive Io V.The main work and contributions are as follows:1.Aiming at the problem of low energy efficiency using the traditional OSA model spectrum sensing in cognitive Io V,this thesis proposes a power allocation algorithm of cooperative spectrum sensing to maximize the energy efficiency of the system when the transmission cooperative spectrum sensing result is not perfect.First,use hybrid spectrum access technology for system modeling,and consider the probability of errors when sensing nodes report channel status to determine the energy efficiency function of the system.Then jointly consider the system constraints including SU average transmit power threshold,PU interference threshold,minimum communication rate,etc.to establish the objective function to maximize the energy efficiency of the system.Finally,convexly optimize the objective function using fractional programming and combine with the Lagrange multiplier iteration method to obtain the optimal power allocation.Simulation results show that compared with the traditional model,the proposed model can further improve the system energy efficiency under the premise of ensuring the sensing performance,which is very meaningful for the energy saving of the vehicle system.2.Aiming at the problem of over-the-horizon communication in cognitive Io V,which requires the use of relay transmission,this thesis proposes a low-complexity suboptimal step-wise resource allocation algorithm based on asymmetric relay with the goal of maximizing system throughput.First,establish the objective function of maximizing system throughput under constraints such as power,interference threshold,transmission rate,and total slot length.Then,evenly distribute the power on cognitive sources and relays to quickly obtain the optimal solution for the subcarrier allocation in different relays.Finally,re-optimize the power allocation by an alternating iterative algorithm.Simulation results show that the proposed algorithm not only can maximize system throughput,but also has low algorithm complexity,and is suitable for cognitive Io V systems that require faster transmission rates,higher reliability,and wider coverage.3.Aiming at the problem of rapid channel state changes due to vehicle movement in cognitive Io V and the need to better protect the primary user's communication,this thesis proposes a resource allocation algorithm under an estimated channel transient condition to minimize the interference caused by the secondary user to the primary user.First,use the location prediction technology to predicate the channel state information of next time and set protection parameters to combat the prediction error caused by shadow fading.Then,under the constraints of SU's Qo S,cognitive base station total transmission power,primary user interference threshold,and minimum communication rate,establish an objective function that minimizes the interference.Finally,obtain the optimal solution by decoupling and using the Hungarian combination algorithm.Simulation results show that the proposed algorithm can better reduce the interference to the primary user while ensuring the SU's Qo S requirements and the power constraints of the cognitive base station.