Intelligent reflecting surface(IRS)can efficiently improve the performance of wireless commu-nication networks by intelligently reconfiguring the wireless propagation environment.Recently,IRS has been integrated with ...Intelligent reflecting surface(IRS)can efficiently improve the performance of wireless commu-nication networks by intelligently reconfiguring the wireless propagation environment.Recently,IRS has been integrated with cognitive radio(CR)network in order to improve the resource utilization of communication systems.It is a challenging issue for IRS-assisted CR networks to improve the rate performance of the secondary user(SU)through the rational design of IRS passive beamforming while limiting the interference to the primary network.This paper investigates the optimization of downlink rate of SU in a double-IRS-assisted CR network.The achievable rate is maximized by jointly optimizing the active beamforming vector at the secondary transmitter(SU-TX)and the coop-eratively passive reflective beamforming at the two distributed IRSs.To solve the proposed non-con-vex joint optimization problem,the alternating optimization(AO)and semidefinite relaxation(SDR)techniques are then adopted to iteratively optimize the two variables.Numerical results vali-date that the proposed double-IRS assisted system can significantly improve the performance of the CR network compared with the existing single-IRS assisted CR system.展开更多
The common reflection surface （CRS） stack is based on the local dip of the reflector and the reflection response within the first Fresnel zone. During the CRS stack all the information given by a multi-coverage refl...The common reflection surface （CRS） stack is based on the local dip of the reflector and the reflection response within the first Fresnel zone. During the CRS stack all the information given by a multi-coverage reflection dataset can be successfully utilized. By now, it is known as the best zero-offset （ZO） imaging method. In this paper high quality CRS kinematic parameter sections are obtained by a modified CRS optimization strategy. Then stack apertures are calculated using the parameter sections which finally results in the realization of the CRS stack based on optimized aperture. Thus the advantages of CRS parameters are fully developed. Application to model and real seismic data reveals that, compared with the image section by a conventional CRS stack, the image section by CRS stack based on an optimized aperture improves both the signal-to-noise ratio and the continuity of reflection events.展开更多
The authors use the common offset ground penetrating radar(GPR)data inversion based on ray theory to estimate interval velocity and to obtain the relative permittivity.In the ray-tracing based inversion,the input data...The authors use the common offset ground penetrating radar(GPR)data inversion based on ray theory to estimate interval velocity and to obtain the relative permittivity.In the ray-tracing based inversion,the input data are the offset distance between antennas,the velocity of the first layer,the pick-up amplitude and re-ference amplitude of each reflection layer.The thickness and velocity of each layer are calculated by this recursive method.Firstly,the horizontal homogeneous layered medium model is established,and the ideal inversion results are obtained.Subsequently,Monte Carlo method is used to establish a randomly undulating homogeneous layered medium model.The common offset GPR data for the built geological model is then simulated by finite-difference time-domain(FDTD).It proved that this ray-tracing based inversion method is feasible for the horizontal layered geological model,even the layered geological model with random undulation.Undulation,represented by RMS height and CL(correlation length),influences the inversion results.Finally,a more complex geological model--pinch-out model was established.In the pinch-out model,the pinch-out interface can be clearly identified,though there is a false anomaly,which will not significantly affect the identification of the underground medium structure.展开更多
基金Supperted by the National Natural Science Foundation of China(No.61971310,62371341).
文摘Intelligent reflecting surface(IRS)can efficiently improve the performance of wireless commu-nication networks by intelligently reconfiguring the wireless propagation environment.Recently,IRS has been integrated with cognitive radio(CR)network in order to improve the resource utilization of communication systems.It is a challenging issue for IRS-assisted CR networks to improve the rate performance of the secondary user(SU)through the rational design of IRS passive beamforming while limiting the interference to the primary network.This paper investigates the optimization of downlink rate of SU in a double-IRS-assisted CR network.The achievable rate is maximized by jointly optimizing the active beamforming vector at the secondary transmitter(SU-TX)and the coop-eratively passive reflective beamforming at the two distributed IRSs.To solve the proposed non-con-vex joint optimization problem,the alternating optimization(AO)and semidefinite relaxation(SDR)techniques are then adopted to iteratively optimize the two variables.Numerical results vali-date that the proposed double-IRS assisted system can significantly improve the performance of the CR network compared with the existing single-IRS assisted CR system.
基金sponsored by the 863 Program (Grant No.2006AA06Z206)the 973 Program (Grant No.2007CB209605)
文摘The common reflection surface （CRS） stack is based on the local dip of the reflector and the reflection response within the first Fresnel zone. During the CRS stack all the information given by a multi-coverage reflection dataset can be successfully utilized. By now, it is known as the best zero-offset （ZO） imaging method. In this paper high quality CRS kinematic parameter sections are obtained by a modified CRS optimization strategy. Then stack apertures are calculated using the parameter sections which finally results in the realization of the CRS stack based on optimized aperture. Thus the advantages of CRS parameters are fully developed. Application to model and real seismic data reveals that, compared with the image section by a conventional CRS stack, the image section by CRS stack based on an optimized aperture improves both the signal-to-noise ratio and the continuity of reflection events.
基金Supported by Project of National Natural Science Foundation of China (No. 41874136)。
文摘The authors use the common offset ground penetrating radar(GPR)data inversion based on ray theory to estimate interval velocity and to obtain the relative permittivity.In the ray-tracing based inversion,the input data are the offset distance between antennas,the velocity of the first layer,the pick-up amplitude and re-ference amplitude of each reflection layer.The thickness and velocity of each layer are calculated by this recursive method.Firstly,the horizontal homogeneous layered medium model is established,and the ideal inversion results are obtained.Subsequently,Monte Carlo method is used to establish a randomly undulating homogeneous layered medium model.The common offset GPR data for the built geological model is then simulated by finite-difference time-domain(FDTD).It proved that this ray-tracing based inversion method is feasible for the horizontal layered geological model,even the layered geological model with random undulation.Undulation,represented by RMS height and CL(correlation length),influences the inversion results.Finally,a more complex geological model--pinch-out model was established.In the pinch-out model,the pinch-out interface can be clearly identified,though there is a false anomaly,which will not significantly affect the identification of the underground medium structure.
