Research On Key Technologies Of Wireless Resource Allocation In Dynamic Spectrum Sharing Systems

With the rapid development of mobile Internet,the explosion amount of wireless data triggers a sharp increase in the demand for spectrum resources.High-frequency electromagnetic waves such as millimeter waves and terahertz are used to alleviate the problem of spectrum gaps.However,due to the poor diffraction ability of high-frequency electromagnetic waves,it is currently difficult to meet the requirements for wide-area coverage of future mobile communications.Therefore,the utilization of spectrum not only pays attention to high-frequency resources,but also taps low-frequency resources.Currently,low-frequency resources are becoming increasingly rare due to the continuous emergence of various radio services.Meanwhile,the traditional method of fixed spectrum allocation is inadaptable to the dynamic changes in spectrum demand,which results in idle and uneven utilized spectrum,leading to the conflict between spectrum supply and demand.Dynamic spectrum sharing technology significantly increases the utilization rate of spectrum resources by adopting a dynamic spectrum management mechanism,and is one of the key technologies to solve the current bottleneck problem of radio spectrum resources.However,the wireless information transmission is extremely vulnerable to hybrid attacks such as eavesdropping and interference due to the openness of architectures,the heterogeneity of modes,the diversity of users,and the density of networking of dynamic spectrum sharing systems,which also causes huge energy overhead in the wireless communication system.To this end,this dissertation aims at improving the secrecy rate(SR),energy efficiency(EE)and secrecy energy efficiency(SEE)of wireless communications,and providing insightful study on the wireless resource allocation technology in the dynamic spectrum sharing system.The main research contents and innovations of this dissertation are as follows:First of all,for the secure transmission problem in a heterogeneous cellular network,power allocation schemes of eavesdropper’s instantaneous channel state information(CSI)based sum SR maximization(ICSI-SSRM)and eavesdropper’s statistical CSI based sum SR maximization(SCSISSRM)are proposed.The transmit power of macro base station and small base station across different orthogonal frequency division multiplexing subcarriers are optimized under the constraint of the transmit power of macro base station to maximize the sum SR of the heterogeneous cellular network.The successive convex approximation(SCA)method is used to achieve the near-optimal solutions of proposed ICSI-SSRM and SCSI-SSRM optimization problems,and corresponding iterative power allocation algorithms are presented to solve proposed sum SR maximization schemes.The results show that the proposed ICSI-SSRM and SCSI-SSRM algorithms have good convergence performance,and the sum SR performance of the proposed ICSI-SSRM and SCSI-SSRM schemes are better than the traditional power allocation schemes.Secondly,for the transmission energy consumption problem in a heterogeneous cellular network,an EE maximization based joint power and bandwidth allocation(EEM-JPBA)scheme is proposed.A joint power and bandwidth allocation problem is formulated to maximize the EE of small cells under the constraints of a guaranteed quality-of-service requirement of the macro cell and the limited total transmit power and bandwidth of small base stations.The proposed non-convex EEM-JPBA optimization problem is solved by applying the Dinkelbach and augmented Lagrangian multiplier methods.Moreover,a new two-tier iterative algorithm is presented to obtain the optimal solution of the EEM-JPBA scheme.The results show that the proposed EEM-JPBA algorithm has good convergence performance,and the EEM-JPBA scheme significantly outperforms the conventional power and bandwidth allocation methods in terms of their EE performance.Then,for the physical-layer security and energy consumption problems of wireless transmission in a cognitive wireless network,power allocation schemes for instantaneous CSI of eavesdropper based SEE maximization(ICSI-SEEM)and statistical CSI of eavesdropper based SEE maximization(SCSI-SEEM)are proposed.The power allocation problems are formulated to maximize the SEE of the cognitive network while satisfying the secure communication of primary and cognitive networks.Since proposed ICSI-SEEM and SCSI-SEEM optimization problems are non-convex,fractional optimization problems are equivalently transformed into corresponding subtractive forms through the Dinkelbach method,and then the SCA approach is used to obtain near-optimal solutions of transformed non-convex optimization problems.Furthermore,two-tier iterative power allocation algorithms are presented to respectively solve the proposed ICSI-SEEM and SCSI-SEEM problems.Simulation results illustrate that the proposed ICSI-SEEM scheme has a better SEE performance than the SCSI-SEEM scheme,and moreover,the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance.Finally,for the physical-layer security and energy consumption problems of wireless transmission in a two-tier heterogeneous cellular network,a joint beamforming design scheme combing confidential signals and artificial noise is proposed,namely joint beamforming based SEE maximization(JBF-SEEM)scheme.In order to defend against eavesdroppers,the artificial noise signal is emitted at a macro base station and small base stations at the expense of transmit power.A JBF-SEEM problem is formulated to maximize the SEE while guaranteeing the power constraints for the macro base station and small base stations.Dinkelbach and SCA methods are applied to solve the proposed non-convex optimization problem,and a two-tier iterative JBF-SEEM algorithm is proposed to obtain the near-optimal beamforming solution.Numerical results show that the proposed JBF-SEEM algorithm is convergent while achieving a better SEE performance than conventional approaches.