Research On Resource Optimization Method Of Wireless Interference Network Based On Successive Convex Approximation Technology

Since the development of wireless communication,it has penetrated into almost every corner of modern society.With the further development of the informatization process,people's demand for wireless communication will continue to increase on a large scale.At the same time,the limited wireless spectrum resources that wireless communications rely on will lead to increasing interference in wireless networks.Interference will reduce the quality of wireless signal transmission,resulting in a reduction in the efficiency of wireless resource utilization,which in turn restricts the increase in wireless network capacity.Radio resource management and interference suppression play an important role in improving the radio spectrum and energy utilization efficiency,and increasing the capacity of the wireless network.In this context,this thesis focuses on wireless interference networks and studies power resource optimization and interference suppression techniques.Multi-antenna technology provides new technical solutions for improving the performance of wireless communication systems by expanding the spatial dimension.In a multi-antenna system,beamforming can adjust the direction and amplitude of the transmitted signal to achieve the regulation of interference and signal strength,thus becoming a key technology for power resource allocation and interference suppression between users.However,in multi-user interference networks,due to the influence of interference coupling factors between users,the problem of beamforming optimization design with network and rate as the optimization goal is generally a non-convex NP-hard problem.At present,the efficient solution to such optimization problems is still an open problem,which is very challenging.Successive convex approximation(SCA)is an optimization technique for iteratively solving non-convex optimization problems.It successively solves a series of convex replacement problems of the original problem,thereby gradually converging to the stationary solution of the original problem.Therefore,this paper focuses on SCA-based multi-user interference network power control and multi-antenna beamforming optimization algorithms.The main research content includes the following three parts:(1)For user single-input single-output(SISO)interference channel networks,the optimization problem of "sum-rate-maximum power control" is studied,and an SCA-based power control optimization algorithm is designed.First,the sum rate maximization power control problem under the independent power constraint of the transmitter is modeled.Second,using the nature of the logarithmic function,the sum rate function is converted into a concave difference function.On this basis,the Taylor series expansion is used to construct a concave approximate substitution function at a given point,and then the original problem is transformed into a convex optimization problem.Through the obstacle method and Newton iteration,the optimal solution of the convex optimization replacement problem is obtained,and the solution is used as a fixed point to construct a new approximate function,thereby obtaining an iterative power control solution algorithm.Experimental results show that the proposed SCA-based power control algorithm achieves significantly better sum-rate performance than traditional algorithms(such as geometric planning algorithms).(2)For the user's multiple-input signal-output(MISO)interference channel network,"the optimization problem of the sum rate maximization beamforming under the power constraints of each transmitter" is studied,and the beamforming algorithm based on SCA is designed..Different from the traditional algorithm,the traditional algorithm has no direct optimization and rate lead and rate performance is more conservative.The proposed algorithm overcomes the shortcomings of the traditional algorithm.First,the optimization problem of the sum rate maximization beamforming under the independent power constraint of the transmitter is modeled.Then,through Taylor series expansion,the concave substitution function of the original sum rate function is constructed.Finally,the solution of alternative problems is obtained by Semidefinite Programming Relaxation(SDR)technology.By continuously updating the solution,the stable solution of the original optimization function is obtained.Experimental results show that the SCA-based beamforming optimization algorithm proposed in this paper has a better sum rate value than the classic algorithm.In addition,the experimental results also reveal that when the antenna dimension is less than the number of users,the proposed algorithm can actively close the users with poor channel conditions to ensure the guarantee and maximize the rate.(3)For the MISO cognitive radio(CR)interference channel network,the problem of "optimization of the maximum rate and beamforming under the constraints of the main user service quality requirements" is studied,and the beamforming in the MISO cognitive radio network based on SCA is designed The algorithm achieves efficient sharing of spectrum resources in wireless networks.First,the problem of sum rate maximization under the constraints of the primary user SINR service quality and transmitter power constraints is modeled.Similar to the research content(2),the beamforming algorithm in the MISO cognitive radio network based on SCA and SNR is designed,and a stable solution of the original function is obtained.Experimental results show that the SCA-based beamforming optimization algorithm proposed in this paper can optimize the network and rate while ensuring the SINR requirements of primary users.