Research On Compressed Sensing Based Ofdm Channel Estimation Algorithm

With the research and development of 6G technology,the combination of Orthogonal Frequency Division Multiplexing(OFDM)and satellite communication has become a trend.OFDM technology fully utilizes the spectral resources to provide high-speed parallel transmission of serial data and has excellent anti-multipath interference capabilities,thus being widely used in wireless communication.However,due to the influence of the environment on wireless signal propagation,the received signal may change.Therefore,it is essential to use channel estimation technology to estimate channel characteristics and make appropriate compensation for parameters such as phase and amplitude.This ensures the stability and reliability of the wireless communication system and maximizes data transmission quality and speed.Numerous research results show that in practical applications,the channel often exhibits significant sparsity in both the time domain and frequency domain.Compressed sensing theory suggests that highly accurate signal reconstruction can be achieved with limited observation data.Thus,applying compressed sensing theory to channel estimation has become a research hotspot.This paper aims to explore channel estimation methods based on compressed sensing.By proposing an innovative pilot position optimization strategy and a more practical and noise-resistant reconstruction algorithm,the goal of improving channel estimation performance is achieved.The main work of this paper is as follows:First,compare and analyze the differences and connections between OFDM channel estimation and compressed sensing theory,and derive the channel estimation model based on compressed sensing.Second,pilot position optimization method.In response to the poor performance of traditional pilot positions in compressed sensing,this paper proposes an Enhanced Butterfly Optimization Algorithm(EBOA)based on a good point set initialized population,adaptive switching probability,and t-distribution mutation factor.Simulation results show that the proposed algorithm has better performance compared to other algorithms,achieving smaller column coherence coefficients and better Bit Error Rate(BER)and Mean Square Error(MSE)performance during channel estimation.Third,research on compressed sensing recovery algorithm based on channel estimation.In view of the poor anti-noise performance and low efficiency of the atom selection method of the generalized Orthogonal Matching Pursuit(gOMP)algorithm,this paper proposes a new improved gOMP algorithm(generalized Orthogonal Matching Pursuit-Modified,gOMP-M),which guarantees the speed advantage while also taking into account the anti-noise performance to improve the accuracy and stability of channel estimation.This method uses the Jaccard coefficient screening criterion instead of the inner product matching criterion to optimize atom selection,and proposes a method of atom refinement to achieve secondary screening of atoms,effectively solving the problem of greedy algorithm losing dictionary atoms during atom selection.At the same time,the problem of the gOMP algorithm’s poor reconstruction performance caused by too many dictionary atoms being selected in the case of sparse channel estimation of noise-containing signals is also solved.Simulation results show that the proposed algorithm has better reconstruction accuracy compared with OMP,ROMP,gOMP and other algorithms,and has a certain degree of anti-noise ability.