Research On Channel Estimation Based On Index Modulation Orthogonal Frequency Division Multiplexing System

With the popularity of smartphones and the rise of Io T(Internet of Things),data traffic transmitted by wireless networks has experienced an exponential growth on a yearly basis.To meet the ever-increasing demands and challenges of system capacity and user communication rate,expanding and fully utilizing wireless channel resources are two core parts that should be highlighted in the design of the wireless transmission.The combination of MIMO and OFDM featuring high spectrum utilization and anti-multipath interference,plays a critical role in improving transmission rate,transmission reliability,system spectrum efficiency,and interference suppression.Besides,accurate channel state information is required by the base station for processing such as precoding and beamforming,so as to fully mine the technical advantages of the MIMO system.Meanwhile,accurate channel parameters also contribute to restraining the influence of pilot interference brought about by the ultra-dense network(UDN).Evidently,the top priority is to acquire the channel matrix.Compressive sensing,as one of the research hotspots in recent years,which has been extensively applied in a multitude of fields for it can save a mass of resources and costs in data transmission and storage by simultaneously conducting signal acquisition and compression processes.Since wireless communication is also one of the focuses of compressive sensing research,the application of compressive sensing in channel estimation.It is worth noting that compressive sensing technology can be for channel estimation due to the sparsity of the channel in the broadband communication system.Moreover,many studies show that the channel estimation algorithm based on compressive sensing can achieve channel estimation performance that is more favorable than that of the traditional channel estimateon algorithm using a few observations,contributing to the improvement of link performance and the comprehensive utilization of spectrum resources.To sum up,this thesis is primarily concerned with the application of the adaptive compressive sensing reconstruction algorithm in the SISO-OFDM system and the MIMO-OFDM system.The main contributeons are presented as follows:(1)A transmission model is constructed for the OFDM system under the sparse channel.On this basis,the compressive sensing reconstruction algorithm OMP is applied to the channel estimation of the transmission model.In this way,it verifies that the reconstruction algorithm based on compressive sensing is more effective in reconstruction accuracy and utilization of spectrum resources,in comparison to the traditional channel estimation algorithm through simulation.(2)The compressive sensing theory is utilized to study the OFDM system based on the sparse channel,and apply the proposed Variable Step Adaptive Compressive Sampling Matching Pursuit(VSACSMP)algorithm to the channel estimation of the system.As can be observed from the simulation result,the proposed VSACSMP algorithm is superior to the traditional adaptive compressive sensing reconstruction algorithm in terms of channel estimation.(3)A PSAMP algorithm using short pilot frequency is proposed after the transmission model is established for the MIMO-OFDM system under the sparse channel,which is composed of sparsity pre-estimation and tracking reconstruction.To begin with,an estimated value that is slightly smaller than the real sparsity is obtained through the trial of power function.Secondly,estimated results are improved by reconstructing signals through the compressive sampling matching pursuit.If the reconstruction fails,then the number of signal atoms should be increased gradually.Finally,the simulation results suggest that the proposed PSAMP algorithm has better channel estimation performance in the field of high signal to noise ratio than the traditional adaptive compressive sensing reconstruction algorithm.When the signal-to-noise ratio is greater than 10 d B,the performance of the three algorithms is improved with the increase of SNR.VSSt AMP algorithm has the advantage of 4d B compared with OMP algorithm,and the NMSE curve of PSAMP algorithm is about 3d B lower than VSSt AMP.The PSAMP algorithm proposed in this paper is better than other algorithms.