| Massive Multiple-Input Multiple-Output(Massive MIMO)deploys antenna arrays on a large scale in base station(BS)to provide users with high-speed and reliable transmission services,which has significantly improved communication system performance and become the key technology of the fifth-generation mobile communication system(5G).Orthogonal Frequency Division Multiplexing(OFDM)technology has strong resistance to frequency selective fading and excellent spectrum utilization.Combining OFDM technology with massive MIMO has become a major breakthrough in the field of wireless communications.In order to fully utilize the advantages of massive MIMO-OFDM systems,it is necessary to obtain channel state information(CSI)accurately.However,traditional channel estimation methods are no longer suitable for massive MIMO-OFDM systems due to high pilot overhead and low estimation accuracy.In order to design a channel estimation method for massive MIMO-OFDM systems with high estimation accuracy and low pilot consumption,this paper studies the channel estimation algorithm based on compressed sensing theory.The main research tasks of this paper are as follows:(1)In a massive MIMO-OFDM communication system,the channel matrix has a sparse characteristic.Based on the sparsity of massive MIMO-OFDM communication system,the channel estimation method based on compressed sensing theory is studied.Firstly,this paper analyzes and simulates the traditional greedy reconstruction algorithm.Secondly,aiming at the shortcomings of the sparsity adaptive matching pursuit(SAMP)algorithm which does not need to predict the channel sparsity,an improved regularized double threshold SAMP algorithm(RDT-SAMP)is proposed.Based on the SAMP algorithm,the algorithm combines the ideas of regularization and double thresholds that can change the fixed step size to achieve accurate reconstruction of the signal.Finally,by comparing the simulation results,it is found that the RDT-SAMP algorithm is significantly better than the SAMP algorithm in terms of normalized mean square error(NMSE)and bit error rate(BER)performance.(2)An improved algorithm for convex optimization is proposed on the basis of channel space-time sparsity in massive MIMO-OFDM communication systems.The Iterative Support Detection(ISD)algorithm is an improved convex optimization algorithm in compressed sensing which uses iterative support set detection to improve the reconstruction accuracy of the Basis Pursuit(BP)algorithm,and the algorithm meets the requirement of unknown signal sparsity in actual communication.Therefore,based on the ISD algorithm,combined with the block sparsity of the channel,this paper proposes an improved iterative support detection(IISD)algorithm.The algorithm makes full use of the inherent block sparsity in the block sparse equivalent channel impulse response(CIR),which increases the robustness of detection and improves the performance of channel estimation.The simulation comparison further shows that the IISD algorithm is better than the ISD algorithm in NMSE performance,and compared with the ISD algorithm,the IISD algorithm only adds some comparison operations,and does not increase the complexity of the algorithm. |
