Channel Estimation And Pilot Assignment For Clustering Based Cell-free Massive MIMO Systems

Nowadays,with the rapid development of information technology and advanced manufacturing technology,the production mode of traditional manufacturing is changing to intelligent.Smart factories have put forward higher requirements on energy efficiency and spectrum efficiency.In recent years,cell-free massive multiple-input multiple-output(MIMO)has received widespread attention.The cell-free massive MIMO in the smart factory improves energy efficiency and spectrum efficiency.However,in order to obtain accurate channel state information(CSI),the cell-free massive MIMO technology still faces many practical challenges.We propose a low-complexity enhanced K-means clustering(E-KMC)algorithm for semi-blind channel estimation of uplink cell-free massive multiple-input multipleoutput(MIMO)systems.The proposed E-KMC algorithm is applicable to a system with a wide range of the number of transmit antennas,modulation order and frame length.It operates with significantly less clusters and complexity than the K-means clustering(KMC)algorithm while achieving enhanced bit error rate(BER)performance,as the KMC algorithm converges extremely slowly even with just medium modulation order and medium number of transmit antennas.Moreover,the proposed E-KMC scheme can achieve significant spectral efficiency improvement over KMC with spatial modulation(SM).A near-optimal short pilot is designed to assist clustering of the E-KMC based channel estimation scheme.The semi-blind receiver structure achieves a BER performance that is very close to the case with perfect CSI,as well as a mean square error(MSE)of channel estimation that is very close to the theoretical lower bound derived in the paper.We derive the exact and closed-form signal to interference plus noise ratio(SINR)and BER expressions and the validity of the derived error probability expression is verified through simulations.The proposed E-KMC based channel estimation scheme also significantly outperforms other types of semi-blind channel estimation approaches including second-and higher-order statistics based and machine learning based approaches,while at a much lower complexity.Aiming at the pilot contamination of cell-free massive MIMO systems,a joint clustering and tabu search algorithm is proposed.Using clustering to allocate pilots for some sensors can effectively reduce the search space for subsequent tabu search,thereby reducing the complexity of pilot assignment.The simulation results show that the proposed algorithm achieves a significant reduction in complexity while eliminating the pilot contamination effectively.In addition,it outperforms other classical methods such as random assignment and greedy search.