Research On Pilot Design And Channel Estimation With Low Complexity In Massive MIMO Systems

By deploying hundreds of antennas at the base station(BS)and serving tens of single-antenna users in the same time-frequency resource block,massive multiple-input-multiple-output(MIMO)systems have attracted much attention.It can enhance the spectrum efficiency and energy efficiency by orders of magnitude.As these benefits depend on the accuracy of estimating the channel state information(CSI)at the base station,the estimation of CSI has become a great challenge.Perfect CSI means severe pilot overhead,resulting in a decline in system performance.Therefore,pilot design and CSI estimation are the most important factors restricting system performance.For single-cell cellular system,the most serious problem is the frequency division duplex(FDD)downlink channel estimation problem.For multi-cell cellular system,the most serious problem is the time division duplex(TDD)uplink channel estimation problem.Therefore,we research on the pilot design and channel estimation with low complexity for single-cell cellular FDD downlink system and multi-cell cellular TDD uplink system.For single-cell cellular massive MIMO system,we explore the joint sparsity of multi-path massive MIMO channel in delay-angle domain.By exploiting the joint sparsity,a decoupling pilot design scheme with low overhead and low complexity is proposed for FDD massive MIMO systems.In the proposed pilot design scheme,the pilot design is decoupled into two parts.In the first part,two two-layer greedy iterative algorithm(TLGIA)are developed to obtain the pilot subcarrier pattern,which are global optimal selection greedy iterative algorithm(GOS-GIA)and local optimal selection greedy iterative algorithm(LOS-GIA).The proposed TLGIA algorithm is based on the mutual incoherence property(MIP)of the pilot matrix.In each iteration of the inner loop,we select the worst subcarrier and best subcarrier respectively,and the worst subcarrier is replaced by the best subcarrier.In the second part,random Rademacher distribution pilot matrix is used as the angle-domain pilot matrix.Additionally,we analyze the algorithm complexity and system performance of the proposed pilot design scheme.Simulation results verify that the proposed scheme achieves the better performance with lower pilot overhead.With the proposed decoupling pilot design scheme by exploiting the joint sparsity in delay-angle domain,one corresponding channel estimation strategy is required.By utilizing the joint sparsity,compressed sensing(CS)method may be employed for the channel estimation to reduce the pilot overhead.However,the complexity of the channel estimation by utilizing the joint sparsity is very high.So we need to propose a new channel estimation method with low complexity to ensure the accuracy of channel recovery.Based on the proposed pilot design scheme,one corresponding two-stage channel estimation strategy is provided.The first stage of the strategy is concerned with retrieving the positions of the non-zero dominant taps in delay domain.To retrieve the non-zero taps,one block subspace pursuit(Block-SP)algorithm is developed by exploiting the sparse common support(SCS).The second stage focuses on estimating the channel coefficients at these taps.One parallel subspace pursuit(Parallel-SP)algorithm is developed to estimate the coefficients.Additionally,we analyze the algorithm complexity and system performance of the proposed channel estimation.Algorithm complexity analysis shows that the proposed channel estimation strategy reduces the computational complexity by more than two orders of magnitude.Simulation results verify that the proposed channel estimation scheme achieves the better performance.For multi-cell cellular TDD uplink massive MIMO system,a low complexity and universal strategy of pilot sequences dynamic allocation is proposed to mitigate pilot contamination.We allocate the same pilot sequences set for the center users in different cells,while mutually orthogonal pilot sequences sets for the edge users in adjacent cells.By utilizing the proposed pilot sequences dynamic allocation strategy with eliminated pilot contamination for the edge users,we analytically derive the approximate system capacity which is accurate when the number of antennas at the base station tends to infinite.Finally,the simulation results show that the proposed pilot sequences allocation strategy achieves higher system capacity than the traditional pilot sequences allocation strategies whose sequences reuse rate is one or three.The optimal number of pilot sequences in different signal to noise ratio(SNR)to maximize the system capacity is also analyzed.