Abstract
Massive multi-input multi-output (MIMO) has been regarded as one of the key technologies for fifth generation (5G) mobile communication systems, as it can significantly enhance the system capacity with high spectrum and energy efficiency. For massive MIMO systems, accurate channel estimation is a challenging problem, especially when the number of parameters to be estimated is large and the number of pilots is limited. In this paper, a compression-based linear minimum mean square error (CLMMSE) channel estimation algorithm is proposed for massive MIMO in 5G systems. Compared with the traditional linear minimum mean square error (LMMSE) algorithm, the proposed approach calculates the channel autocorrelation matrix by investigating the channel prior information based on compressive sensing (CS) theory, utilizing the block sparsity of massive MIMO channels, to reduce the complexity for obtaining autocorrelation matrix. Then it substitutes matrix inverse operation by singular value decomposition to further reduce the computational complexity. In addition, a block sparsity adaptive matching pursuit (BSAMP) method is also proposed to adaptively estimate the block sparsity of the channel in the first step of the proposed CLMMSE algorithm, which can make it more efficient. The sparsity-adaptive processing is achieved by setting a threshold and finding the position of the maximum backward difference, then using the regularized method to solve channel estimation as a convex optimization problem. Analyses and simulations indicate that with slight performance degradation, the proposed algorithm reduces the computational complexity significantly compared with the traditional LMMSE algorithm. And compared with pure CS methods, CLMMSE has an obviously better performance, which is beneficial to solve the pilot pollution problem of massive MIMO in 5G systems. Furthermore, the BSAMP-based CLMMSE algorithm has better performance and lower time complexity than the algorithm based on other CS methods, which further improves the system performance.
Highlights
In the area of mobile communications, new technologies being able to increase the capacity and spectrum efficiency are forever needed to satisfy the increasing data rate demand from the users
Based on all the descriptions above, we summarize and list all the contributions of this paper here: First, by utilizing the sparsity of massive Multi-input multi-output (MIMO) channels, a compression based linear minimum mean square error (LMMSE) channel estimation algorithm named compression based linear minimum mean square error (CLMMSE) is proposed, which gains the channel autocorrelation matrix by compressive sensing (CS) estimated channel prior information, solving the problem of obtaining the autocorrelation matrix, reducing the complexity of traditional LMMSE based channel estimation, improving the spectrum utilization compared with pure CS estimation for 5G wireless communication systems
In this paper, a compression based LMMSE algorithm named CLMMSE is proposed to recover channel state information, which is a challenging problem for massive MIMO in 5G systems
Summary
In the area of mobile communications, new technologies being able to increase the capacity and spectrum efficiency are forever needed to satisfy the increasing data rate demand from the users. A compression based LMMSE (CLMMSE) channel estimation algorithm is proposed to reduce the complexity of LMMSE algorithm and enhance the performance of pure CS based estimation for massive MIMO systems It uses the channel prior information estimated by CS theory to gain channel autocorrelation matrix, this operation using optimal rank reduction compared with traditional channel autocorrelation matrix calculation. If the step size is much smaller than the signal sparsity, a large number of iterations will be required It has been demonstrated in [20] that the sub-channels between different transmitting and receiving antenna pairs have the same sparsity support set in massive MIMO systems, and an adaptive structured subspace pursuit (ASSP) algorithm exploiting such joint sparsity has been proposed in [21]. The sparsity adaptive processing is more flexible, for it does not rely on fixed step size, which reduces the computational complexity effectively
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