Compression-Based LMMSE Channel Estimation With Adaptive Sparsity for Massive MIMO in 5G Systems

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 (SVD) 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 benefit 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.

Decoupling Channel Estimation for FDD Massive MIMO Systems Utilizing Joint Sparsity

“…To reduce the pilot overhead, some non-orthogonal pilot designs and channel estimation techniques based on channel statistics have been proposed for massive MIMO systems [9], [10], [13]- [16]. Based on the long-term channel statistics at the user side, [17] proposed an open-loop and closed-loop channel estimation strategy for massive MIMO by exploiting the practical MIMO channels correlation in time and space.…”

Average SER Analysis for Layered Division Multiplexing System with Index Modulation

“…The simulation of the proposed system is implemented based on these two diagrams. Besides of them, in the simulation, a frequency-selective slow Rayleigh Fading channel is considered as the channel model and a two-layer power structure is exploited to serve two UEs with individual input bits [18], [19]. The injection level between two layers is assumed larger enough to avoid interference between each other.…”

Channel Estimation for MmWave Massive MIMO With Hybrid Precoding Based on Log-Sum Sparse Constraints

“…Due to the beam squint of the mmWave MIMO system, M. Wang et al utilized the shift-invariant block sparsity of the resulting nonstandard channel model to design a compressive sensing-based channel estimation algorithm [7]. The work in [8] exploited the block sparsity of massive MIMO channels, and calculated the channel autocorrelation matrix by investigating the channel prior information based on compressive sensing (CS) theory. Then, the L.Ge et alof [8] used regularized method to treat the channel estimation as a convex optimization problem.…”

“…The work in [8] exploited the block sparsity of massive MIMO channels, and calculated the channel autocorrelation matrix by investigating the channel prior information based on compressive sensing (CS) theory. Then, the L.Ge et alof [8] used regularized method to treat the channel estimation as a convex optimization problem. S. Rao et al established performance bounds on the channel estimation of one-bit mmWave massive MIMO receivers for different types of channel models, and considered the structured of the channel model [9].…”