Abstract
Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. Simulation results show that the proposed scheme can effectively reduce the deviation, improve the channel estimation accuracy, mitigate the impact of pilot contamination and obtain accurate CSI with low overhead and computational complexity.
Highlights
Massive Multiple-input Multiple-output (MIMO) is a very novel communication technology that uses large-scale antenna arrays to replace multiple antennas in 4G networks in existing base station, forming a large-scale MIMO communication environment [1,2,3]
The study found that the performance of massive MIMO technology relies mainly on accurate channel state information (CSI), but existing research indicates that pilot contamination [11] is the main interference problem in channel estimation
In order to overcome the shortcomings of the above algorithms, especially [13], we proposed an improved joint Singular Value Decomposition (SVD) and ILSP algorithm to effectively improve the detection performance and reduce the computational complexity of the channel estimation in massive MIMO system
Summary
Massive MIMO is a very novel communication technology that uses large-scale antenna arrays to replace multiple antennas in 4G networks in existing base station, forming a large-scale MIMO communication environment [1,2,3]. The study found that the performance of massive MIMO technology relies mainly on accurate channel state information (CSI), but existing research indicates that pilot contamination [11] is the main interference problem in channel estimation. In [13], a semi-blind channel estimation algorithm based on Singular Value Decomposition (SVD) is proposed for massive MIMO system. As the existing SVD algorithm in [13] is based on the assumption that the covariance matrix is obtained by using limited sample data instead of real data This assumption limits the performance of [13] which motivates the proposed research work in which the novel contribution is to overcome the limitations of [13] and propose an improved SVD-ILSP algorithm to efficiently estimate the channel with high accuracy than [13].
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