Bayesian Channel Estimation in Massive MIMO System

Massive MIMO is a promising technology to improve both the spectrum efficiency and the energy efficiency. The key problem that impacts the throughput of a massive MIMO system is the pilot contamination due to the nonorthogonality of the pilot sequences in different cells. Conventional channel estimation schemes cannot mitigate this problem effectively, and the computational complexity is increasingly becoming larger in views of the large number of antennas employed in a massive MIMO system. Furthermore, the channel estimation is always carried out with some ideal assumptions such as the complete knowledge of large-scale fading. In this paper, a new channel estimation scheme is proposed by utilizing interference cancellation and joint processing. Highly interfering users in neighboring cells are identified based on the estimation of large-scale fading and then included in the joint channel processing; this achieves a compromise between the effectiveness and efficiency of the channel estimation at a reasonable computational cost, and leads to an improvement in the overall system performance. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

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Coordinated Wiener Predictor for channel estimation in massive-MIMO system

2017 IEEE 13th Malaysia International Conference on Communications (MICC) ◽

10.1109/micc.2017.8311767 ◽

2017 ◽

Author(s):

M. Hasbullah Mazlan ◽

Ehab Ali ◽

Rosdiadee Nordin ◽

Mahamod Ismail

Keyword(s):

Channel Estimation ◽

Massive Mimo ◽

Mimo System

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Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

Defence Science Journal ◽

10.14429/dsj.67.10984 ◽

2017 ◽

Vol 67 (6) ◽

pp. 668

Author(s):

Qingzhu Wang ◽

Mengying Wei ◽

Yihai Zhu

Keyword(s):

Channel Estimation ◽

Compressive Sensing ◽

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Three Dimensional ◽

Data Representation ◽

Frequency Division ◽

Frequency Division Duplexing

<p class="p1">To make full use of space multiplexing gains for the multi-user massive multiple-input multiple-output, accurate channel state information at the transmitter (CSIT) is required. However, the large number of users and antennas make CSIT a higher-order data representation. Tensor-based compressive sensing (TCS) is a promising method that is suitable for high-dimensional data processing; it can reduce training pilot and feedback overhead during channel estimation. In this paper, we consider the channel estimation in frequency division duplexing (FDD) multi-user massive MIMO system. A novel estimation framework for three dimensional CSIT is presented, in which the modes include the number of transmitting antennas, receiving antennas, and users. The TCS technique is employed to complete the reconstruction of three dimensional CSIT. The simulation results are given to demonstrate that the proposed estimation approach outperforms existing algorithms.</p>

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