.png){kind=icon}
.png){kind=icon}
Return
A Low-Complexity Massive MIMO Precoding Algorithm Based on Chebyshev Iteration
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2760881.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Precoding algorithm is used to transmit signals effectively and to reduce the interferences from other user terminals in the massive multiple-input multiple-output (MIMO) systems. In order to decrease the computational complexity of the precoding matrix, this paper proposes a new precoding algorithm. We use Chebyshev iteration to estimate the matrix inversion in the regularized zero-forcing precoding (RZF) algorithm. It does not need to compute the matrix inversion directly but uses iterations to estimate the matrix inversion. Therefore, the computational complexity can be decreased in this way. Furthermore, Chebyshev iteration has lower convergence rate, and it can gain precoding matrix quickly. This paper analyzes the performance of the Chebyshev-RZF precoding algorithm using average achievable rate and computes the complexity of the algorithm. Then, this paper optimizes initial values of the Chebyshev iteration algorithm on the basis of the feature of massive MIMO systems and makes initial values easier to be obtained. Simulation results show that after two iterations, the Chebyshev-RZF precoding algorithm can get similar average achievable rate as the RZF precoding algorithm does. An optimized Chebyshev-RZF precoding algorithm gets similar performance to the Chebyshev-RZF precoding algorithm after one iteration.
Keywords:
RZF precoding
Chebyshev iteration
Newton iteration
massive MIMO
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W