Abstract
In massive multiple-input multiple-output (MIMO) system, scaling up the antennas of base station (BS) has a clear benefit on sum rate and energy efficiency, but the signal processing complexity can be very high and many algorithms cannot be implemented in practice for high hardware cost. Approximative Matrix Inverse Computations (AMIC) algorithm is a kind of low-complexity precoding for large multiuser MIMO systems, but the Bite Error Rate (BER) performance is shown to be not better than the classical MMSE precoding. To improve the BER performance of AMIC algorithm, in this paper, we use norm minimization algorithm to change the coefficient of the precoding matrix to improve the BER performance of AMIC algorithm. It can verify that the proposed algorithm can achieve better BER performance than the AMIC algorithm by using only a limited number of Neumann series iterations, and keep lower complexity. The proposed scheme is a compromise solution between complexity and BER performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
