Abstract
Precoding is one of the most important technologies for downlink massive multiple-input multiple-output (MIMO) systems. Although the popular weighted minimum mean-square error (WMMSE) algorithm is guaranteed to converge to at least a local optimum of the weighted sum rate (WSR) maximization problem, its computational complexity is still too high due to the complicated matrix inversion. To this end, an iterative method based WMMSE algorithm (IM-WMMSE) is proposed in this paper. By adopting the traditional iterative methods to bypass the matrix inversion, the complexity of IM-WMMSE is significantly less than WMMSE with negligible performance loss. Meanwhile, inspired by deep learning, a deep neural network named as IM-WMMSE-Net is also designed for further complexity reduction. Simulation results demonstrate that IM-WMMSE is able to effectively reduce the computational complexity while the designed IM-WMMSE-Net achieves high performance with low computational complexity.
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.
