Abstract
Recently, various iterative methods are investigated to achieve linear minimum mean square error (MMSE) detection accuracy for uplink massive multiple-input multiple-output (MIMO) systems. This letter introduces the non-stationary Richardson (NSR) iteration to achieve fast convergence rate, and reduces its complexity with approximate eigenvalues in massive MIMO system. However, when the system scale grows and channel correlation is considered, the performance of NSR method decays obviously. To improve the robustness, this letter further proposes a deeply fused SDNSR algorithm, which effectively overcomes the weakness of NSR method by fully utilizing the information obtained through the steepest descent (SD) method and NSR method. Moreover, the complexity is significantly reduced by adopting matrix-vector multiplication and reusing intermediate results. Simulation results and complexity analysis exhibit that the SDNSR method achieves superior performance with lower complexity compared to the recently reported works.
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.
