Abstract
In this paper, we propose a hybrid maximum likelihood (ML) decoding scheme for multiple-input multiple-output(MIMO) systems. After partitioning the searching tree into several stages, the proposed scheme adopts thecombination of depth- and breadth-first search methods in an organized way. Taking the number of stages, the size ofsignal constellation, and the number of antennas as the parameter of the scheme, we provide extensive simulationresults for various MIMO communication conditions. Numerical results indicate that, when the depth- and breadth-firstsearch methods are employed appropriately, the proposed scheme exhibits substantially lower computationalcomplexity than conventional ML decoders while maintaining the ML bit error performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.