A dynamic MIMO detector algorithm based on channel analysis

Yu, Nie; Ye, Fan; Ren, Junyan

doi:10.1109/iccps.2011.6089781

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…In 2011, Yu et al (2011) evaluated the performance of the signal detection based on the channel gain effect in MIMO and generated an intelligent MIMO signal detection algorithm. This algorithm selected the number of the child-node on the basis of the antenna gain.…”

Section: Methodsmentioning

confidence: 99%

Review on various detectors in massive MIMO technology: a performance analysis

Manju

Ganesh

2021

IJPCC

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Purpose Multiple-input multiple-output (MIMO) combined with multi-user massive MIMO has been a well-known approach for high spectral efficiency in wideband systems, and it was targeted to detect the MIMO signals. The increasing data rates with multiple antennas and multiple users that share the communication channel simultaneously lead to higher capacity requirements and increased complexity. Thus, different detection algorithms were developed for the Massive MIMO. Design/methodology/approach This paper focuses on the various literature analyzes on various detection algorithms and techniques for MIMO detectors. Here, it reviews several research papers and exhibits the significance of each detection method. Findings This paper provides the details of the performance analysis of the MIMO detectors and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be useful for future researchers to be accomplished in MIMO massive detectors Originality/value This paper has presented a detailed review of the detection of massive MIMO on different algorithms and techniques. The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa. The performance measures and the best performance of each of the detectors are described.

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Section: Methodsmentioning

confidence: 99%