A Near Optimum Low Complexity Detection Algorithm for MIMO OFDM Channels

2007 ◽  
Author(s):  
Ebrahim Karami ◽  
Markku Juntti
Keyword(s):  
Low Complexity ◽  
Detection Algorithm ◽  
Mimo Ofdm

An Improved MIMO-OFDM Detection Algorithm Based on OSIC

2014 ◽  
Vol 696 ◽  
pp. 201-206
Author(s):  
Da Jiang Yang ◽  
Zi Fa Zhong
Keyword(s):  
Signal Detection ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Optimal Detection ◽  
Ofdm Signal ◽  
Mimo Ofdm ◽  
Ml Detection ◽  
Improved Algorithm

This paper proposes a MIMO-OFDM signal detection algorithm with joint ML and MMSE-OSIC based on researches of ML algorithm and MMSE-OSIC algorithm. This kind of algorithm is an improved algorithm of MMSE-OSIC. Comparing to the traditional MMSE-OSIC algorithm, this algorithm uses ML detection on the relatively weaker signal layer. According to the experiment, it was found close to the optimal detection performance, much less complicated than the ML algorithm, which is a near-optimal and low-complexity MIMO-OFDM detection algorithm.

Low-Complexity Soft-ML Detection Algorithm for Modified-DCM in WiMedia UWB Systems

2013 ◽  
Vol E96.B (3) ◽  
pp. 910-913 ◽  
Author(s):  
Kilhwan KIM ◽  
Jangyong PARK ◽  
Jihun KOO ◽  
Yongsuk KIM ◽  
Jaeseok KIM
Keyword(s):  
Low Complexity ◽  
Detection Algorithm ◽  
Ml Detection

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

Low-complexity FFT/IFFT IP hardware macrocells for OFDM and MIMO–OFDM CMOS transceivers

2009 ◽  
Vol 33 (3) ◽  
pp. 191-200 ◽  
Author(s):  
Sergio Saponara ◽  
Nicola E. L’Insalata ◽  
Luca Fanucci
Keyword(s):  
Low Complexity ◽  
Mimo Ofdm ◽  
Cmos Transceivers
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