k-NN Based MCS Selection in Distributed OFDM Wireless Networks

2011 ◽  
Vol 225-226 ◽  
pp. 974-977
Author(s):  
Qing Min Meng ◽  
Xiong Gu ◽  
Feng Tian ◽  
Bao Yu Zheng
Keyword(s):  
Wireless Networks ◽  
Cognitive Radio ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Core Component ◽  
Cognitive Engine ◽  
Frequency Selective Fading ◽  
Surrounding Environment ◽  
Rate Selection ◽  
Neighbor Classification

Cognitive radio is seen as an intelligent wireless communication system that can learn and adapt the surrounding environment. Cognitive engine is the core component of implementation of cognitive radio. Information in knowledge base of cognitive engine can be obtained by using of machine learning. In this work, we consider wireless networks with clustered nodes and OFDM physical layer and present a combined sub-channel selection and modulation and coding rate selection based on k-Nearest Neighbor classification algorithm. Computer simulation results show that, in frequency selective fading channel, the scheme makes a new network node easy to choose appropriate modulation and coding rate.

scholarly journals Exclusive lasso-based k-nearest-neighbor classification

2021 ◽  
Author(s):  
Lin Qiu ◽  
Yanpeng Qu ◽  
Changjing Shang ◽  
Longzhi Yang ◽  
Fei Chao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)

2013 ◽  
Vol 3 ◽  
pp. 462-469 ◽  
Author(s):  
Martijn D. Steenwijk ◽  
Petra J.W. Pouwels ◽  
Marita Daams ◽  
Jan Willem van Dalen ◽  
Matthan W.A. Caan ◽  
...  
Keyword(s):  
White Matter ◽  
Nearest Neighbor ◽  
White Matter Lesion ◽  
Tissue Type ◽  
Lesion Segmentation ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Identification of Student Academic Performance using the KNN Algorithm

2020 ◽  
Vol 2 (3) ◽  
pp. 115-122
Author(s):  
Aldi Nugroho ◽  
Osvaldo Richie Riady ◽  
Alexander Calvin ◽  
Derwin Suhartono
Keyword(s):  
Graduation Rates ◽  
Teaching And Learning ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Student Academic Performance ◽  
Education Needs ◽  
Attention To Students ◽  
The University ◽  
Neighbor Classification ◽  
Previous Learning

Students are an important asset in the world of education also an institution and therefore also need to pay attention to students' graduation rates on time. The ups and downs of the percentage of students' abilities in classroom learning is one important element for assessing university accreditation. Therefore, it is necessary to monitor and evaluate teaching and learning activities using the KNN Algorithm classification. By processing student complaints data and seeing the results of previous learning can obtain important things for higher education needs. In predicting graduation rates based on complaints, this study uses the K-Nearest Neighbor classification algorithm by grouping data k = 1, k = 2, k = 3 with the smallest value possible. In experiments using the KNN method the results were clearly visible and showed quite good accuracy. From the experiment it was concluded that if there were fewer complaints from one student it could minimize the level of student non-graduates at the university and ultimately produce good accreditation.

scholarly journals Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

2019 ◽  
Vol 1 (3) ◽  
pp. 1-12
Author(s):  
Agus Wahyu Widodo ◽  
Deo Hernando ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Mangrove Forest ◽  
Nearest Neighbor ◽  
Classification Method ◽  
Mangrove Forests ◽  
K Nearest Neighbor ◽  
Distance Method ◽  
K Value ◽  
Nearest Neighbor Classification ◽  
Haralick Features ◽  
Neighbor Classification

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

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