scholarly journals Online machine learning algorithms to optimize performances of complex wireless communication systems

2021 ◽  
Vol 19 (2) ◽  
pp. 2056-2094
Author(s):  
Koji Oshima ◽  
◽  
Daisuke Yamamoto ◽  
Atsuhiro Yumoto ◽  
Song-Ju Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wireless Communication ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Optimal Decision ◽  
Wireless Communication Systems ◽  
Optimal Decision Making ◽  
Comprehensive Framework

<abstract><p>Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.</p></abstract>

scholarly journals Modulation Recognition of Communication Signal Based on Convolutional Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2302
Author(s):  
Kaiyuan Jiang ◽  
Xvan Qin ◽  
Jiawei Zhang ◽  
Aili Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Recognition Task ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signal

In the noncooperation communication scenario, digital signal modulation recognition will help people to identify the communication targets and have better management over them. To solve problems such as high complexity, low accuracy and cumbersome manual extraction of features by traditional machine learning algorithms, a kind of communication signal modulation recognition model based on convolution neural network (CNN) is proposed. In this paper, a convolution neural network combines bidirectional long short-term memory (BiLSTM) with a symmetrical structure to successively extract the frequency domain features and timing features of signals and then assigns importance weights based on the attention mechanism to complete the recognition task. Seven typical digital modulation schemes including 2ASK, 4ASK, 4FSK, BPSK, QPSK, 8PSK and 64QAM are used in the simulation test, and the results show that, compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR, which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

scholarly journals Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2786
Author(s):  
Vasileios P. Rekkas ◽  
Sotirios Sotiroudis ◽  
Panagiotis Sarigiannidis ◽  
Shaohua Wan ◽  
George K. Karagiannidis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Wireless Communications ◽  
Wireless Communication ◽  
Communication Systems ◽  
State Of The Art ◽  
Wireless Communication Systems ◽  
Future Trends ◽  
Network Applications ◽  
Open Issues

Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Sign in / Sign up

Export Citation Format

Share Document