scholarly journals Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning

2021 ◽  
Author(s):  
Evgeny Bobrov ◽  
Dmitry Kropotov ◽  
Hao Lu ◽  
Danila Zaev
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Learning Algorithm ◽  
Adaptive Modulation ◽  
System Level ◽  
Adaptive Modulation And Coding ◽  
Q Learning ◽  
Deep Learning Algorithm ◽  
System Level Simulation ◽  
Fully Connected

The paper describes an online deep learning algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-Learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA our algorithm shows 10% to 20% improvement of user throughput in full buffer case. <br>

scholarly journals Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning

2021 ◽  
Author(s):  
Evgeny Bobrov ◽  
Dmitry Kropotov ◽  
Hao Lu ◽  
Danila Zaev
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Learning Algorithm ◽  
Adaptive Modulation ◽  
System Level ◽  
Adaptive Modulation And Coding ◽  
Q Learning ◽  
Deep Learning Algorithm ◽  
System Level Simulation ◽  
Fully Connected

The paper describes an online deep learning algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-Learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA our algorithm shows 10% to 20% improvement of user throughput in full buffer case. <br>

scholarly journals Faultless Decision Making for False Information in Online: A Systematic Approach

2021 ◽  
Vol 2 (4) ◽  
pp. 226-235
Author(s):  
Yasir Babiker Hamdan ◽  
Sathish
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Systematic Approach ◽  
Learning Algorithm ◽  
Research Work ◽  
High Accuracy ◽  
Fake News ◽  
False Information ◽  
Q Learning ◽  
Deep Learning Algorithm

An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation of real and fake news rather than other methods. This research work constructs naïve bayes and CNN classifiers with Q-learning decision making. The two different approaches detect fake news in online and it gives to decision making section which is designed at tail in our research. The deep decision making section compares the input and make the decision wisely and it provides the more accurate output rather than single classifiers in deep learning. This research work comprises compare between our proposed works with single classifiers.

A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction

Landslides ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 217-229 ◽  
Author(s):  
Faming Huang ◽  
Jing Zhang ◽  
Chuangbing Zhou ◽  
Yuhao Wang ◽  
Jinsong Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Sparse Autoencoder ◽  
Fully Connected
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