scholarly journals Toward More Reliable Deep Learning-Based Link Adaptation for WiFi 6

2020 ◽  
Author(s):  
Mostafa Hussien
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Adaptive Modulation ◽  
Selection Criterion ◽  
Link Adaptation ◽  
Classification Problem ◽  
System Throughput ◽  
Adaptive Modulation And Coding ◽  
Proposed Model ◽  
Multi Class Classification

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in model failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, retransmission is required, which largely degrades the system throughput. To address this issue, we formulate the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed formulation allows more control over what the model predicts in failure cases. In this context, we propose a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset is generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. It is shown that some criteria for dataset selection consistently behave better than others. To confirm the performance, we applied the proposed model for adapting the IEEE 802.11ax standard in outdoor propagation scenarios. The simulation results show that the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions. Finally, we propose an optimal subdataset selection criterion.

scholarly journals Toward More Reliable Deep Learning-Based Link Adaptation for WiFi 6

2020 ◽  
Author(s):  
Mostafa Hussien
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Adaptive Modulation ◽  
Selection Criterion ◽  
Link Adaptation ◽  
Classification Problem ◽  
System Throughput ◽  
Adaptive Modulation And Coding ◽  
Proposed Model ◽  
Multi Class Classification

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in model failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, retransmission is required, which largely degrades the system throughput. To address this issue, we formulate the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed formulation allows more control over what the model predicts in failure cases. In this context, we propose a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset is generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. It is shown that some criteria for dataset selection consistently behave better than others. To confirm the performance, we applied the proposed model for adapting the IEEE 802.11ax standard in outdoor propagation scenarios. The simulation results show that the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions. Finally, we propose an optimal subdataset selection criterion.

scholarly journals Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6

2021 ◽  
Author(s):  
Mostafa Hussien
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Adaptive Modulation ◽  
Link Adaptation ◽  
Classification Problem ◽  
System Throughput ◽  
Adaptive Modulation And Coding ◽  
Coding Scheme ◽  
Proposed Model ◽  
Multi Class Classification

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, a retransmission is required, which largely degrades the system throughput. To address this issue, we model the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed modeling allows more control over what the model predicts in failure cases. We also design a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset has been generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. The proposed model adapts the IEEE 802.11ax communications standard in outdoor scenarios. The simulation results show the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions.

scholarly journals Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2215
Author(s):  
Jung-Kai Tsai ◽  
Chih-Hsing Hung
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Classification Problem ◽  
Back Propagation Neural Network ◽  
Future Research ◽  
Support Vector ◽  
Enterprise Performance ◽  
Classification Problems ◽  
Proposed Model ◽  
Multi Class Classification

Because COVID-19 occurred in 2019, the behavioxr of humans has been changed and it will influence the business model of enterprise. Enterprise cannot predict its development according to past knowledge and experiment; so, it needs a new machine learning framework to predict enterprise performance. The goal of this research is to modify AdaBoost to reasonably predict the enterprise performance. In order to justify the usefulness of the proposed model, enterprise data will be collected and the proposed model can be used to predict the enterprise performance after COVID-19. The test data correct rate of the proposed model will be compared with some of the traditional machine learning models. Compared with the traditional AdaBoost, back propagation neural network (BPNN), regression classifier, support vector machine (SVM) and support vector regression (SVR), the proposed method possesses the better classification ability (average correct rate of the proposed method is 88.04%) in handling two classification problems. Compared with traditional AdaBoost, one-against-all SVM, one-against-one SVM, one-against-all SVR and one-against-one SVR, the classification ability of the proposed method is also relatively better for coping with the multi-class classification problem. Finally, some conclusions and future research will be discussed at the end.

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 Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Dongyul Lee ◽  
Chaewoo Lee
Keyword(s):  
Resource Allocation ◽  
Video Streaming ◽  
Adaptive Modulation ◽  
Scalable Video Coding ◽  
Video Quality ◽  
System Throughput ◽  
Adaptive Modulation And Coding ◽  
Scalable Video ◽  
Wireless Multimedia ◽  
Channel Conditions

The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm.

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