A Neural Network Prediction-Based Adaptive Mode Selection Scheme in Full-Duplex Cognitive Networks

Zhang, Yirun; Hou, Jiancao; Towhidlou, Vahid; Shikh-Bahaei, Mohammad

doi:10.1109/tccn.2019.2911005

Cited by 26 publications

(11 citation statements)

References 34 publications

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“…In [205], a Deep Q-Network (DQN) is proposed to predict and select a free channel for WSNs. In [208], the authors design a NN predictor to predict PUs future activity based on past channel occupancy sensing results, with the goal of improving secondary users (SUs) throughput while alleviating collision to primary user (PU) in full-duplex (FD) cognitive networks.…”

Section: Medium Access Control (Mac) Analysismentioning

confidence: 99%

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A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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Section: Medium Access Control (Mac) Analysismentioning

confidence: 99%

“…• MAC identification [194][195][196][197][198][199] • Wireless interference detection at packet level [200][201][202][203] • Spectrum prediction [204][205][206][207][208][209] Network prediction…”

mentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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“…• MAC identification [187][188][189][190] • Wireless interference identification [191][192][193][194] • Spectrum prediction [195][196][197][198][199][200] Network prediction…”

Section: Mac Analysismentioning

confidence: 99%

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

Kulin¹,

Kazaz²,

Moerman³

et al. 2020

Preprint

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This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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“…e following is the use of neural network technology to solve the problem [8,9]. Geoffrey Hinton obtained some surprising results on MNIST and showed that the acoustic model of a frequently used commercial system can be significantly improved by distilling the knowledge in the model set [10,11]. Yang Shen's program has been trained to extract trunk.…”

Section: Introductionmentioning

confidence: 99%

The Technique of English Word Syllable Division in Speech Synthesis Based on Neural Network

2021

Mathematical Problems in Engineering

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With the increasing maturity of speech synthesis technology, on the one hand, it has been more and more widely used in people’s lives; on the other hand, it also brings more and more convenience to people. The requirements for speech synthesis systems are getting higher and higher. Therefore, advanced technology is used to improve and update the accent recognition system. This paper mainly introduces the word stress annotation technology combined with neural network speech synthesis technology. In Chinese speech synthesis, prosodic structure prediction has a great influence on naturalness. The purpose of this paper is to accurately predict the prosodic structure, which has become an important problem to be solved in speech synthesis. Experimental data show that the average error of samples in the network training process is lel/85, and the minimum value of the training error after 500 steps is 0.00013127, so the final sample average error is lel = 85 ∗ 0.0013127 = 0.112 < 0.5, and use the deep neural network (DNN) to train different parameters to obtain the conversion model, and then synthesize these conversion models, and finally achieve the effect of improving the synthesized sound quality.

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A Neural Network Prediction-Based Adaptive Mode Selection Scheme in Full-Duplex Cognitive Networks

Cited by 26 publications

References 34 publications

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

The Technique of English Word Syllable Division in Speech Synthesis Based on Neural Network

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