Applications of Machine Learning in Wireless Communications最新文献

Channel prediction based on machine-learning algorithms 基于机器学习算法的信道预测

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH3

Xue Jiang, Zhimeng Zhong

引用次数: 0

Machine-learning-based perceptual video coding in wireless multimedia communications 无线多媒体通信中基于机器学习的感知视频编码

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH8

Shengxi Li, Mai Xu, Yufan Liu, Z. Ding

引用次数: 0

Machine-learning-based saliency detection and its video decoding application in wireless multimedia communications 基于机器学习的显著性检测及其在无线多媒体通信中的视频解码应用

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH9

Mai Xu, Lai Jiang, Zhiguo Ding

{"title":"Machine-learning-based saliency detection and its video decoding application in wireless multimedia communications","authors":"Mai Xu, Lai Jiang, Zhiguo Ding","doi":"10.1049/PBTE081E_CH9","DOIUrl":"https://doi.org/10.1049/PBTE081E_CH9","url":null,"abstract":"Saliency detection has been widely studied to predict human fixations, with various applications in wireless multimedia communications. For saliency detection, we argue that the state-of-the-art high-efficiency video-coding (HEVC) standard can be used to generate the useful features in compressed domain. Therefore, this chapter proposes to learn the video-saliency model, with regard to HEVC features. First, we establish an eye-tracking database for video-saliency detection. Through the statistical analysis on our eye-tracking database, we find out that human fixations tend to fall into the regions with large-valued HEVC features on splitting depth, bit allocation, and motion vector (MV). In addition, three observations are obtained from the further analysis on our eyetracking database. Accordingly, several features in HEVC domain are proposed on the basis of splitting depth, bit allocation, and MV. Next, a support vector machine (SVM) is learned to integrate those HEVC features together, for video-saliency detection. Since almost all video data are stored in the compressed form, our method is able to avoid both the computational cost on decoding and the storage cost on raw data. More importantly, experimental results show that the proposed method is superior to other state-of-the-art saliency-detection methods, either in compressed or uncompressed domain.","PeriodicalId":358911,"journal":{"name":"Applications of Machine Learning in Wireless Communications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126938487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introduction of machine learning 机器学习简介

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH1

Yangli-ao Geng, Ming Liu, Qingyong Li, R. He

引用次数: 0

Data-driven vehicular mobility modeling and prediction 数据驱动的车辆移动性建模与预测

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH13

Yong Li, Fengli Xu, Manzoor Ahmed

{"title":"Data-driven vehicular mobility modeling and prediction","authors":"Yong Li, Fengli Xu, Manzoor Ahmed","doi":"10.1049/PBTE081E_CH13","DOIUrl":"https://doi.org/10.1049/PBTE081E_CH13","url":null,"abstract":"Vehicular networks have been recently attracting an increasing attention from both the industry and research communities. One of the challenges in this area is the understanding of vehicular mobility and further propose accurate and realistic mobility models to aid the vehicular communication and networks design and evaluation. In this chapter, different from the current works focusing on designing microscopic level models that are describing the individual mobility behaviors, we are exploring the use of open Jackson queuing network frameworks to model the macroscopic level vehicular mobility. The proposed intuitive model can accurately describe the vehicular mobility, and further predict various measures of network-level performance. These measures include the vehicular distribution and vehicular-level performance, such as average sojourn time in each area and the number of sojourned areas in the vehicular networks. Model validation based on two large-scale urban vehicular motion traces reveals that such a simple model can accurately predict a number of system measure concerned with the vehicular network performance. Moreover, we develop two applications to illustrate the proposed model's effectiveness in the analysis of system-level performance and dimensioning of vehicular networks.","PeriodicalId":358911,"journal":{"name":"Applications of Machine Learning in Wireless Communications","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114141910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Q-learning-based power control in small-cell networks 基于q学习的小蜂窝网络功率控制

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH12

Zhicai Zhang, Zhengfu Li, Jianmin Zhang, Haijun Zhang

{"title":"Q-learning-based power control in small-cell networks","authors":"Zhicai Zhang, Zhengfu Li, Jianmin Zhang, Haijun Zhang","doi":"10.1049/PBTE081E_CH12","DOIUrl":"https://doi.org/10.1049/PBTE081E_CH12","url":null,"abstract":"Because of the time-varying nature of wireless channels, it is difficult to guarantee the deterministic quality of service (QoS) in wireless networks. In this chapter, by combining information theory with the effective capacity (EC) principle, the energy-efficiency optimization problem with statistical QoS guarantee is formulated in the uplink of a two-tier femtocell network. To solve the problem, we introduce a Q-learning mechanism based on Stackelberg game framework. The macro users act as leaders and know the emission power strategy of all femtocell users (FUS).The femtocell user is the follower and only communicates with the macrocell base station (MBS) without communicating with other femtocell base stations (FBSs). In Stackelberg game studying procedure, the macro user chooses the transmit power level first according to the best response of the femtocell, and the micro users interact directly with the environment, i.e., leader's transmit power strategies, and find their best responses. Then, the optimization problem is modeled as a noncooperative game, and the existence of Nash equilibriums (NEs) is studied. Finally, in order to improve the self-organizing ability of femtocell, we adopt Q-learning framework based on noncooperative game, in which all the FBS are regarded as agents to achieve power allocation. Numerical results show that the algorithm cannot only meet the delay requirements of delay-sensitive traffic but also has good convergence.","PeriodicalId":358911,"journal":{"name":"Applications of Machine Learning in Wireless Communications","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133432608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signal identification in cognitive radios using machine learning 使用机器学习的认知无线电信号识别

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH5

Jingwen Zhang, Fanggang Wang

引用次数: 1

Machine-learning-based channel estimation 基于机器学习的信道估计

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH4

Yue Zhu, Gongpu Wang, F. Gao

引用次数: 0

Reinforcement-learning-based wireless resource allocation 基于强化学习的无线资源分配

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH11

Rui Wang

{"title":"Reinforcement-learning-based wireless resource allocation","authors":"Rui Wang","doi":"10.1049/PBTE081E_CH11","DOIUrl":"https://doi.org/10.1049/PBTE081E_CH11","url":null,"abstract":"In this chapter, we shall focus on the formulation of radio resource management via Markov decision process (MDP). Convex optimization has been widely used in the RRM within a short-time duration, where the wireless channel is assumed to be quasi-static. These problems are usually referred to as deterministic optimization problems. On the other hand, MDP is an elegant and powerful tool to handle the resource optimization of wireless systems in a longer timescale, where the random transitions of system and channel status are considered.These problems are usually referred to as stochastic optimization problems. Particularly, MDP is suitable for the joint optimization between physical and media-access control (MAC) layers. Based on MDP, reinforcement learning is a practical method to address the optimization without a priori knowledge of system statistics. In this chapter, we shall first introduce some basics on stochastic approximation, which serves as one basis of reinforcement learning, and then demonstrate the MDP formulations of RRM via some case studies, which require the knowledge of system statistics. Finally, some approaches of reinforcement learning (e.g., Q-learning) are introduced to address the practical issue of unknown system statistics.","PeriodicalId":358911,"journal":{"name":"Applications of Machine Learning in Wireless Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130878436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Compressive sensing for wireless sensor networks 无线传感器网络的压缩感知

Applications of Machine Learning in Wireless Communications Pub Date : 2019-06-19 DOI: 10.1049/PBTE081E_CH6

Wei Chen

引用次数: 2