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Recent advances on artificial intelligence and learning techniques in cognitive radio networks
2015
EURASIP Journal on Wireless Communications and Networking
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Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. ...
, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. ...
The authors in [86] used the Markov decision process for dynamic spectrum access in cognitive networks. They used the HMM to model a wireless channel and predict the channel state. ...
doi:10.1186/s13638-015-0381-7
fatcat:dq6aba75obc5vlxnbaqrerlsii
Toward Intelligent Vehicular Networks: A Machine Learning Framework
2019
IEEE Internet of Things Journal
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Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. ...
In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. ...
Tasks Learning Methods References Hidden Markov models [67] Location prediction based scheduling and routing Variable-order Markov models [68] Recursive least squares [69] Network congestion control k-means ...
doi:10.1109/jiot.2018.2872122
fatcat:n25uma5isfduvk3hh5mvnai4fy
A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
[article]
2017
arXiv
pre-print
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This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. ...
This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. ...
For continuous target variables, regression trees can be used to learn trends in network performance [98] .
C. ...
arXiv:1606.00191v3
fatcat:me4ufu7gsjcmtcrs3m6g4jf2am
Video streaming over cognitive radio networks
2012
Proceedings of the 4th Workshop on Mobile Video - MoVid '12
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In our framework, we introduce a channel usage model based on a two-state Markov model and estimate the future busy and idle durations of the spectrum based on past observations. ...
In recent years, there has been a tremendous growth in multimedia applications over the wireless Internet. ...
In our formulation, we model the occupancy pattern of secondary channel as a two-sate Markov model . ...
doi:10.1145/2151677.2151685
fatcat:jqklvpfl2bgmrk3xzjqudvlpuy
Machine Learning: A Catalyst for THz Wireless Networks
2021
Frontiers in Communications and Networks
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With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry ...
turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. ...
AUTHOR CONTRIBUTIONS A-AAB envisioned the concept of the paper and prepared the initial draft. EY prepared Section 4 and reviewed the paper. MR, AA, RD, and RK performed internal reviews. ...
doi:10.3389/frcmn.2021.704546
fatcat:c7okz673z5cibo54yga7a2eoiq
Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning
2022
Mathematical Problems in Engineering
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In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. ...
The main contributions of the paper are (1) wireless network planning (WNP) metamodel, a modelling notation for network plans; (2) model-to-model transformation for conversion of WNP to generalized CAP ...
a Markov model for fading channels are obtained based on LCR at different levels [80] . e expressions for the LCR and average duration of fades (AFD) are derived in [37] for different RF V2I parameters ...
doi:10.1155/2022/4140522
fatcat:izi3xaojr5eupfbhizlxrahube
Trends in the development of communication networks: Cognitive networks
2009
Computer Networks
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The recently proposed concept of cognitive network appears as a candidate that can address this issue. ...
We start with identifying the most recent research trends in communication networks and classifying them according to the approach taken towards the traditional layered architecture. ...
An approach for modeling infrastructure wireless networks with the scope of resource management can be found in [24] . ...
doi:10.1016/j.comnet.2009.01.002
fatcat:kabc6k5spnghhmbbpic4r6zumy
Learning Wi-Fi performance
2015
2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
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In this paper, we advocate an approach of "learning by observation" that can remove the need for designing explicit and complex performance models. ...
To demonstrate that learned models can be useful in practice, we build a new algorithm that uses such a model as an oracle to jointly allocate spectrum and transmit power. ...
Finally, a few papers propose to use machine learning techniques in the context of wireless networks. ...
doi:10.1109/sahcn.2015.7338298
dblp:conf/secon/HerzenLH15
fatcat:fpxs4ypzwnf5rn5jynvcel6mjy
A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
2017
IEEE Communications Surveys and Tutorials
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This 7 survey collects and analyzes recent papers leveraging context 8 information to forecast the evolution of network conditions and, 9 in turn, to improve network performance. ...
In particular, we iden-10 tify the main prediction and optimization tools adopted in this 11 body of work and link them with objectives and constraints of the 12 typical applications and scenarios. ...
For con-
1421
tinuous target variables, regression trees can be used to learn
1422
trends in network performance [98].
1423
C. ...
doi:10.1109/comst.2017.2694140
fatcat:xdpdceqnwfgprdlpmbeuhufl4u
RSSI Fingerprinting-based Localization Using Machine Learning in LoRa Networks
[article]
2020
arXiv
pre-print
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RSSI)-based ranging in LoRa networks, on a training dataset collected in two different environments: indoors and outdoors. ...
This article explores the use of intelligent machine learning techniques, such as support vector machines, spline models, decision trees, and ensemble learning, for received signal strength indicator ( ...
Thus, we studied various regression and machine learning models to accommodate the variability of the ranging function. 1) Regression Models: To find a regression-based ranging function, we used linear ...
arXiv:2006.01278v1
fatcat:dgl7pce4c5c4fb5gthzhynugmq
Intelligent D-Band wireless systems and networks initial designs
2021
Zenodo
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system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions ...
This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. ...
Therefore, different from pure data-driven and conventional model-based approaches, model-driven deep learning (MDDL)-based approaches construct the network structure by exploiting a priori knowledge from ...
doi:10.5281/zenodo.5718378
fatcat:l3rkacgotzazha564nqm6wchiy
Artificial Intelligence for UAV-enabled Wireless Networks: A Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2009.11522v1 fatcat:ve2eaqhqtvatpd527wxb3np5ly
In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. ...
As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. ...
RL solutions for UAV-Based problems 1) RL for Autonomous navigation : A network of UAVs can no longer be controlled in a classical way by manually controlling the navigation of each UAV from the network ...
arXiv:2009.11522v2
fatcat:qantzkixtjcq3in27hgiagiaye
Survey of wireless big data
2017
Journal of Communications and Information Networks
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Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. ...
This article reviews the recent advances and trends in the field of wireless big data. ...
A series of MC (Markov Chain) based models were then implemented to predict the actual locations visited by each user. ...
doi:10.1007/s41650-017-0001-2
fatcat:62r2dkm4inesrg3j3r3f3obgxm
Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey
2021
IEEE Open Journal of the Communications Society
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In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. ...
As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. ...
RL Solutions for UAV-based Problems 1) RL for autonomous navigation A network of UAVs can no longer be controlled in a classical way by manually controlling the navigation of each UAV from the network ...
doi:10.1109/ojcoms.2021.3075201
fatcat:4q6cl2sz7nha5mijm6fmomv3fi
Detecting Intruders and Packet Modifiers in Wireless Sensor Networks
2013
IOSR Journal of Computer Engineering
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any number of packets simultaneously. ...
The basic scheme (MABS-B) eliminates packet loss and also efficient in terms of latency computation and communication overhead due to effective cryptographic primitive called batch signature which authenticates ...
Finally, the authors would like to thank everybody who were important and helped them out in every process to the successful realization of the thesis. ...
doi:10.9790/0661-0850103
fatcat:ju24fypkjvgdzcg3ajc6hkto4u
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