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Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Nadine Abbas, Youssef Nasser, Karim El Ahmad
2015 EURASIP Journal on Wireless Communications and Networking  
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

Le Liang, Hao Ye, Geoffrey Ye Li
2019 IEEE Internet of Things Journal  
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]

Nicola Bui, Matteo Cesana, S. Amir Hosseini, Qi Liao, Ilaria Malanchini, Joerg Widmer
2017 arXiv   pre-print
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

Azin Dastpak, Jiangchuan Liu, Mohamed Hefeeda
2012 Proceedings of the 4th Workshop on Mobile Video - MoVid '12  
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

Alexandros-Apostolos A. Boulogeorgos, Edwin Yaqub, Marco di Renzo, Angeliki Alexiou, Rachana Desai, Ralf Klinkenberg
2021 Frontiers in Communications and Networks  
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

Dragana Krstić, Nenad Petrović, Issam Al-Azzoni, Ali Ahmadian
2022 Mathematical Problems in Engineering  
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

Carolina Fortuna, Mihael Mohorcic
2009 Computer Networks  
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

Julien Herzen, Henrik Lundgren, Nidhi Hegde
2015 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)  
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

Nicola Bui, Matteo Cesana, S. Amir Hosseini, Qi Liao, Ilaria Malanchini, Joerg Widmer
2017 IEEE Communications Surveys and Tutorials  
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]

Mahnoor Anjum, Muhammad Abdullah Khan, Syed Ali Hassan, Aamir Mahmood, Hassaan Khaliq Qureshi, Mikael Gidlund
2020 arXiv   pre-print
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

Marco Di Renzo, Xuewen Qian, Halid Hrasnica, Nikos Katzouris, Kyriakos Manganaris, Dimitris Selimis, Fotis Lazarakis, Tachporn Sanguanpuak, Heikki Halmetoja, Moamen Ibrahim, Edwin Yaqub, Rachana Desai (+5 others)
2021 Zenodo  
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]

Mohamed-Amine Lahmeri, Mustafa A.Kishk, Mohamed-Slim Alouini
2021 arXiv   pre-print
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

Lijun Qian, Jinkang Zhu, Sihai Zhang
2017 Journal of Communications and Information Networks  
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

Mohamed-Amine Lahmeri, Mustafa A. Kishk, Mohamed-Slim Alouini
2021 IEEE Open Journal of the Communications Society  
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

S. Navaneethan
2013 IOSR Journal of Computer Engineering  
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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