Opportunistic Bandwidth Sharing Through Reinforcement Learning

Venkatraman, Pavithra; Hamdaoui, Bechir; Guizani, Mohsen

doi:10.1109/tvt.2010.2048766

Cited by 61 publications

(27 citation statements)

References 23 publications

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“…RL is an unsupervised and intelligent approach that enables an agent to observe and learn about the static or dynamic operating environment in the absence of guidance, feedback or the expected response from supervisors (or external critics), and subsequently make decisions on action selection in order to achieve optimal or near-optimal system performance. RL has been adopted in the literature [8][9][10][11][12][13][14][15][16] because it does not require prior knowledge of channel availability and it is highly adaptive to the dynamicity of channels characteristics. In addition, it enables decision makers (or agents) to learn and subsequently achieve near-optimal or optimal solutions in the dynamic environment that may be complex and large-scale in nature [7,8,16,17].…”

Section: Q4mentioning

confidence: 99%

Application of reinforcement learning for security enhancement in cognitive radio networks

Ling

Yau

Qadir

et al. 2015

Applied Soft Computing

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Please cite this article in press as: M.H. Ling, et al., Application of reinforcement learning for security enhancement in cognitive radio networks, Appl. Soft Comput. J. (2015), http://dx. a b s t r a c tCognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to sense for and opportunistically operate in underutilized licensed channels, which are owned by the licensed users (or primary users, PUs). Cognitive radio network (CRN) has been regarded as the next-generation wireless network centered on the application of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and dynamically reconfigure its operating parameters, including the sensing and transmission channels, for network performance enhancement. This motivates the use of artificial intelligence to enhance security schemes for CRNs. Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration. In addition these techniques cannot prevent an authenticated node from acting maliciously. In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or near-optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs. RL, which is an artificial intelligence technique, has the ability to learn new attacks and to detect previously learned ones. RL has been perceived as a promising approach to enhance the overall security aspect of CRNs. RL, which has been applied to address the dynamic aspect of security schemes in other wireless networks, such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs. We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge, this is the first survey article that focuses on the use of RL-based techniques for security enhancement in CRNs.

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Section: Q4mentioning

confidence: 99%

Application of reinforcement learning for security enhancement in cognitive radio networks

Ling

Yau

Qadir

et al. 2015

Applied Soft Computing

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“…Reinforcement learning learns a policy from which most reward is obtained through a series of actions [17]. Reinforcement learning is a broad class of optimal control methods depending on estimating value functions from experience or simulations [18][19][20][21].…”

Section: The Reinforcement Learning Ramp Meteringmentioning

confidence: 99%

Reinforcement Learning Ramp Metering without Complete Information

Wang

Gao

2012

Journal of Control Science and Engineering

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This paper develops a model of reinforcement learning ramp metering (RLRM) without complete information, which is applied to alleviate traffic congestions on ramps. RLRM consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theories. Moreover, it is also a dynamic process with abilities of automaticity, memory and performance feedback. Numerical cases are given in this study to demonstrate RLRM such as calculating outflow rate, density, average speed, and travel time compared to no control and fixed-time control. Results indicate that the greater is the inflow, the more is the effect. In addition, the stability of RLRM is better than fixed-time control.

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“…As examples, we can mention [49][50][51] for spectrum sensing, [52,53] for spectrum sharing and [54] for spectrum access and control. We can distinguish two types of learning algorithms generally used in this context: reinforcement learning and Q-learning.…”

Section: Learning Based Approachesmentioning

confidence: 99%

Untitled

2012

IJWMN

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In the last decade, cognitive radio technology received a lot of consideration for spectrum optimization. This issue creates huge opportunities for interesting research and development in a wide range of applications. This paper presents a state of the art on cognitive radio researches especially works using multi-agent systems. We propose among others a classification of cognitive radio proposals based on multi-agent concept and point out the pros and cons for each of the described approach.

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Opportunistic Bandwidth Sharing Through Reinforcement Learning

Cited by 61 publications

References 23 publications

Application of reinforcement learning for security enhancement in cognitive radio networks

Application of reinforcement learning for security enhancement in cognitive radio networks

Reinforcement Learning Ramp Metering without Complete Information

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