Learning automata based multipath multicasting in cognitive radio networks

Ali, Asad; Qadir, Junaid; Baig, Adeel

doi:10.1109/jcn.2015.000071

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…The paths with less closeness are preferred because such paths are less prone to the PU activities due to the greater distance among these paths. In another interesting CRN-based work [125], contrary to most of the multipath routing works presented in this paper (that have addressed unicast routing), Ali et al have proposed a multipath-based approach to multicast routing in CRNs. The proposed approach makes use of learning automata to select active and backup paths to develop multicast routing protocols that is minimally disrupted by PU activity.…”

Section: Hop-by-hop Multipath Protocols For Crnsmentioning

confidence: 99%

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Exploiting the Power of Multiplicity: A Holistic Survey of Network-Layer Multipath

Qadir

Ali

Yau

et al. 2015

IEEE Commun. Surv. Tutorials

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Abstract-The Internet is inherently a multipath network-for an underlying network with only a single path connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity-through which a diverse collection of paths is resource pooled as a single resource-to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault-tolerance (through the use of multiple paths in backup/ redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be unmistakably multipath, including the use of multipath technology in datacenter computing; multi-interface, multi-channel, and multi-antenna trends in wireless; ubiquity of mobile devices that are multi-homed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as MP-TCP.The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely the control plane problem of how to compute and select the routes, and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work.

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Section: Hop-by-hop Multipath Protocols For Crnsmentioning

confidence: 99%

“…Ali et al [125] 2015 Hop-by-Hop Backup Reliability Proposed a learning automata based multipathing solution for multicast routing in CRNs. Vehicular Ad-hoc Networks (VANETs) Huang et al [75] 2009 Hop-by-Hop Concurrent Delay, Interference, Reliability…”

Section: Wireless Mesh Network (Wmns)mentioning

confidence: 99%

Exploiting the Power of Multiplicity: A Holistic Survey of Network-Layer Multipath

Qadir

Ali

Yau

et al. 2015

IEEE Commun. Surv. Tutorials

Self Cite

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“…For non-gamebased wireless networking problems, (distributed) LA has been shown to be efficient in the scenarios which can be formulated to be of single state and controlled by a single active decision-making entity at one time instance. Successful applications of LA in these scenarios can be found in the works such as multipath on-demand multicast routing in CRNs [150] and multicast routing in mobile ad-hoc networks [151]. When it comes to the more complicated framework of network control games, most of the LA-based learning schemes are employed to obtain NE policies.…”

Section: Theorem 2 (Convergence Of Fp) 1) Strict Ne 8 Are the Absorbi...mentioning

confidence: 99%

A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks

Wang

Kwasinski

Niyato

et al. 2016

IEEE Commun. Surv. Tutorials

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Abstract-The framework of cognitive wireless radio is expected to endow the wireless devices with the cognitionintelligence ability, with which they can efficiently learn and respond to the dynamic wireless environment. In many practical scenarios, the complexity of network dynamics makes it difficult to determine the network evolution model in advance. As a result, the wireless decision-making entities may face a black-box network control problem and the model-based network management mechanisms will be no longer applicable. In contrast, model-free learning has been considered as an efficient tool for designing control mechanisms when the model of the system environment or the interaction between the decision-making entities is not available as a-priori knowledge. With model-free learning, the decision-making entities adapt their behaviors based on the reinforcement from their interaction with the environment and are able to (implicitly) build the understanding of the system through trial-and-error mechanisms. Such characteristics of model-free learning is highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Recently, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the stateof-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models that those applications are based on, a systematic overview of the learning algorithms in the domains of single-agent system, multi-agent systems and multi-player games is provided. Furthermore, the applications of model-free learning to various problems in cognitive wireless networks are discussed with the focus on how the learning mechanisms help to provide the solutions to these problems and improve the network performance over the existing model-based, non-adaptive methods. Finally, a broad spectrum of challenges and open issues is discussed to offer a guideline for the future research directions.

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“…Thus, repeating of the process increases the possibility of selecting the ideal action. LA is used in several areas including wireless sensor networks, online social networks, resources allocation, pattern classification, signal processing, and in some other troublesome areas of wireless communication such as those in the literatures …”

Section: Introductionmentioning

confidence: 99%

“…LA is used in several areas including wireless sensor networks, 24 online social networks, 25 resources allocation, 26 pattern classification, 27 signal processing, 28 and in some other troublesome areas of wireless communication such as those in the literatures. [29][30][31][32] Optimization problems become increasingly complex in the practical scenarios and real communication systems. On the other hand, time and energy limitations demand better optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Channel estimation based on learning automata for OFDM systems

Salehi

Majidi

Neda

2018

Int J Communication

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Summary The abundant benefits of Orthogonal Frequency‐Division Multiplexing and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system's efficiency is the accurate estimation of channel state information. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response properly only using the received signals. Unfortunately, each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate, our proposed method is capable of providing the same performance as GA and PSO. For a 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error.

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Learning automata based multipath multicasting in cognitive radio networks

Cited by 9 publications

References 40 publications

Exploiting the Power of Multiplicity: A Holistic Survey of Network-Layer Multipath

Exploiting the Power of Multiplicity: A Holistic Survey of Network-Layer Multipath

A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks

Channel estimation based on learning automata for OFDM systems

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