Distributed Stochastic Learning and Adaptation to Primary Traffic for Dynamic Spectrum Access

Zandi, Marjan; Dong, Min; Grami, Ali

doi:10.1109/twc.2015.2495154

Cited by 31 publications

(14 citation statements)

References 33 publications

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“…throughput loss). These algorithms have been selected as they have shown to outperform DMC [11] and algorithms such as [7][8][9][10] and hence, we don't include the rest to maintain the clarity of plots. All the results presented here are obtained after averaging over 50 experiments and each experiment consists of the horizon of 10 5 time slots.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Various distributed algorithms have been proposed to facilitate learning and coordination tasks in static networks where the network parameters do not change with time [3][4][5][6][7][8][9]. The musical chair (MC) based MCTopM algorithm in [3] is the current state-of-the-art algorithm for static networks but assumes prior knowledge of the number of users, N which is not practical for ad-hoc networks.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Algorithm for Dynamic Cognitive Ad-hoc Networks

Kumar¹,

Satapathy²,

Singh³

et al. 2020

Preprint

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Cognitive ad-hoc networks allow users to access an unlicensed/shared spectrum without the need for any coordination via a central controller and are being envisioned for futuristic ultra-dense wireless networks. The ad-hoc nature of networks require each user to learn and regularly update various network parameters such as channel quality and the number of users, and use learned information to improve the spectrum utilization and minimize collisions. For such a learning and coordination task, we propose a distributed algorithm based on a multi-player multi-armed bandit approach and novel signaling scheme. The proposed algorithm does not need prior knowledge of network parameters (users, channels) and its ability to detect as well as adapt to the changes in the network parameters thereby making it suitable for static as well as dynamic networks. The theoretical analysis and extensive simulation results validate the superiority of the proposed algorithm over existing state-of-the-art algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Algorithm for Dynamic Cognitive Ad-hoc Networks

Kumar¹,

Satapathy²,

Singh³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The modified ρ EST algorithm overcomes latter issue, but its guarantees holds only asymptotically. Other set of works in [15], [16] considers selfish behavior of players and analyze their equilibrium behavior. However, all these algorithms work only for the static case and cannot extend to the dynamic scenarios which is the focus of this work.…”

Section: A Related Workmentioning

confidence: 99%

Multiplayer Bandits: A Trekking Approach

Hanawal

Darak

2022

IEEE Trans. Automat. Contr.

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We study stochastic multi-armed bandits with many players. The players do not know the number of players, cannot communicate with each other and if multiple players select a common arm they collide and none of them receive any reward. We consider the static scenario, where the number of players remains fixed, and the dynamic scenario, where the players enter and leave at any time. We provide algorithms based on a novel 'trekking approach' that guarantees constant regret for the static case and sub-linear regret for the dynamic case with high probability. The trekking approach eliminates the need to estimate the number of players resulting in fewer collisions and improved regret performance compared to the state-of-theart algorithms. We also develop an epoch-less algorithm that eliminates any requirement of time synchronization across the players provided each player can detect the presence of other players on an arm. We validate our theoretical guarantees using simulation based and real test-bed based experiments.

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“…This process is assumed to be independently and identically distributed across time slots and unknown to the SUs and the jammers. The same network model is considered for the study of non-cooperative CRN in several works including [5], [11], [10] [13]. Other important aspects of networking like, rendezvous, error-free sensing, time-synchronization are important but not considered here to keep the focus of the paper on the learning aspects as done in other papers [5], [11], [10] [13].…”

Section: Network Modelmentioning

confidence: 99%

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

Sawant

Kumar

Hanawal

et al. 2020

IEEE Trans. on Mobile Comput.

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Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability.We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiment. IndexTerms-Cognitive radio network, jamming attack, distributed learning ! PLACE PHOTO HERE Sunnet Sawant received his B.Tech and M.Tech. dual degree in Electrical Engineering from IIT Bombay in 2017. His research interests include communication networks PLACE PHOTO HERE Rohit Kumar received his B.

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Distributed Stochastic Learning and Adaptation to Primary Traffic for Dynamic Spectrum Access

Cited by 31 publications

References 33 publications

Distributed Algorithm for Dynamic Cognitive Ad-hoc Networks

Distributed Algorithm for Dynamic Cognitive Ad-hoc Networks

Multiplayer Bandits: A Trekking Approach

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

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