Distributed Stochastic Online Learning Policies for Opportunistic Spectrum Access

Gai, Yi; Krishnamachari, Bhaskar

doi:10.1109/tsp.2014.2360821

Cited by 74 publications

(71 citation statements)

References 17 publications

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“…Anandkumar et al [8] proposed a distributed algorithm named ρ PRE policy based on the ε n -greedy policy [10]. Gai et al [9] proposed a SL(K) subroutine and then established the prioritized access policy (DLP) and fair access policy (DLF) based on SL(K) and a pre-allocation order. Chen et al [11] then proposed kth-UCB1 policy combined the ε n -greedy and UCB1 policy and evaluated its performance both for real-time applications and best-effort applications.…”

Section: System Modelmentioning

confidence: 99%

“…Due to the spectrum varying nature of CRN, SUs are required to perform proactive spectrum handoffs when the spectrum band is occupied by PUs in the MAB framework, which results in a handoff delay consisting of RF reconfiguration or negotiation between transceiver In the above work [9][10][11], the handoff delay is not taken into consideration in their paper, i.e., the spectrum handoff is assumed to be costless. In this paper, by including a fixed handoff delay, SUs have to make the choice of either staying foregoing spectrum with low availability or handing off to a spectrum with higher availability and tolerating the handoff delay.…”

Section: System Modelmentioning

confidence: 99%

“…Blind spectrum selection in decentralized cognitive radio networks can be formulated as a decentralized MAB problem for multiple distributed SUs [8,9,[12][13][14], and in this paper the terms "channel" and "arm" are used interchangeably. Denote π j as the decentralized policy for SU j and ,1 π {π } j j M ≤ ≤ = as the set of homogeneous policies of all users.…”

Section: Problem Formulation and Policiesmentioning

confidence: 99%

“…In a decentralized CRN, prior knowledge of spectrum statistical information maybe not acquirable; in this context, many scholars have developed the multi-armed bandit (MAB) framework for opportunistic spectrum access (OSA) of CRN [6][7][8][9]. Anandkumar et al [8] proposed a distributed algorithm named PRE policy based on the ε n -greedy policy [10].…”

Section: Introductionmentioning

confidence: 99%

“…The rest of this paper is organized as follows: Section 2 describes the system model, which is similar to related works [9,12] except that the handoff delay is included as a handoff cost. In Section 3, we formulate the problem and present the three policies.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Blind Spectrum Selection in Cognitive Radio Networks Considering Handoff Cost

et al. 2017

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Due to the spectrum varying nature of cognitive radio networks, secondary users are required to perform spectrum handoffs when the spectrum is occupied by primary users, which will lead to a handoff delay. In this paper, based on the multi-armed bandit framework of medium access in decentralized cognitive radio networks, we investigate blind spectrum selection problem of secondary users whose sensing ability of cognitive radio is limited and the channel statistics are a priori unknown, taking the handoff delay as a fixed handoff cost into consideration. In this scenario, secondary users have to make the choice of either staying foregoing spectrum with low availability or handing off to another spectrum with higher availability. We model the problem and investigate the performance of three representative policies, i.e., PRE , SL(K), kth-UCB1. The simulation results show that, despite the inclusion of the fixed handoff cost, these policies achieve the same asymptotic performance as that without handoff cost. Moreover, through comparison of these policies, we found the kth-UCB1 policy has better overall performance.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Problem Formulation and Policiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decentralized Blind Spectrum Selection in Cognitive Radio Networks Considering Handoff Cost

et al. 2017

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MABO‐TSCH: Multihop and blacklist‐based optimized time synchronized channel hopping

Gomes

Watteyne

Krishnamachari

2017

Trans Emerging Tel Tech

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Emerging Industrial Internet of Things applications, such as smart factories, require reliable communication and robustness against interference from colocated wireless systems. To address these challenges, frequency‐hopping spread spectrum has been used by different protocols, including IEEE802.15.4‐2015 TSCH. Frequency‐hopping spread spectrum can be improved with the aid of blacklists to avoid bad frequencies. The quality of channels in most environments shows significant spatial‐temporal variation, which limits the effectiveness of simple blacklisting schemes. In this article, we propose an enhanced blacklisting solution to improve the TSCH protocol. The proposed algorithms work in a distributed fashion, where each pair of receiver/transmitter nodes negotiates a local blacklist, based on the estimation of packet delivery ratio. We model the channel quality estimation as a multiarmed bandit problem and show that it is possible to create blacklists that provide results close to optimal without any separate learning phase. The proposed algorithms are implemented in OpenWSN and evaluated through simulations in 2 different scenarios with about 40 motes and experiments using an indoor testbed with 40 TelosB motes.

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TAMU‐RPL: Thompson sampling‐based multichannel RPL

Gomes

Krishnamachari

2019

Trans Emerging Tel Tech

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For the success of critical applications in the IoT, there is a need to counteract the effects of external interference, especially when the unlicensed spectrum is used. One way of improving the performance is with a routing protocol that quickly reacts to changes in the environment and avoids path and/or frequencies with higher interference. In this paper, we propose an optimization to the RPL protocol, called TAMU‐RPL, that can keep a more accurate estimation of link quality to the neighbors and quickly react to degradation on the links. TAMU‐RPL also uses the quality of links at different frequencies and opportunistically avoids the ones with bad quality. It is evaluated through simulations with connectivity traces from a 40‐node test bed and in a real 5‐node deployment. We compare TAMU‐RPL with a baseline RPL protocol and a Dijkstra‐based shortest‐path algorithm. Results show that TAMU‐RPL can successfully explore the neighbors and obtain ETX values much closer to the ones obtained with a shortest‐path tree, even when link quality changes over time. In the simulation evaluation, TAMU‐RPL was able to double the number of packets received at the sink compared to RPL and reduced the average delay of packets (in time slots) by more than 10%. In the real deployment, the number of packets received at the sink was increased by more than 33%.

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Distributed Stochastic Online Learning Policies for Opportunistic Spectrum Access

Cited by 74 publications

References 17 publications

Decentralized Blind Spectrum Selection in Cognitive Radio Networks Considering Handoff Cost

Decentralized Blind Spectrum Selection in Cognitive Radio Networks Considering Handoff Cost

MABO‐TSCH: Multihop and blacklist‐based optimized time synchronized channel hopping

TAMU‐RPL: Thompson sampling‐based multichannel RPL

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