Energy efficient constrained shortest path first-based joint resource allocation and route selection for multi-hop CRNs

Chen, Qianbin; Wang, Ling; Gao, Yuanpeng; Chai, Rong; Huang, Xiaoge

doi:10.1109/cc.2017.8246338

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In order to obtain the maximum value of P i , s , the fractional programming problem P2 can be equivalent to Dinkelbach’s method [ 26 ]: where η is a non-negative parameter. When f ( η * ) = 0, the energy efficiency function reaches its maximum value.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

Liao

Jiang

et al. 2023

PLoS ONE

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This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.

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Section: The Proposed Methodsmentioning

confidence: 99%

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

Liao

Jiang

et al. 2023

PLoS ONE

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“…At present, some related studies have implemented a route policy in multi-hop CRNs [5]- [9]. Khalife et al [7] proposed three separate routing scheme categories.…”

Section: A Related Workmentioning

confidence: 99%

Routing Selection With Reinforcement Learning for Energy Harvesting Multi-Hop CRN

Jiang

Song

et al. 2019

IEEE Access

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This paper considers the routing problem in the communication process of an energy harvesting (EH) multi-hop cognitive radio network (CRN). The transmitter and the relay harvest energy from the environment use it exclusively for transmitting data. In a relay on the path, a limited data buffer is used to store the received data and forward it. We consider a real-world scenario where the EH node has only local causal knowledge, i.e., at any time, each EH node only has knowledge of its own EH process, channel state, and currently received data. An EH routing algorithm based on Q learning in reinforcement learning (RL) for multi-hop CRNs (EHR-QL) is proposed. Our goal is to find an optimal routing policy that can maximize throughput and minimize energy consumption. Through continuous intelligent selection under the partially observable Markov decision process (POMDP), we use the Q learning algorithm in RL with linear function approximation to obtain the optimal path. Compared with the basic Q learning routes, the EHR-QL is superior for longer distances and higher hop counts. The algorithm produces more EH, less energy consumption, and predictable residual energy. In particular, the time complexity of the EHR-QL is analyzed and its convergence is proved. In the simulation experiments, first, we verify the EHR-QL using six EH secondary users (EH-SUs) nodes. Second, the performance (i.e., network lifetime, residual energy, and average throughput) of the EHR-QL is evaluated under the influences of different the learning rates α and discount factors γ. Finally, the experimental results show that the EHR-QL obtains a higher throughput, a longer network lifetime, less latency, and lower energy consumption than the basic Q learning routing algorithms. INDEX TERMS Routing selection, multi-hop CRN, energy harvesting, Q learning, reinforcement learning, MDP.

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An integrated service model to support user specific QoS routing in cognitive radio ad hoc network

Dhingra

Dhand

Chawla

et al. 2020

Peer-to-Peer Netw. Appl.

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Energy efficient constrained shortest path first-based joint resource allocation and route selection for multi-hop CRNs

Cited by 6 publications

References 11 publications

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

Routing Selection With Reinforcement Learning for Energy Harvesting Multi-Hop CRN

An integrated service model to support user specific QoS routing in cognitive radio ad hoc network

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