Reinforcement Learning Based Channel Selection for Design of Routing Protocol in Cognitive Radio Network

Talekar, Sopan; Terdal, Sujata

doi:10.1109/csitss47250.2019.9031024

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The Actor-Critic model of Reinforcement Learning is used to improve the routing performance in CRN. In this, the optimal policy is determined by the actor-critic model to perform the channel selection in CRN [17]. In [18], impact of variable packet size on routing performance in cognitive radio networks is evaluated.…”

Section: Related Workmentioning

confidence: 99%

“…Network Simulator NS2 with CRCN patch is used to evaluate the performance of the proposed routing protocol. The performance analysis of the proposed routing protocol IQRLC is compared with the existing routing protocol CSRC [17], LCRP [15] ,and CRP and it is discussed below.…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

“…The proposed IQRLC protocol is evaluated with different routing metrics and it is compared with CSRC [20], LCRP and CRP routing protocols [17]. In LCRP, the optimal policy for channels is derived based on the policy iteration method.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Untitled

2023

IJCNC

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Cognitive Radio Networks are an emerging technology in for wireless communication. With increasing number of wireless devices in wireless communication, there is a shortage of spectrum. Also, due to the static allocation of channels in wireless networks, there is a scarcity of spectrum underutilization. For efficient spectrum utilization, secondary users dynamically select the free channel of primary users for the transmission of packets. In this work, the performance of routing in a cognitive radio network is improved by the decision of optimal channel selection. The aim of this work is to maximize the throughput and reduce the end-to-end delay. Therefore, an Improved Q-Reinforcement learning algorithm is proposed for the optimal channel selection during the packet routing between source and destination. The performance of this work is compared with the existing routing protocols. It is simulated in network simulator-2 (NS2) with Cognitive Radio Cognitive Network (CRCN) simulation. After performance evaluation, it is observed that the proposed work performs better than existing work with respect to packet delivery ratio, throughput, delay, jitter, control overhead, call blocking probability, packet dropping ratio, good put and normalized routing overhead.

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Section: Related Workmentioning

confidence: 99%

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

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2023

IJCNC

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“…In [27], Li et al proposed an interference-aware RL algorithm to solve the joint problem of multichannel selection and data scheduling. In [28], Talekar and Terdal proposed a solution for optimal channel selection and routing and applied RL to select the best channel for routing. However, these works 2…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Two-Stage Task Offloading Optimization for Power Distribution Internet of Things

Huang

et al. 2022

Wireless Communications and Mobile Computing

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The increase in the number of services in the power distribution grid leads to a massive increase in task data. Power distribution internet of things (PDIoT) is the specific application of internet of things (IoT) in the power distribution grid. By deploying a large number of PDIoT devices, the voltage, active power, reactive power, and harmonic parameters are collected to support distribution grid services such as fault identification and status detection. Therefore, PDIoT utilizes massive devices to collect and offload tasks to the edge server through 5G network for real-time data processing. However, how to dynamically select edge servers and channels to meet the energy-efficient and low-latency task offloading requirements of PDIoT devices still faces several technical challenges such as task offloading decisions coupling among devices, unobtainable global state information, as well as interrelation of various quality of service (QoS) metrics such as energy efficiency and delay. To this end, we firstly construct a joint optimization problem to maximize the weighted difference between energy efficiency and delay of devices in PDIoT. Then, the joint optimization problem is decomposed into a large-timescale server selection problem and a small-timescale channel selection problem. Next, we propose an ML-based two-stage task offloading algorithm, where the large-timescale problem is solved by two-side matching in the first stage, and the small-timescale problem is solved by adaptive ε -greedy learning in the second stage. Finally, simulation results show that compared with the task offloading delay-first matching algorithm and the matching theory-based task offloading strategy, the proposed algorithm performs superior in terms of energy efficiency and delay.

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Routing Prediction Strategy for UAV Swarm Network Using Pigeon-Inspired Optimization-Based Neural Network

Zheng

2023

Int. J. Aeronaut. Space Sci.

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A routing prediction strategy via a pigeon-inspired optimization (PIO)-based neural network (NN) is designed for UAV swarm networks with highly dynamic topology. The proposed strategy can predict the performance of the neighboring nodes as the next hop. For more precise prediction and less computational complexity, the states of the UAV swarm motion and the network are considered as the prior information, and the PIO-based NN framework is established. Based on the system model, PIO is applied to find the optimal weight matrices of the NN-based routing prediction model. The matrix of the hop count index function is calculated using this prediction model. The proposed strategy can directly determine the next hop based on the prediction results or can be combined with other routing methods to maintain a balance between the stability and the shortest path. Numerical simulations are conducted to demonstrate the effectiveness of the proposed strategy.

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Reinforcement Learning Based Channel Selection for Design of Routing Protocol in Cognitive Radio Network

Cited by 4 publications

References 16 publications

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Machine Learning-Based Two-Stage Task Offloading Optimization for Power Distribution Internet of Things

Routing Prediction Strategy for UAV Swarm Network Using Pigeon-Inspired Optimization-Based Neural Network

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