A Cross-Layer Routing Protocol Based on Quasi-Cooperative Multi-Agent Learning for Multi-Hop Cognitive Radio Networks

Du, Yihang; Chen, Chun; Ma, Pengfei; Xue, Lei

doi:10.3390/s19010151

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…The development of joint approach of routing and resource allocation is a challenging task due to tradeoff between resource allocation and routing. Recently, Du et al [20] presented a joint approach for multi-hop Cognitive Radio Network to deal with the issue of transmission delay and energy efficient communication. To mitigate these issues, a cross-layer routing protocol is developed where all delay and power efficiency issues are addressed using a utility function and later this problem is modeled in the form of stochastic games.…”

Section: Literature Surveymentioning

confidence: 99%

Joint Channel and Interference Aware Cooperative Routing for Cognitive Radio Network

Gudihatti K N*,

Manjula,

K R

2020

IJRTE

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Cognitive Radio based network technology provides a promising solution for various types of real-time wireless communication by offering better spectrum utilization and resource allocation. Generally, the dynamic network topology, interference, channel switching and under-utilization of resource can degrade the network performance. Therefore, development of promising solution to obtain the desired performance is a challenging research topic in CRNs. Several researches have been carried out which have focused on development of routing protocol for CRNs to improve the performance. These routing protocols are classified as local and global routing which are mainly focused on overhead reduction and optimal route selection respectively. However, the conventional approaches suffer from various issues such as interflow-interference, channel switching delay and node overhearing problems which can progress towards the poor network performance. In this paper, our objective is to focus on the inter-flow interference, channel switching delay, and develop a cooperative communication based approaches where inter-flow interference and overhearing issues are mitigated using cooperative communication. Furthermore, Switching Delay and Interference (SDI) routing metric is developed to reduce the switching delay and also finally, a cooperative scheme of packet transmission is developed where direct or cooperative communication is selected for successful packet transmission. The proposed approach jointly considers channel and interference issue, hence it is known as Joint Cooperative Channel and Interference Aware Routing (JCIAR). The performance of proposed approach is compared with the existing techniques such as Primary User Aware k-hop route discovery scheme (PAK), AODV, CognitiveAODV, and Location-Aided Routing for CRN (LAUNCH) in terms of delay, packet delivery rate and throughput. The obtained result shows a significant improvement in network performance.

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Section: Literature Surveymentioning

confidence: 99%

Joint Channel and Interference Aware Cooperative Routing for Cognitive Radio Network

Gudihatti K N*,

Manjula,

K R

2020

IJRTE

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“…Otherwise, it is assumed that the current transmit power is being maintained within an appropriate range and should remain unchanged. According to this principle, the concept of responsibility rating was introduced in [20,23]. Nevertheless, as for the responsibility rating, the update framework is defined as follows:…”

Section: Dynamic Adjustment Ratingmentioning

confidence: 99%

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Xue

et al. 2019

Energies

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Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).

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“…The network conjunction with the MAC provides a routing solution that is imperative [8]. Often with dynamic alloca-tion and its value, CRNs [9][10][11][12][13][14] are cross-layered. It often boosts customised programming, spectrum resources and power control by improving the optimum routing of the CRN.…”

Section: Introductionmentioning

confidence: 99%

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

Dhiman¹,

Sharma

2021

Complex Intell. Syst.

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In the case of new technology application, the cognitive radio network (CRN) addresses the bandwidth shortfall and the fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity of resources and mobility. In this paper, we present a reconfigurable CRN-based cross-layer routing protocol with the purpose of increasing routing performance and optimizing data transfer in reconfigurable networks. Recently developed spotted hyena optimizer (SHO) is used for tuning the hyperparameters of machine-learning models. The system produces a distributor built with a number of tasks, such as load balance, quarter sensing and the development path of machine learning. The proposed technique is sensitive to traffic and charges, as well as a series of other network metrics and interference (2bps/Hz/W average). The tests are performed with classic models that demonstrate the residual energy and strength of the resistant scalability and resource.

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A Cross-Layer Routing Protocol Based on Quasi-Cooperative Multi-Agent Learning for Multi-Hop Cognitive Radio Networks

Cited by 11 publications

References 26 publications

Joint Channel and Interference Aware Cooperative Routing for Cognitive Radio Network

Joint Channel and Interference Aware Cooperative Routing for Cognitive Radio Network

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

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