Energy-Efficient Resource Allocation in Cognitive Radio Networks Under Cooperative Multi-Agent Model-Free Reinforcement Learning Schemes

Kaur, Amandeep; Kumar, Krishan

doi:10.1109/tnsm.2020.3000274

Cited by 68 publications

(45 citation statements)

References 26 publications

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“…In order to solve the problem of resource allocation in complex communication network scenarios, we propose a reinforcement learning architecture to solve the problem of resource allocation optimization in communication networks. The existing reinforcement learning algorithms applied to resource allocation are mainly divided into distributed multi-agent reinforcement learning algorithm [5] and centralized single agent reinforcement learning algorithm [27,25]. The centralized algorithm needs global information, has better utility value, and can balance the whole network users.…”

Section: Related Workmentioning

confidence: 99%

“…But the network model of [27] does not consider network slicing. [5] proposes a distributed reinforcement learning algorithm based on Q-Learning and SARSA. The secondary users are organized into a random dynamic team in a decentralized and cooperative way, which speeds up the convergence speed of the algorithm, improves the network capacity, and obtains the optimal energy-saving resource allocation strategy.…”

Section: Related Workmentioning

confidence: 99%

“…The secondary users are organized into a random dynamic team in a decentralized and cooperative way, which speeds up the convergence speed of the algorithm, improves the network capacity, and obtains the optimal energy-saving resource allocation strategy. But [5] only considers a single kind of service slice (high rate service slice) in the network model, and due to the use of table-based Q-learning and SARSA algorithm, the state space becomes discrete space, and there is a certain quantization error when segmenting the state space. [25] proposes a graph convolutional networkbased reinforcement learning algorithm based on DQN.…”

Section: Related Workmentioning

confidence: 99%

“…Let t max and R min be the threshold for the data packet transmission delay and transmission rate to meet the communication requirements. The expression that meets the communication requirements is shown in ( 5). The user's QoE is equal to the ratio of the number of packets that meet the inequality requirements to the total number of packets.…”

Section: Fig 2 Transmission Delay Of Data Packetmentioning

confidence: 99%

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Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Yuan

Zhang

Qie

et al. 2021

ComSIS

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With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on under1 lay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Fig 2 Transmission Delay Of Data Packetmentioning

confidence: 99%

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Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Yuan

Zhang

Qie

et al. 2021

ComSIS

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“…Hence, RL technologies in multi-agent environments and distributed networks have become more and more popular. A multi-agent model-free RL scheme for resource allocation is presented in [ 22 ], which mitigates interference and eliminates the need of network model. This scheme is implemented in a decentralized cooperative manner with CRs acting as a multi-agent, forming a stochastic dynamic team to obtain the optimal strategy.…”

Section: Introductionmentioning

confidence: 99%

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

Zhao

Qin

Song

et al. 2020

Sensors

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Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.

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2023

5G and Beyond Wireless Communication Networks

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Energy-Efficient Resource Allocation in Cognitive Radio Networks Under Cooperative Multi-Agent Model-Free Reinforcement Learning Schemes

Cited by 68 publications

References 26 publications

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

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