Cognitive Communication in Link-Layer Evaluation Based Cellular-Vehicular Networks

Zhang, Luyong; Yang, Yijie; Chen, Jinhua

doi:10.1109/access.2018.2882887

Cited by 11 publications

(1 citation statement)

References 42 publications

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“…Mutual interference between multiple networks is a factor that needs to be considered in actual communication scenarios. In this paper, the latter is selected as the research basis among the two spectrum sharing methods of master user priority and master-slave coordination under threshold [6]. There are several sending and receiving endpoints, and it is considered that there are multiple cognitive networks between the two, which share the spectrum with the main user network through the above method, and merge the nodes that can establish relay transmission between the two endpoints into the same network [7], which is regarded as a cellular network scenario when there is only one node.…”

Section: Resource Allocation Modelmentioning

confidence: 99%

Cognitive network spectrum allocation based on multi-agent reinforcement learning

Wang

Zhang²

2022

Third International Conference on Computer Communication and Network Security (CCNS 2022)

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A more important part of the field of deep reinforcement learning is the study of multi-agents, for the specific scenario of multi-cognitive networks, the choice of spectrum will be affected by two parts, a single cognitive network and the access device under the cognitive network. In view of this specific problem, this paper uses the improved multi-agent reinforcement learning to solve, through the use of multiple agents from the user link modeling, can solve the problem of different cognitive networks for action execution when the environmental state is constantly updated, compared with the original algorithm. In the scenario where spectrum allocation is required in multi-cognitive networks, the improved algorithm can better handle the relationship between master and slave users in multiple networks, so that the spectrum utilization and the overall communication performance of the system are further improved.

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Section: Resource Allocation Modelmentioning

confidence: 99%

Cognitive network spectrum allocation based on multi-agent reinforcement learning

Wang

Zhang²

2022

Third International Conference on Computer Communication and Network Security (CCNS 2022)

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Multi-cognitive network resource allocation based on improved artificial bee colony algorithm

Zhang

2020

J. Phys.: Conf. Ser.

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This paper introduces a graph theory model for resource allocation in multi-cognitive wireless network scenarios. Aiming at the problems of low search accuracy and slow convergence speed of the basic artificial bee colony algorithm, an improved bee colony algorithm is proposed. The improved algorithm introduces an adaptive t-distribution mutation strategy. Compared with the original algorithm, the performance of the improved algorithm has greatly improved. At the same time, the improved bee colony algorithm is applied to multi-cognitive wireless network scenarios for resource allocation in the model. The experimental results show that the improved artificial bee colony algorithm can obtain greater system throughput and effectively reduce the performance of multi-cognitive wireless network systems, and can allocate resources more reasonably.

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Deep Reinforcement Learning for Dynamic Multichannel Access in Multi-Cognitive Radio Networks

Wang

Zhang

Chen

et al. 2020

J. Phys.: Conf. Ser.

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We propose a reinforcement learning framework based on deep recurrent learning to solve the dynamic spectrum access problem in the scenario where multiple cognitive networks coexist. In this scenario, the shared spectrum is divided into multiple channels, and the channel occupation by the primary user is modeled as a Markov model. The observation of the channel status by secondary users in this area obeys the partially observed Markov process, that is, each user can only observe the status of one channel in each time slot, and cannot obtain global information. To avoid collision in different cognitive networks, we adopt the distributed multi-agent deep reinforcement learning method. Without the interaction of different cognitive networks, based on the current partial observations, a suitable channel access policy can be obtained by a neural network. Comparing with Slotted-aloha and DQN-DSA Algorithm, the result indicates DRQN-DSA proposed in this paper performs better.

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Cognitive Communication in Link-Layer Evaluation Based Cellular-Vehicular Networks

Cited by 11 publications

References 42 publications

Cognitive network spectrum allocation based on multi-agent reinforcement learning

Cognitive network spectrum allocation based on multi-agent reinforcement learning

Multi-cognitive network resource allocation based on improved artificial bee colony algorithm

Deep Reinforcement Learning for Dynamic Multichannel Access in Multi-Cognitive Radio Networks

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