Topology-Transparent Scheduling Based on Reinforcement Learning in Self-Organized Wireless Networks

Qiao, Mu; Zhao, Haitao; Zhou, Li; Zhu, Chunsheng; Huang, Shucai

doi:10.1109/access.2018.2823725

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…[16] and [17] investigated the spectrum sharing problem in cognitive radio networks and developed RL-based spectrum access algorithms for cognitive users. [18] and [19] focused on the self-organization network and adopted RL to deal with the request coordination problem and the user scheduling problem, respectively. In addition, [20] applied RL in the physical layer security and proposed an RL-based spoofing detection scheme.…”

Section: B Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Zhang

Tan

Liang

et al. 2019

IEEE Trans. Wireless Commun.

118

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We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by MCS switchings, we further introduce a switching cost factor in the proposed algorithm. Simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ∼ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, and is 30% ∼ 100% higher than those of the benchmark algorithms. Meanwhile, the proposed algorithm with the switching cost factor can achieve a better balance between the primary transmission rate and system overheads than both the optimal algorithm and benchmark algorithms.

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Section: B Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Zhang

Tan

Liang

et al. 2019

IEEE Trans. Wireless Commun.

118

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“…In [41] Qiao et al extend [17] by employing learningbased approaches. The authors make use of arti icial intelligence techniques for allocating slots to the nodes and utilizing unused slots, thus improving throughput.…”

Section: Srivastava Et Al Inmentioning

confidence: 99%

A probabilistic refined policy for topology-independent medium access control in ad hoc network environments

Dragonas¹,

Tsoumanis²,

Oikonomou³

2022

ITU J-FET

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Modern network environments are starting to engulf billions of different interconnected devices that support a wide range of applications. Depending on the case, these environments range from static (e.g., wireless sensors) to highly dynamic (e.g., vehicular networks) with respect to topology changes and have different constraints for throughput, time delay, energy consumption etc. Supporting such applications in a topology-varying ad hoc environment is a challenging task. Thus, TDMA-based MAC policies are revisited here and a new policy, i.e., the refined policy is proposed, which builds and improves on the topology-independent policies that appear in the literature. In particular, an individual access probability is introduced that is distributively calculated by each node to access time slots that may result to collisions, but if not then unused network resources will be utilized, thus, increasing throughput. The key idea under the refined policy is to identify and refrain from transmitting during slots that collisions are likely to appear. An analytical expression for the individual access probability is also derived here. It is also shown through simulation experiments that energy consumption is also reduced in addition to throughput incremented under the proposed policy.

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“…Then the size of the grid map is k g m × k g n, and the number of γ points is k 2 g mn. In the process of solving γ point, (26) and (27) need to be executed cyclically k 2 g mn times. Equation (27) includes exponentiation and norm operation, and (28) is a process of finding the maximum value in a list of length k 2 g mn.…”

Section: Computational Complexitymentioning

confidence: 99%

“…Agents can select the optimal action in different states according to their previous experience [24]. RL is widely used in various process control fields, including sensor networks [25], [26], path planning [27], [28] and robotics [29], [30]. Recently, the application of reinforcement learning to multi-agent collaborative control is increasing [31]- [34].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

et al. 2020

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Dynamic area coverage is widely used in military and civil fields. Improving coverage efficiency is an important research direction for multi-agent dynamic area coverage. In this paper, we focus on the non-optimal coverage problem of free dynamic area coverage algorithms. We propose a distributed dynamic area coverage algorithm based on reinforcement learning and a γ-information map. The γ-information map can transform the continuous dynamic coverage process into a discrete γ point traversal process, while ensuring no-hole coverage. When agent communication covers the whole target area, agents can obtain the global optimal coverage strategy by learning the whole dynamic coverage process. In the event that communication does not cover the whole target area, agents can obtain a local optimal coverage strategy; in addition, agents can use the proposed algorithm to obtain a global optimal coverage path through off-line planning. Simulation results demonstrate that the required time for area coverage with the proposed algorithm is close to the optimal value, and the performance of the proposed algorithm is significantly better than the distributed anti-flocking Algorithms for dynamic area coverage. INDEX TERMS Dynamic area coverage, multi-agent, reinforcement learning, optimal coverage.

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Topology-Transparent Scheduling Based on Reinforcement Learning in Self-Organized Wireless Networks

Cited by 11 publications

References 29 publications

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

A probabilistic refined policy for topology-independent medium access control in ad hoc network environments

A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

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