Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks

Cao, Kaitian; Qian, Ping

doi:10.3390/s20041146

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…Specifically, Q-learning approaches are developed in [63]- [65] to find the optimal handoff strategies for SUs. To speed up the learning process of the newly joined SUs, both [63] and [64] employ the offline Learning from Demonstration TL strategy. Particularly, the authors propose to transfer the complete Q-table of the nearest SU to serve as an initial point for the new SU.…”

Section: A Cognitive Radio Networkmentioning

confidence: 99%

“…Particularly, the authors propose to transfer the complete Q-table of the nearest SU to serve as an initial point for the new SU. Simulation results show that the TL approaches in [63] and [64] can improve the convergence rate of the new SU's learning process by up to 30% and 14%, respectively. Unlike [63] and [64], [65] proposes to transfer the knowledge of an expert SU selected based on its similarities with the new SU in terms of channel statistics, node statistics, and application statistics.…”

Section: A Cognitive Radio Networkmentioning

confidence: 99%

“…Simulation results show that the TL approaches in [63] and [64] can improve the convergence rate of the new SU's learning process by up to 30% and 14%, respectively. Unlike [63] and [64], [65] proposes to transfer the knowledge of an expert SU selected based on its similarities with the new SU in terms of channel statistics, node statistics, and application statistics. Particularly, the level of similarity is determined using the Bregman divergence of the channel statistics, node statistics, and application statistics among the SUs.…”

Section: A Cognitive Radio Networkmentioning

confidence: 99%

See 2 more Smart Citations

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

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With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. Doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on applications of TL in wireless networks. Particularly, we first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, localization, signal recognition, security, human activity recognition and caching, which are all important to nextgeneration networks such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

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Section: A Cognitive Radio Networkmentioning

confidence: 99%

Section: A Cognitive Radio Networkmentioning

confidence: 99%

Section: A Cognitive Radio Networkmentioning

confidence: 99%

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Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

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show abstract

“…From the perspective of computational complexity, DQN and Q-learning have obvious advantages, because their mapping from state to action has been trained. Since there is no recursive calculation, the time complexity of DQN and Q-Learning are both (1) O . For h-DQN, the metacontroller and sub-controller separately calculates the Q value and the reward for each action in the next possible state.…”

Section: ) Computational Complexitymentioning

confidence: 99%

“…In wireless networks, spectrum resources are becoming more and more scarcer due to the increasing demand for wireless communication [1]. It is necessary to address the problem of spectrum underutilization and inefficiency.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Multichannel Sensing in Cognitive Radio: Hierarchical Reinforcement Learning

Liu

Jiayun

2021

IEEE Access

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Efficient use of spectral resources is critical in wireless networks and has been extensively studied in recent years. Dynamic spectrum access (DSA) is one of the key techniques on utilizing the spectral resources. Among them, reinforcement learning (RL) for DSA has gained great attention due to the excellent performance. Limited by the large state space in RL, obtaining the best solution to the spectrum access problem is often computationally expensive. Besides, it is hard to balance multiple objectives of the reward function in RL. To tackle these problems, we explore deep reinforcement learning in a layered framework and propose a hierarchical deep Q-network (h-DQN) model for DSA. The proposed approach divides the original problem into separate "sub problems", each of which is solved using its own reinforcement learning agent. This partitioning simplifies each individual problem, enables modularity, and reduces the complexity of the whole optimization process in the multi-objective case. The performance of Q-learning for dynamic sensing(QADS), deep reinforcement learning for dynamic access (DRLDA), and the proposed h-DQN model is evaluated through simulations. The simulation results show that h-DQN yields better performance with the faster convergence and higher channel utilization than the other two compared methods.INDEX TERMS Dynamic multichannel sensing, deep Q-network, hierarchical reinforcement learning, cognitive radio.

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Spectrum Mobility in Cognitive Radio Networks Using Spectrum Prediction and Monitoring Techniques

2021

Spectrum Sharing in Cognitive Radio Networks

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Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks

Cited by 10 publications

References 23 publications

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Dynamic Multichannel Sensing in Cognitive Radio: Hierarchical Reinforcement Learning

Spectrum Mobility in Cognitive Radio Networks Using Spectrum Prediction and Monitoring Techniques

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