Bandit Learning with Concurrent Transmissions for Energy-Efficient Flooding in Sensor Networks

Zhang, Peilin; Gao, Alex Yuan; Theel, Oliver

doi:10.4108/eai.20-3-2018.154369

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…In particular, D2D communication is always restricted by computational processing and limited knowledge about the environment. Several solutions have been proposed to tackle these problems, for example [14]- [18]. In [14], the author proposed a joint link scheduling and power allocation optimization algorithm to maximize the system throughput of D2D-assisted wireless caching networks.…”

Section: Related Workmentioning

confidence: 99%

Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

et al. 2019

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Recently, there is the widespread use of mobile devices and sensors, and rapid emergence of new wireless and networking technologies, such as wireless sensor network, device-to-device (D2D) communication, and vehicular ad hoc networks. These networks are expected to achieve a considerable increase in data rates, coverage, and the number of connected devices with a significant reduction in latency and energy consumption. Because there are energy resource constraints in user's devices and sensors, the problem of wireless network resource allocation becomes much more challenging. This leads to the call for more advanced techniques in order to achieve a tradeoff between energy consumption and network performance. In this paper, we propose to use reinforcement learning, an efficient simulation-based optimization framework, to tackle this problem so that user experience is maximized. Our main contribution is to propose a novel non-cooperative and real-time approach based on deep reinforcement learning to deal with the energy-efficient power allocation problem while still satisfying the quality of service constraints in D2D communication.INDEX TERMS Energy efficient wireless communication, power allocation, D2D communication, multiagent reinforcement learning, deep reinforcement learning.

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Section: Related Workmentioning

confidence: 99%

Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

et al. 2019

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“…Wireless sensor networks (WSNs) [1][2][3][4][5] have been shown the widely applications in nearly all aspects of life nowadays. Tracking and monitoring are two main targets of WSNs, which cover almost applications including environment, agriculture, military, healthcare, and industry.…”

Section: Introductionmentioning

confidence: 99%

Reliability-Security Analysis for Harvest-to-Jam based Multi-hop Cluster MIMO Networks Using Cooperative Jamming Methods Under Impact of Hardware Impairments

Anh¹,

Minh²,

Duy³

et al. 2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In this paper, we consider harvest-to-jam based secure multi-hop cluster multi-input multi-output networks, where a multi-antenna source sends its data to a multi-antenna destination via multi-antenna intermediate cluster heads. The data transmission at each hop is realized by using transmit antenna selection and selection combining techniques, and is overheard by a multi-antenna eavesdropper using selection combining. In addition, joint antenna and jammer selection methods are performed at each hop to reduce quality of the eavesdropping channels. The cluster members can harvest wireless energy from the previous cluster head, and use the harvested energy for emitting jamming noises on the eavesdropper. We propose three cooperative jamming algorithms, named best antenna and best jammer selection (BA-BJ), random antenna and all jammer selection (RA-AJ) and all antenna and all jammer selection (AA-AJ). Then, end-to-end outage probability and intercept probability of the proposed algorithms are evaluated via both simulation and analysis, under impact of hardware impairments, over Rayleigh fading channel.

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“…Resource allocation problems in D2D communication have received enormous attention from the research community [10]- [15]. In [10], the authors considered three scenarios, namely the perfect channel state information, partial channel state information, and imperfect channel between the users and the transmitters, to present a resource allocation algorithm to achieve optimal performance in terms of secrecy throughput and energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications

et al. 2019

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Device-to-device (D2D) communication is an emerging technology in the evolution of the 5G network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next generation of many platforms and applications, e.g. real-time high-quality video streaming, virtual reality game, and smart city operation. However, the rapid proliferation of user devices and sensors leads to the need for more efficient resource allocation algorithms to enhance network performance while still capable of guaranteeing the quality-of-service. Currently, deep reinforcement learning is rising as a powerful tool to enable each node in the network to have a real-time self-organising ability. In this paper, we present two novel approaches based on deep deterministic policy gradient algorithm, namely ''distributed deep deterministic policy gradient'' and ''sharing deep deterministic policy gradient'', for the multi-agent power allocation problem in D2D-based V2V communications. Numerical results show that our proposed models outperform other deep reinforcement learning approaches in terms of the network's energy efficiency and flexibility. INDEX TERMS Non-cooperative D2D communication, D2D-based V2V communications, power allocation, multi-agent deep reinforcement learning, and deep deterministic policy gradient (DDPG).

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Bandit Learning with Concurrent Transmissions for Energy-Efficient Flooding in Sensor Networks

Cited by 3 publications

References 21 publications

Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

Reliability-Security Analysis for Harvest-to-Jam based Multi-hop Cluster MIMO Networks Using Cooperative Jamming Methods Under Impact of Hardware Impairments

Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications

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