Deep-Reinforcement-Learning-Based Proportional Fair Scheduling Control Scheme for Underlay D2D Communication

Budhiraja, Ishan; Kumar, Neeraj; Tyagi, Sudhanshu

doi:10.1109/jiot.2020.3014926

Cited by 70 publications

(42 citation statements)

References 32 publications

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“…We build the two deep neural networks, consisting of three fully-connected layers with 64, 64 and 32 neurons. The computational complexity of the proposed ADQN algorithm for training is W T × W P as the number of iterations of loops in [39], [40], the priori information does not introduce extra complexity. Moreover, after training the algorithm, the complexity of ADQN for making decisions depends on the structure of the neural network.…”

Section: Asynchronous Dqn-based Schemementioning

confidence: 99%

Buffer-Aided Relay Selection for Cooperative Hybrid NOMA/OMA Networks With Asynchronous Deep Reinforcement Learning

Huang

Chen

Gong

et al. 2021

IEEE J. Select. Areas Commun.

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This paper investigates asynchronous reinforcement learning algorithms for joint buffer-aided relay selection and power allocation in the non-orthogonal-multiple-access (NOMA) relay network. With the hybrid NOMA/OMA transmission, we investigate joint relay selection and power allocation to maximize the throughput with the delay constraint. To solve this complicated high-dimensional optimization problem, we propose two asynchronous reinforcement learning-based schemes: the asynchronous deep Q-Learning network (ADQN)-based scheme and the asynchronous advantage actor-critic (A3C)-based scheme, respectively. The A3C-based scheme achieves better performance and robustness when the action space is large, while the ADQNbased scheme converges faster with a small action space. Moreover, a-prior information is exploited to improve the convergence of the proposed schemes. The simulation results show that the proposed asynchronous learning-based schemes can learn from the environment and achieve good convergence.

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Section: Asynchronous Dqn-based Schemementioning

confidence: 99%

Buffer-Aided Relay Selection for Cooperative Hybrid NOMA/OMA Networks With Asynchronous Deep Reinforcement Learning

Huang

Chen

Gong

et al. 2021

IEEE J. Select. Areas Commun.

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show abstract

“…4, and the specific implementation process of DDQNPI-RP scheme is shown in Algorithm 2. The computational complexity of the proposed algorithm with/without the priori information is V (M + W ) as the number of iterations of loops in [39], [40], exploring the priori information does not introduce extra complexity.…”

Section: Learning Algorithm With the Priori Informationmentioning

confidence: 99%

Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks With Double Deep Q-Network

Huang

Chen

Gong

et al. 2021

IEEE Trans. Cogn. Commun. Netw.

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This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases, the optimization relies on the buffer states, the interference to/from the primary user, and the constraints on the delay and/or secrecy. This makes it mathematically intractable to apply the traditional optimization methods. In this paper, the double deep Q-network (DDQN) is used to solve the above two optimization problems. We also apply the a-priori information in the CR network to improve the DDQN learning convergence. Simulation results show that the proposed scheme outperforms the traditional algorithm significantly.

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“…I. Budhiraja et al [39] propose communication resource and power allocation control based on deep reinforcement learning. Communication resource and transmission power are allocated to each D2D device so that minimum acceptable Signal-to-Interference and Noise Ratio (SINR) is kept for D2D devices and cellular users while mitigating interference among them.…”

Section: Radio Interference Avoidance In Wireless Networkmentioning

confidence: 99%

Joint Channel Allocation and Routing for ZigBee/Wi-Fi Coexistent Networks

Tanigawa

Nishikori

Kinoshita

et al. 2021

IEICE Trans. Inf. & Syst.

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Deep-Reinforcement-Learning-Based Proportional Fair Scheduling Control Scheme for Underlay D2D Communication

Cited by 70 publications

References 32 publications

Buffer-Aided Relay Selection for Cooperative Hybrid NOMA/OMA Networks With Asynchronous Deep Reinforcement Learning

Buffer-Aided Relay Selection for Cooperative Hybrid NOMA/OMA Networks With Asynchronous Deep Reinforcement Learning

Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks With Double Deep Q-Network

Joint Channel Allocation and Routing for ZigBee/Wi-Fi Coexistent Networks

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