Joint EH Time and Transmit Power Optimization Based on DDPG for EH Communications

Li, Lingyun; Xu, Hongbo; Ma, Jie; Zhou, Anyu; Liu, Jun

doi:10.1109/lcomm.2020.2999914

Cited by 25 publications

(12 citation statements)

References 8 publications

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“…Note that the data collection process is independent of energy harvesting and data transmission. Each node in the network is assumed to be equipped with a single antenna as [8], [13], [28], [31], [32]. We assume that each PU decides its transmission based on two-state Discrete-Time Markov Chain (DTMC) model, where the transfer probabilities are P 1 and P 2 .…”

Section: System Modelmentioning

confidence: 99%

“…In the EH system, the problem of joint time and transmit power optimization based on DRL was studied in [31], [32]. Due to the outstanding performance advantage in the continuous action space problem, the DDPG [33] algorithm was used in [31], [32] for continuous time and transmit power decisions. In [31], Li et al studied an EH point-to-point network, in which rechargeable batteries were installed in the transmitter.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the outstanding performance advantage in the continuous action space problem, the DDPG [33] algorithm was used in [31], [32] for continuous time and transmit power decisions. In [31], Li et al studied an EH point-to-point network, in which rechargeable batteries were installed in the transmitter. Based on the novel DDPG algorithm, the transmitter sends data with an appropriate transmit power in the period τ t , and harvests energy in the remaining time slot T − τ t .…”

Section: Introductionmentioning

confidence: 99%

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“…, adopted by the secondary user as π P 0,n ,αn . Then, the corresponding optimization problem can be formulated as follows [8], [9], [23]:…”

Section: System Modelmentioning

confidence: 99%

“…In the following, problem P1 will be decomposed into two simpler optimization subproblems, in order to facilitate the application of DDPG. First, similar to [8], we introduce an energy fluctuation parameter, which is defined as the difference between the energy consumed and the energy harvested at t n :…”

Section: B Problem Reformulationmentioning

confidence: 99%

No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks

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2021

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This paper applies machine learning to optimize the transmission policy of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks, where time-division multiple access (TDMA) is used to serve multiple primary users and an energy-constrained secondary user is admitted to the primary users' time slots via NOMA. During each time slot, the secondary user performs the two tasks: data transmission and energy harvesting based on the signals received from the primary users. The goal of the paper is to maximize the secondary user's long-term throughput, by optimizing its transmit power and the time-sharing coefficient for its two tasks. The long-term throughput maximization problem is challenging due to the need for making decisions that yield long-term gains but might result in shortterm losses. For example, when in a given time slot, a primary user with large channel gains transmits, intuition suggests that the secondary user should not carry out data transmission due to the strong interference from the primary user but perform energy harvesting only, which results in zero data rate for this time slot but yields potential long-term benefits. In this paper, a deep reinforcement learning (DRL) approach is applied to emulate this intuition, where the deep deterministic policy gradient (DDPG) algorithm is employed together with convex optimization. Our simulation results demonstrate that the proposed DRL assisted NOMA transmission scheme can yield significant performance gains over two benchmark schemes.

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Joint training method for transmission defects based on component hierarchy

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Joint EH Time and Transmit Power Optimization Based on DDPG for EH Communications

Cited by 25 publications

References 8 publications

Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications

Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications

No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks

Joint training method for transmission defects based on component hierarchy

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