Power Control Based on Deep Reinforcement Learning for Spectrum Sharing

Zhang, Haijun; Yang, Ning; Huangfu, Wei; Long, Keping; Leung, Victor C. M.

doi:10.1109/twc.2020.2981320

Cited by 129 publications

(44 citation statements)

References 26 publications

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“…In [34], sensing nodes were used to perceive the environmental information to assist the SU in sharing the spectrum. Zhang et al used the more advanced A3C algorithm in DRL to perform the power control in spectrum sharing [35]. They focused on the tuning and optimization of the A3C algorithm to reduce the dependence on gradient update in the learning process, while we adopted the Dueling DQN method.…”

Section: Related Workmentioning

confidence: 99%

Dueling deep Q-networks for social awareness-aided spectrum sharing

Wang

Xue-yang

Wan

et al. 2021

Complex Intell. Syst.

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In overlapping spectrum sharing, due to the complexity of cognitive environment, it is a real challenge for a secondary user (SU) to correctly sense the usage of the spectrum in real time. To tackle this challenge, a social awareness-aided transmit power control policy for SUs is developed. First, a social network composed of a group of third-party sensing nodes that do not share the spectrum with the PU is established, which helps an SU collect the power information of the PU. Then, we design a Dueling Deep Q-Network (DQN) model to achieve efficient dynamic spectrum sharing between the PU and the SU with the power information collected in the social network. Experimental results show that the spectrum sharing success rate is higher and the comprehensive performance is improved with the sensing nodes selected by the social relationship. Moreover, compared with other deep reinforcement learning (DRL) algorithms, the performance of Dueling DQN is more stable on our targeted spectrum sharing problem.

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Section: Related Workmentioning

confidence: 99%

Dueling deep Q-networks for social awareness-aided spectrum sharing

Wang

Xue-yang

Wan

et al. 2021

Complex Intell. Syst.

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“…Deep ReLU (rectified linear unit) learning can assist in learning an operative action charge policy even when the state notes are contaminated by measurement errors or arbitrary noise. Using deep reinforcement learning power control-based spectrum sharing in wireless networks showing the relation between PU and SU, wireless sensors have been studied in [34] and channel selection policy for PU and SU has been studied in [35]. On the other hand, the conventional ReLU method is unfeasible for such issues because of an inadequate number of states in the presence of arbitrary noise.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning‐Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Aslam

Ahmed

et al. 2021

Wireless Communications and Mobile Computing

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The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent-based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady-state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large-scale sensing delay in the CR network.

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“…Despite its great benefit for spectrum efficiency, there are still some tricky problems with the NOMA techniques. A notable issue targets energy consumption [23][24][25][26][27]. To support simultaneous transmission in NOMA systems, much more transmission and circuit power consumption are inevitable.…”

Section: Introductionmentioning

confidence: 99%

User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

et al. 2021

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Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush–Kuhn–Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods.

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Power Control Based on Deep Reinforcement Learning for Spectrum Sharing

Cited by 129 publications

References 26 publications

Dueling deep Q-networks for social awareness-aided spectrum sharing

Dueling deep Q-networks for social awareness-aided spectrum sharing

A Deep Learning‐Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

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