Joint Rate and Power Optimization for Multimedia Streaming in Wireless Fading Channels via Parametric Policy Gradient

Chai, Shuqi; Lau, Vincent K. N.

doi:10.1109/tsp.2019.2924579

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…On the other hand, policy-based reinforcement learning plays an important role in the optimization of wireless channel. [22] focused on the joint rate and power optimization in the fading channel by using policy gradient with deterministic policy. Innovatively, Actor-Critic method, as the combination of policy gradient and tabular Q-learning, performed well in the problem of user scheduling and resource allocation in [23].…”

Section: A Prior Workmentioning

confidence: 99%

Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel

Liu¹,

Wang²

2020

Preprint

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With the high development of wireless communication techniques, it is widely used in various fields for convenient and efficient data transmission. Different from commonly used assumption of the time-invariant wireless channel, we focus on the research on the time-varying wireless downlink channel to get close to the practical situation. Our objective is to gain the maximum value of sum rate in the time-varying channel under the some constraints about cut-off signal-to-interference and noise ratio (SINR), transmitted power and beamforming. In order to adapt the rapid changing channel, we abandon the frequently used algorithm convex optimization and deep reinforcement learning algorithms are used in this paper. From the view of the ordinary measures such as power control, interference incoordination and beamforming, continuous changes of measures should be put into consideration while sparse reward problem due to the abortion of episodes as an important bottleneck should not be ignored. Therefore, with the analysis of relevant algorithms, we proposed two algorithms, Deep Deterministic Policy Gradient algorithm (DDPG) and hierarchical DDPG, in our work. As for these two algorithms, in order to solve the discrete output, DDPG is established by combining the Actor-Critic algorithm with Deep Q-learning (DQN), so that it can output the continuous actions without sacrificing the existed advantages brought by DQN and also can improve the performance. Also, to address the challenge of sparse reward, we take advantage of meta policy from the idea of hierarchical theory to divide one agent in DDPG into one meta-controller and one controller as hierarchical DDPG. Our simulation results demonstrate that the proposed DDPG and hierarchical DDPG performs well from the views of coverage, convergence and sum rate performance.

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Section: A Prior Workmentioning

confidence: 99%

Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel

Liu¹,

Wang²

2020

Preprint

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“…However, in real world, 'fast' dynamics and 'slow' dynamics may coexist in one system, which can be modeled as a singularly perturbation system, such as circuit systems, high-speed aircraft systems, etc. [31][32][33][34]. Recently, SPCNs have attracted considerable concern of many scholars [35][36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

Wang,

Wu,

Cao

2024

Neural Process Lett

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This paper investigates the topology identification and synchronization in finite time for fractional singularly perturbed complex networks (FSPCNs). Firstly, a convergence principle is developed for continuously differential functions. Secondly, a dynamic event-triggered mechanism (DETM) is designed to achieve the network synchronization, and a topology observer is developed to identify the network topology. Thirdly, under the designed DETM, by constructing a Lyapunov functional and applying the inequality analysis technique, the topology identification and synchronization condition in finite time is established in the forms of the matrix inequality. In addition, it is proved that the Zeno behavior can be effectively excluded. Finally, the effectiveness of the main results is verified by an application example.

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“…Furthermore, a DNN task may have to span multiple time slots to finish. In addition, the wireless channels are also time-varying due to the mobility of the WDs [29], [30]. Thus, the existing static optimization approach fails to embrace the dynamic nature of the DNN task offloading problem.…”

Section: Introductionmentioning

confidence: 99%

Sequential Offloading for Distributed DNN Computation in Multiuser MEC Systems

Wang¹,

Cai²,

Lau³

2022

Preprint

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Joint Rate and Power Optimization for Multimedia Streaming in Wireless Fading Channels via Parametric Policy Gradient

Cited by 11 publications

References 33 publications

Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel

Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

Sequential Offloading for Distributed DNN Computation in Multiuser MEC Systems

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