Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

Nasir, Yasar Sinan; Guo, Dongning

doi:10.1109/jsac.2019.2933973

Cited by 531 publications

(334 citation statements)

References 34 publications

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“…Recently, Guo et al [111] have proposed a novel multiagent framework, which includes a centralized agent and multiple intelligent nodes. In particular, the centralized agent is responsible for training a common RL model for all the intelligent nodes and each intelligent node makes action decisions independently according to the trained RL model.…”

Section: B Intelligent Action 1) Reinforcement Learning Enabled Intementioning

confidence: 99%

20 Years of Evolution From Cognitive to Intelligent Communications

Qin

Zhou

Zhang

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AIenabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.

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Section: B Intelligent Action 1) Reinforcement Learning Enabled Intementioning

confidence: 99%

20 Years of Evolution From Cognitive to Intelligent Communications

Qin

Zhou

Zhang

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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“…Indeed, this is fundamental and helps the agent to figure out exactly which action to perform until it converges to an optimal policy. Nevertheless, as highlighted in [13], Q-learning has two serious problems: (1) the amount of memory required to save and update the Q-table can increase exponentially as the number of states and actions increases, (2) many states are rarely visited and, consequently, the amount of time required to explore all these possibilities (state/action pairs) in order to create a good estimate for Q-table would be unrealistic or impractical in a real setting [14].…”

Section: Problem Formulation and Optimal Solutionmentioning

confidence: 99%

“…Then, the train DQN updates its parameters with the new parameters provided by training. According to [14], the convergence to a set of good parameters occurs quickly.…”

Section: Problem Formulation and Optimal Solutionmentioning

confidence: 99%

“…1. llustration of the proposed multi-agent deep RL algorithm for resource allocation [14], adapted. the state vector of length (N + J) and the output layer has dimensionality corresponding to the number of possible actions, in this case, J.…”

mentioning

confidence: 99%

“…This framework eases implementation and improves stability. Moreover, this strategy can also significantly reduce the amount of memory and computational resources required by training [14]. Therefore, each agent has the same copy of Q θtarget , while Q θtrain is localized at the central module.…”

mentioning

confidence: 99%

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Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks

Saraiva

Braga

Monteiro

et al. 2020

JCIS

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In this article, we study a Radio Resource Allocation (RRA) that was formulated as a non-convex optimization problem whose main aim is to maximize the spectral efficiency subject to satisfaction guarantees in multiservice wireless systems. This problem has already been previously investigated in the literature and efficient heuristics have been proposed. However, in order to assess the performance of Machine Learning (ML) algorithms when solving optimization problems in the context of RRA, we revisit that problem and propose a solution based on a Reinforcement Learning (RL) framework. Specifically, a distributed optimization method based on multi-agent deep RL is developed, where each agent makes its decisions to find a policy by interacting with the local environment, until reaching convergence. Thus, this article focuses on an application of RL and our main proposal consists in a new deep RL based approach to jointly deal with RRA, satisfaction guarantees and Quality of Service (QoS) constraints in multiservice celular networks. Lastly, through computational simulations we compare the state-of-art solutions of the literature with our proposal and we show a near optimal performance of the latter in terms of throughput and outage rate.

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