Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

He, Ying; Zhang, Zheng; Yu, F. Richard; Zhao, Nan; Yin, Hujun; Leung, Victor C. M.; Zhang, Yanhua

doi:10.1109/tvt.2017.2751641

Cited by 278 publications

(121 citation statements)

References 34 publications

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“…Fortunately, reinforcement learning (RL) has been shown effective in addressing decision making under uncertainty [6]. In particular, recent success of deep RL in humanlevel video game play [7] and AlphaGo [8] has sparked a flurry of interest in applying RL techniques to solve problems from a wide variety of areas and remarkable progress has been made ever since [9], [10], [11]. It provides a robust and principled way to treat environment dynamics and perform sequential decision making under uncertainty, thus representing a promising method to handle the unique and challenging V2X dynamics.…”

Section: A Problem Statement and Motivationmentioning

confidence: 99%

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Liang

Hao

2019

IEEE J. Select. Areas Commun.

340

201

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This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.Index Terms-Vehicular networks, distributed spectrum access, spectrum and power allocation, multi-agent reinforcement learning.

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Section: A Problem Statement and Motivationmentioning

confidence: 99%

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Liang

Hao

2019

IEEE J. Select. Areas Commun.

340

201

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“…For instance, an intelligent modulation and coding selection [67] has been developed for the primary transmission where a DRL agent is implemented at the primary transmitter to learn the interference pattern from secondary transmitters. Moreover, He et al have used DNN to learn the impact of user scheduling on the sum-rate in a wireless caching network [68]. It is noted that the perception and action is usually coupled, especially when we adopt reinforcement learning techniques.…”

Section: B Learning From Radio Environmentmentioning

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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“…Albeit reasonable for discrete states, these approaches cannot deal with large continuous state-action spaces. To cope with such spaces, DRL approaches have been considered for content caching in e.g., [19], [20], [21], [22], [6], [23].…”

Section: A Prior Art On Cachingmentioning

confidence: 99%

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

Sadeghi

Wang

Giannakis

2019

IEEE Trans. Cogn. Commun. Netw.

109

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Caching is envisioned to play a critical role in nextgeneration content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning (RL) framework is put forth. To handle the large continuous state space, a scalable deep RL approach is pursued. The novel approach relies on a hyper-deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learnand-adapt to unknown policies of leaf nodes as well as spatiotemporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.

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Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

Cited by 278 publications

References 34 publications

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

20 Years of Evolution From Cognitive to Intelligent Communications

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

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