Dynamic Heterogeneous Learning Games for Opportunistic Access in LTE-Based Macro/Femtocell Deployments

Alnwaimi, Ghassan; Vahid, Seiamak; Moessner, Klaus

doi:10.1109/twc.2014.2384510

Cited by 70 publications

(36 citation statements)

References 27 publications

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“…In [14] the authors presented a heterogeneous fully distributed multi-objective strategy based on a reinforcement learning model con- Spectrum learning in cognitive radio [12] figure 3. Illustration of reinforcement learning: a) Markov decision process; b) partially observed Markov decision process; c) Q-learning.…”

Section: Q-learning: Femto/small Cellsmentioning

confidence: 99%

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Machine Learning Paradigms for Next-Generation Wireless Networks

Jiang¹,

Zhang

Ren

et al. 2017

IEEE Wireless Commun.

992

496

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Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

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Section: Q-learning: Femto/small Cellsmentioning

confidence: 99%

“…• Unknown system transition model • Q-function maximization Femto and small cells [14,15] Multi-armed bandit…”

Section: Future Research and Conclusionmentioning

confidence: 99%

Machine Learning Paradigms for Next-Generation Wireless Networks

Jiang¹,

Zhang

Ren

et al. 2017

IEEE Wireless Commun.

992

496

View full text Add to dashboard Cite

show abstract

“…In addition, reinforcement learning is also of significant importance in THz communication as the self-organization capability is needed to enable femtocells to autonomously identify the available spectrum and tune their parameters accordingly. Such cells will therefore function under the limits of evading interference and satisfying QoS demands [35].…”

Section: Terahertz Communications For Mobile Hetnetsmentioning

confidence: 99%

Terahertz communication: The opportunities of wireless technology beyond 5G

Elayan

Amin

Shubair

et al. 2018

2018 International Conference on Advanced Communication Technologies and Networking (CommNet)

186

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Abstract-Over the past years, carrier frequencies used for wireless communications have been increasing to meet bandwidth requirements. The engineering community witnessed the development of wide radio bands such as the millimeter-wave (mmW) frequencies to fulfill the explosive growth of mobile data demand and pave the way towards 5G networks. Other research interests have been steered towards optical wireless communication to allow higher data rates, improve physical security and avoid electromagnetic interference. Nevertheless, a paradigm change in the electromagnetic wireless world has been witnessed with the exploitation of the Terahertz (THz) frequency band (0.1-10 THz). With the dawn of THz technology, which fills the gap between radio and optical frequency ranges, ultimate promise is expected for the next generation of wireless networks. In this paper, the light is shed on a number of opportunities associated with the deployment of the THz wireless links. These opportunities offer a plethora of applications to meet the future communication requirements and satisfy the ever increasing user demand of higher data rates.

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“…For instance, in [102], RL has been used for opportunistic spectrum sharing, which achieves good performance without prior knowledge on the environment. To improve the spectral efficiency in heterogeneous networks, a distributed strategy has been proposed in [103] based on RL to reduce both intra-cell and inter-cell interference and improve the throughput under the environment uncertainty. In [104], distributed independent RL based on Q-learning has been used so that only local information at nodes is required and the utility value given a specific task can be optimized.…”

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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Dynamic Heterogeneous Learning Games for Opportunistic Access in LTE-Based Macro/Femtocell Deployments

Cited by 70 publications

References 27 publications

Machine Learning Paradigms for Next-Generation Wireless Networks

Machine Learning Paradigms for Next-Generation Wireless Networks

Terahertz communication: The opportunities of wireless technology beyond 5G

20 Years of Evolution From Cognitive to Intelligent Communications

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