Self-optimization of capacity and coverage in LTE networks using a fuzzy reinforcement learning approach

Razavi, Rouzbeh; Klein, Siegfried; Claußen, Holger

doi:10.1109/pimrc.2010.5671622

Cited by 69 publications

(47 citation statements)

References 9 publications

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“…Thus various modified RL schemes are often used. A number of authors have found RL suitable for developing self optimisation algorithms [114], [126] and [127].…”

Section: A Learning Algorithmsmentioning

confidence: 99%

A Survey of Self Organisation in Future Cellular Networks

Aliu

Imran

et al. 2013

IEEE Commun. Surv. Tutorials

379

217

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This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks.

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“…Thus various modified RL schemes are often used. A number of authors have found RL suitable for developing self optimisation algorithms [114], [126] and [127].…”

Section: A Learning Algorithmsmentioning

confidence: 99%

A Survey of Self Organisation in Future Cellular Networks

Aliu

Imran

et al. 2013

IEEE Commun. Surv. Tutorials

379

217

View full text Add to dashboard Cite

show abstract

“…The algorithm stores past successful optimization instances that improved the performance in the memory and applies these instances directly to new situations. In [19][20][21], a fuzzy Q-learning algorithm was used to learn the optimal antenna tilt control policy based on the continuous inputs of current antenna configuration and corresponding performance, and output of the optimized antenna configuration. Yet, the impact on neighboring cells due to such an adjustment was neglected.…”

Section: Related Workmentioning

confidence: 99%

Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning

Fan

Tian

Sengul

2014

J Wireless Com Network

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Self-organization is a key concept in long-term evolution (LTE) systems to reduce capital and operational expenditures (CAPEX and OPEX). Self-optimization of coverage and capacity, which allows the system to periodically and automatically adjust the key radio frequency (RF) parameters through intelligent algorithms, is one of the most important tasks in the context of self-organizing networks (SON). In this paper, we propose self-optimization of antenna tilt and power using a fuzzy neural network optimization based on reinforcement learning (RL-FNN). In our approach, a central control mechanism enables cooperation-based learning by allowing distributed SON entities to share their optimization experience, represented as the parameters of learning method. Specifically, SON entities use cooperative Q-learning and reinforced back-propagation method to acquire and adjust their optimization experience. To evaluate the coverage and capacity performance of RL-FNN, we analyze cell-edge performance and cell-center performance indicators jointly across neighboring cells and specifically consider the difference in load distribution in a given region. The simulation results show that RL-FNN performs significantly better than the best fixed configuration proposed in the literature. Furthermore, this is achieved with significantly lower energy consumption. Finally, since each self-optimization round completes in less than a minute, RL-FNN can meet the need of practical applications of self-optimization in a dynamic environment.

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“…P Blasco et al [20] proposed a learning theoretical approach to energy harvesting communication system optimization, which proved that the transmitter is able to learn the optimal transmission policy that maximizes the expected sum of the data transmitted during the transmitter's lifetime. R Razavi et al [21] introduced a solution to enable the self-optimization of coverage and capacity in LTE (Long Term Evolution networks) based on a fuzzy reinforcement-learning approach. However, learning approaches generally have the property of extremely slow convergence when they have a large number of feasible actions [8].…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of Steering Mechanisms for Articulated Off-Road Vehicles Based on Genetic Algorithms

Zhou

Liu

2018

Algorithms

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Two cylinders arranged symmetrically on a frame have become a major form of steering mechanism for articulated off-road vehicles (AORVs). However, the differences of stroke and arm lead to pressure fluctuation, vibration noise, and a waste of torque. In this paper, the differences of stroke and arm are reduced based on a genetic algorithm (GA). First, the mathematical model of the steering mechanism is put forward. Then, the difference of stroke and arm are optimized using a GA. Finally, a FW50GLwheel loader is used as an example to demonstrate the proposed GA-based optimization method, and its effectiveness is verified by means of automatic dynamic analysis of mechanical systems (ADAMS). The stroke difference of the steering hydraulic cylinders was reduced by 92% and the arm difference reached a decrease of 78% through GA optimization, in comparison with unoptimized structures. The simulation result shows that the steering mechanism optimized by GA behaved better than by previous methods.

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Self-optimization of capacity and coverage in LTE networks using a fuzzy reinforcement learning approach

Cited by 69 publications

References 9 publications

A Survey of Self Organisation in Future Cellular Networks

A Survey of Self Organisation in Future Cellular Networks

Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning

Design Optimization of Steering Mechanisms for Articulated Off-Road Vehicles Based on Genetic Algorithms

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