Automated optimization of service coverage and base station antenna configuration in UMTS networks

Siomina, Iana; Värbrand, Peter; Yuan, Di

doi:10.1109/mwc.2006.275194

Cited by 121 publications

(67 citation statements)

References 8 publications

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“…This signaling can be done through X2 interface in LTE or LTE-A; and most importantly it can be done only when location of RS changes. Another advantage of SOT, that makes it more pragmatic, is that due to its highly localised nature it is very agile and can be implemented in autonomous and SO fashion to cope with even mobile RS, as tilts can be changed electronically and remotely [2], [4].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…A significant number of works have embarked on tilt optimization for coverage and capacity enhancement in macro cellular networks [2]- [4]. However to the best of our knowledge, this concept of spectral efficiency enhancement on access link through BS antenna tilt adaptation is novel and the solution presented here is first attempt in this particular direction.…”

Section: Introductionmentioning

confidence: 99%

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Relay Station Access Link Spectral Efficiency Optimization Through SO of Macro BS Tilts

Imran

Tafazolli

2011

IEEE Commun. Lett.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relay Station Access Link Spectral Efficiency Optimization Through SO of Macro BS Tilts

Imran

Tafazolli

2011

IEEE Commun. Lett.

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“…As state of the art antennas allow remote electrical tilting in addition to mechanical tilting, there is a margin for self configuration and even self optimisation of antenna tilt. A significant number of papers in the literature have addressed this issue, most of which have focused on improving the coverage and capacity in general for GSM [38], CDMA [39] and HSDPA [40]. Authors in [41] propose a scheme for antenna tilt angles and power configuration settings from a centralised server that has a global knowledge of all current system settings.…”

Section: ) Propagation Parameter Configurationmentioning

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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“…For instance, local search methods, such as gradient ascent [10] and simulated annealing [11,12], are adopted for radio network planning. In [10], a heuristic variant of the gradient ascent method was adopted to optimize antenna tilt.…”

Section: Related Workmentioning

confidence: 99%

“…In [10], a heuristic variant of the gradient ascent method was adopted to optimize antenna tilt. In [11,12], simulated annealing algorithm was used to control the downlink transmit power. The proposed approaches rely on the accurate downlink interference information in an eNodeb's own and neighboring cells under all possible parameter configurations.…”

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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Automated optimization of service coverage and base station antenna configuration in UMTS networks

Cited by 121 publications

References 8 publications

Relay Station Access Link Spectral Efficiency Optimization Through SO of Macro BS Tilts

Relay Station Access Link Spectral Efficiency Optimization Through SO of Macro BS Tilts

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

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