Vertical Antenna Tilt Optimization for LTE Base Stations

Eckhardt, Harald; Klein, Siegfried; Gruber, Markus

doi:10.1109/vetecs.2011.5956370

Cited by 54 publications

(44 citation statements)

References 10 publications

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“…In the context of cellular systems, heuristics are applied mainly in self-optimization in coverage and capacity optimization [221], [222], and load balancing [223]- [226].…”

Section: F Heuristic Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

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Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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“…In the context of cellular systems, heuristics are applied mainly in self-optimization in coverage and capacity optimization [221], [222], and load balancing [223]- [226].…”

Section: F Heuristic Algorithmsmentioning

confidence: 99%

“…While in [221], Eckhardt et al, propose an algorithm for antenna down-tilt angle optimization in order to optimize the spectral efficiency of users. The approach considered a LTE network scenario and is based on heuristics to find the best antenna parameters.…”

Section: Coverage and Capacitymentioning

confidence: 99%

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

View full text Add to dashboard Cite

show abstract

“…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. In [11,12], simulated annealing algorithm was used to control the downlink transmit power.…”

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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“…Several optimization algorithms based on antenna tilt modifications can be found in the literature. Many of them are used as COC algorithms [16][17][18]. These works present different methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the performance of each strategy is evaluated in three different network states: deployment, normal operation, and cell outage. In [18], a heuristic variant of the gradient ascent method to optimize antenna tilts is presented. The proposed method is capable of compensating a cell outage in an autonomous manner.…”

Section: Introductionmentioning

confidence: 99%

Fault compensation algorithm based on handover margins in LTE networks

de-la-Bandera

Barco

Muñoz

et al. 2016

J Wireless Com Network

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In the context of Self-Organizing Networks (SON), this paper presents a novel cell degradation compensation algorithm based on handover margin modifications. The fault considered in this paper is a weak coverage use case so that the faulty cell will be active during the compensation, but its power will be abnormally lower than it should. Unlike the extensively studied cell outage use case, in this case, the modification of the faulty cell's parameters can be considered as part of the compensation algorithm. In particular, this paper proposes to modify the handover margins for cell degradation compensation. To analyze the proposed algorithm, a set of simulations has been carried out using an LTE simulator with a realistic scenario. Results show that the proposed algorithm is able to improve the degradation caused by the fault with a low effect in the neighboring cells under different conditions.

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Vertical Antenna Tilt Optimization for LTE Base Stations

Cited by 54 publications

References 10 publications

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

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

Fault compensation algorithm based on handover margins in LTE networks

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