Dynamic Self-Optimization of the Antenna Tilt for Best Trade-off Between Coverage and Capacity in Mobile Networks

Dandanov, Nikolay; Al-Shatri, Hussein; Klein, Anja; Poulkov, Vladimir

doi:10.1007/s11277-016-3849-9

Cited by 62 publications

(34 citation statements)

References 8 publications

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“…In the operational stage, self-optimization (a.k.a. selftuning) schemes [5], [7], [14], [39]- [45] take advantage of live measurements to dynamically adapt network parameters to changing network conditions. For this purpose, a controller iteratively modifies network parameters based on continuous performance measurements (e.g., cell load or inter-cell interference) without the need for a network model.…”

Section: Related Workmentioning

confidence: 99%

“…For this purpose, a controller iteratively modifies network parameters based on continuous performance measurements (e.g., cell load or inter-cell interference) without the need for a network model. The controller can be an equation solver [5], [39], a local search algorithm [7], [40], a heuristic rule-based controller [14], [41] or an adaptive controller adjusted by reinforcement learning [42]- [45]. These can be implemented as a centralized entity to reduce communication overhead or as a distributed entity to share computational load among base stations.…”

Section: Related Workmentioning

confidence: 99%

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A Computationally Efficient Method for QoE-Driven Self-Planning of Antenna Tilts in a LTE Network

2020

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In future mobile communications systems, network management procedures must be upgraded to consider user quality of experience (QoE) to deal with service diversity. In this work, a computationally efficient centralized method for determining the best configuration of antenna tilts when planning a LTE network is presented. Unlike previous network-centric methods, the proposed self-planning method is driven by QoE criteria. The core of the method is the grouping of cells into clusters without mutual interference, which speeds up the search for the optimal solution with a classical steepest descent algorithm. Method assessment is carried out in a static system-level simulator adjusted with real connection traces. For this purpose, traffic demand in the scenario is broken down per location and service to estimate the QoE obtained by each antenna tilt plan. During the analysis, the method is compared with legacy tilt planning approaches. Results show that the proposed method achieves a near-optimal solution for the overall system QoE with a computational cost lower than state-of-the art algorithms previously reported in the literature.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Computationally Efficient Method for QoE-Driven Self-Planning of Antenna Tilts in a LTE Network

2020

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“…Results on simulated data demonstrate that the proposed method outperforms a fixed strategy for the antenna tilts. A similar approach is taken by the authors of [4], where reinforcement learning is again used to optimize the antenna tilts, this time with the objective of maximizing the overall data rate of the network. Finally, the work in [5] proposes a general machine learning-based network planning tool.…”

Section: Related Workmentioning

confidence: 99%

Transferring knowledge for tilt-dependent radio map prediction

Parera

Redondi

Cesana

et al. 2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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Fifth generation wireless networks (5G) will face key challenges caused by diverse patterns of traffic demands and massive deployment of heterogeneous access points. In order to handle this complexity, machine learning techniques are expected to play a major role. However, due to the large space of parameters related to network optimization, collecting data to train models for all possible network configurations can be prohibitive. In this paper, we analyze the possibility of performing a knowledge transfer, in which a machine learning model trained on a particular network configuration is used to predict a quantity of interest in a new, unknown setting. We focus on the tilt-dependent received signal strength maps as quantities of interest and we analyze two cases where the knowledge acquired for a particular antenna tilt setting is transferred to (i) a different tilt configuration of the same antenna or (ii) a different antenna with the same tilt configuration. Promising results supporting knowledge transfer are obtained through extensive experiments conducted using different machine learning models on a real dataset.

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“…The authors of [85] focus on the problem of self-optimization in the LTE network environment by adjusting antenna tilt and show that this approach is more efficient than supervised learning and Q-learning algorithms in terms of self-healing performance and multiple coverage problems. The authors in [86] add dynamic and adaptive antenna tilt adjustment for the best trade-off between coverage and capacity in mobile networks. This approach is based on RL methodology with low computational complexity for distributed real-time operation.…”

Section: Antenna Tilt Approach (Directional Antenna)mentioning

confidence: 99%

Interference Management Techniques in Small Cells Overlaid Heterogeneous Cellular Networks

Samal

2018

JMM

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Now-a-days because of the rapid increase in mobile users mobile data has also raised. Due to the heavy demand of data rates for advanced applications service providers are forced to adopt different technological advancements in conventional networks. Looking toward this context heterogeneous networks (HetNets) play a vital role in future 5G wireless cellular network deployment. In Heterogeneous Cellular Networks (HCNs) low power small cells are overlaid with the existing macro-only cell which increases the complexity of the network. Some of the emerging network technologies for this cellular evolution are the Femtocell, Picocell and Metrocell networks. Due to the deployment of these small cells in HCNs there are wide varieties of network structures and different transmitting powers which lead to interference in the cellular network. In addition to this, due to the spatiotemporal distribution of the mobile users and their mobility, hotspots also lead to increase of the possibilities of interference generation in wireless cellular network systems. The major technical issues associated with the mass deployment of smaller cells are related to the interference management between smallcells i.e. Femtocells and other serving cells in the same spectrum. Since Femtocells and Macrocells share the same frequency bands, it is necessary to develop efficient interference management techniques to increase the capacity and throughput of HCNs. So in order to meet the user demands in an efficient way

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Dynamic Self-Optimization of the Antenna Tilt for Best Trade-off Between Coverage and Capacity in Mobile Networks

Cited by 62 publications

References 8 publications

A Computationally Efficient Method for QoE-Driven Self-Planning of Antenna Tilts in a LTE Network

A Computationally Efficient Method for QoE-Driven Self-Planning of Antenna Tilts in a LTE Network

Transferring knowledge for tilt-dependent radio map prediction

Interference Management Techniques in Small Cells Overlaid Heterogeneous Cellular Networks

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