Field trial evaluation of UE specific antenna downtilt in an LTE downlink

Danneberg, Martin; Holfeld, Joerg; Grieger, Michael; Amro, Mohammed; Fettweis, Gerhard

doi:10.1109/wsa.2012.6181219

Cited by 13 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have realized that a practical antenna can target its antenna beam towards a given direction via downtilt in the vertical domain, which may effect the network performance [9,16,17]. For example, the authors in [9] found that the antenna downtilt could bring a significant improvement to the cellular network capacity via computer simulations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Base Station Antenna Downtilt in Downlink Cellular Networks

Yang

Ding

Mao

et al. 2019

IEEE Trans. Wireless Commun.

129

View full text Add to dashboard Cite

From very recent studies, the area spectral efficiency (ASE) performance of downlink (DL) cellular networks will continuously decrease and finally to zero with the network densification in a fully loaded ultra-dense network (UDN) when the absolute height difference between a base station (BS) antenna and a user equipment (UE) antenna is larger than zero, which is referred as the ASE Crash. We revisit this issue by considering the impact of the BS antenna downtilt on the downlink network capacity. In general, there exists a height difference between a BS and a UE in practical networks. It is common to utilize antenna downtilt to adjust the direction of the vertical antenna pattern, and thus increase received signal power or reduce inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the base station antenna downtilt and the downlink network capacity in terms of the coverage probability and the ASE. The analytical results of the coverage probability and the ASE are derived, and we find that there exists an optimal antenna downtilt to achieve the maximal coverage probability for each base station density. Moreover, we derive numerically solvable expressions for the optimal antenna downtilt, which is a function of the base station density. Our theoretical and numerical results show that after applying the optimal antenna downtilt, the network performance can be improved significantly. Specifically, with the optimal antenna downtilt, the ASE crash can be delayed by nearly one order of magnitude in terms of the base station density.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In [16], the authors showed that the vertical beamforming could increase SIR by about 5-10 dB for a set of UE locations. N. Seifi and M. Coldrey investigated the performance impact of using antenna downtilt in traditional hexagonal 3D cellular networks in [17].…”

Section: Related Workmentioning

confidence: 99%

Optimal Base Station Antenna Downtilt in Downlink Cellular Networks

Yang

Ding

Mao

et al. 2019

IEEE Trans. Wireless Commun.

129

View full text Add to dashboard Cite

show abstract

“…1 or fall below a pre-specified noise floor level and are skipped [7]. RT model is capable of simulating multi-path propagation [7], [15] and [16]. In this work, the RT package Actix RPS 5.5 was used.…”

Section: Introductionmentioning

confidence: 99%

“…RT key parameters are shown in Table I, which were optimized based on iterative trials and believed to be reflecting the real propagation environments and the used hardware Simulated field environment in RT with two rays [15]. RPS 5.5 was used.…”

Section: Introductionmentioning

confidence: 99%

Ray-tracing wireless channel modeling and verification in Coordinated Multi-Point systems

Amro

Landolsi

Zummo

et al. 2014

2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

Self Cite

View full text Add to dashboard Cite

Coordinated Multi-Point (CoMP) Multiple InputMultiple Output (MIMO) transmission improves user's coverage and data throughput particularly on the cell edges. To make full advantage of CoMP, radio planning tools need very accurate models that fully capture the MIMO channels characteristics. This paper presents detailed modeling and analysis of an uplink CoMP system using Ray-Tracing (RT)-based channel modeling. The contribution of this paper is to show how close RT simulations can predict end-to-end system performance compared to the real-world measured performance. Thorough drive test measurements and RT simulations were performed. CoMP and Conventional MIMO systems performances are evaluated and compared for measured and RT-simulated channels. The results of several scenarios show that the RT matches the measurements in terms of rates and geometrical properties. The CoMP gain resulting from the measurements is almost double the gain of RT simulations. The differences come from the hardware and the RT 3D models impairments.

show abstract

“…The antenna tilt directly impacts the performance of the cell served by the BS in terms of network coverage, signal strength and inter-cell interference, and therefore determines the quality of service experienced by end users. In particular, when the antenna tilt is changed in the BS, its effect on the antenna gain over distance also changes, which further leads to a change of the Reference Signals Received Power (RSRP) values [3]. Therefore, different radio maps can be generated as a function of the selected tilt configuration.…”

mentioning

confidence: 99%

Transfer Learning for Tilt-Dependent Radio Map Prediction

Parera

Liao

Malanchini

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

Machine learning will play a major role in handling 1 the complexity of future mobile wireless networks by improving 2 network management and orchestration capabilities. Due to the 3 large number of parameters that can be monitored and config-4 ured in the network, collecting and processing high volumes of 5 data is often unfeasible or too expensive at network runtime, 6 which calls for taking resource management and service orches-7 tration decisions with only a partial view of the network status. 8 Motivated by this fact, this paper proposes a transfer learning 9 framework for reconstructing the radio map corresponding to a 10 target antenna tilt configuration by transferring the knowledge 11 acquired from another tilt configuration of the same antenna, 12 when no or very limited measurements are available from the 13 target. The performance of the framework is validated against 14 standard machine learning techniques on a data set collected 15 from a 4G commercial base stations. In most of the tested scenar-16 ios, the proposed framework achieves notable prediction accuracy 17 with respect to classical machine learning approaches, with a 18 mean absolute percentage error below 8%. 19 Index Terms-Radio map prediction, antenna tilt, transfer 20 learning. 21 I. INTRODUCTION 22 F IFTH generation wireless networks (5G) are expected to 23 improve the performance of cellular systems, achieving 24 higher data rates, reduced latency, higher reliability and sup-25 port for greater numbers of users. To achieve this, 5G resorts 26 to dense and heterogeneous deployments, coupled with higher 27 flexibility in the network access and core domains, which can 28 be dynamically managed in either a centralized or distributed 29 manner. To cope with such a complex scenario, it is foreseen 30 that machine learning tools will play a major role in enabling 31

show abstract

Field trial evaluation of UE specific antenna downtilt in an LTE downlink

Cited by 13 publications

References 11 publications

Optimal Base Station Antenna Downtilt in Downlink Cellular Networks

Optimal Base Station Antenna Downtilt in Downlink Cellular Networks

Ray-tracing wireless channel modeling and verification in Coordinated Multi-Point systems

Transfer Learning for Tilt-Dependent Radio Map Prediction

Contact Info

Product

Resources

About