Smartphone-based measurements of LTE network performance

Avallone, Stefano; Pasquino, Nicola; Zinno, Stefania; Casillo, Domenico

doi:10.1109/i2mtc.2017.7969854

Cited by 9 publications

(10 citation statements)

References 18 publications

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“…The observed values for CRS-SNR were found to lie within the interval −20 dB to 30 dB. Although the range for this parameter is not defined in the standard, measurements performed in other studies such as in [5] and [6] agree with these values.…”

Section: Collected Datasupporting

confidence: 57%

“…Finally, CRS-SNR values falling out of the normal operating range were discarded. For this purpose, the results in [5] and [6] were used as a reference since the standard does not define this range. Outliers in CRS-SNR values were likely logged during handovers between LTE and other radio access technologies.…”

Section: Filtering Methodsmentioning

confidence: 99%

“…A more recent study can be found in [6], where an LTE-Advanced (LTE-A) network was evaluated under different load conditions and at a lower speed (50 km/h) than the ones considered in [4] and [5]. In the study, channel quality and throughput were assessed while carrier aggregation (an LTE-A feature) was implemented with two carriers of 10 MHz and 15 MHz.…”

Section: Related Workmentioning

confidence: 99%

“…One method to collect such data is to make use of application-specific systems (i.e., hardware and software) in extensive measurement campaigns. This is the approach that was used in available studies [4][5][6][7][8][9][10]. In an alternative way, commonly used smartphones can also be used to collect measurements in a distributed and cooperative manner.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling and analysis of LTE connectivity in a high mobility vehicular environment

Burbano-Abril

Edwards

Rangel-Licea

et al. 2018

Computers & Electrical Engineering

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Long-Term Evolution (LTE) technology has several features that make it an attractive alternative to be used in vehicle-to-infrastructure communications for intelligent transportation systems. However, before LTE can be widely used in this context, a number of analysis must provide convincing evidence that critical network functions (e.g. resource allocation strategies) yield adequate performance. To this end, in this work, we introduce a Markov-chain based model for LTE downlink channel quality, a prime factor affecting performance. Our model comes from the analysis of a large number of measurements of LTE Cell-Specific Reference Signals that were collected through a crowdsourcing application on a motorway in the UK. The model is intended to be used in performance evaluation studies and we exemplify its use with a case study, where we estimate the downlink transmission capacity of an LTE network. We also discuss other potential applications.

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Section: Collected Datasupporting

confidence: 57%

Section: Filtering Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and analysis of LTE connectivity in a high mobility vehicular environment

Burbano-Abril

Edwards

Rangel-Licea

et al. 2018

Computers & Electrical Engineering

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“…The LTE dongle used single-carrier FDMA (SC-FDMA) in the uplink direction. When the channel state is evaluated several factors such as SINR, RSRP and throughput indicate the state [29]. During the measurements Key Performance Indicator (KPI) values such as media access control (MAC) throughput, reference signal received power (RSRP), transmit (TX) power, reference signal received quality (RSRQ) and signal-to-noise ratio (SNR) were gathered.…”

Section: Measurement Toolsmentioning

confidence: 99%

Field Measurement for Antenna Configuration Comparison in Challenging NLOS Locations

Heikkilä

Erkkila

Tervonen

et al. 2018

IEEE Trans. Instrum. Meas.

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This article has two main objectives. First, it describes the practical challenges of field trials and proposes a developed test method. Secondly, the test method is used to compare uplink performance with different antenna technologies when user equipment does not have a line of sight to the evolved Node B. Both passive and active antenna configurations were used in the performance evaluation. Modern cellular networks have high demands for capacity, reliability, and availability. The verification of a network's configuration and technological features is essential to guarantee network performance, and the performance of a network must be verified by laboratory testing or field trials; such trials produce experimental knowledge of technology features and configurations. Technological and environmental factors must also be considered before performing mobile network field-testing. Our work showed that moving user equipment produces more reliable and repeatable results than measurements with stationary user equipment. Our antenna configuration comparison study revealed that in the uplink direction, active antenna system beam control could significantly increase the uplink capacity in non-line-of-sight conditions. Index Terms-2-and 4-way RX diversity, AAS, field trial, horizontal beamforming, non-line-of-sight environment, MIMO, uplink capacity improvement, vertical beamforming.

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ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

Glinskiy,

Kureev,

Khorov

2024

IEEE Access

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A key feature of 5G systems is the Ultra-Reliable Low-Latency Communication (URLLC), which can be used for remote surgery, smart grids, industrial control, etc. URLLC requires millisecondlevel delays and very high reliability, i.e., less than 10 −5 packet loss probability. The ability to satisfy these very strict quality of service requirements depends on selecting the Modulation and Coding Schemes (MCS) for data transmissions. On the one hand, the selected MCS shall be robust enough to avoid multiple retransmissions within a small delay budget. On the other hand, the MCS shall be high-rate to reduce channel resource consumption and, thus, shall increase the system capacity for URLLC. The MCS selection problem is extremely challenging to capture the quickly varying wireless channel effects, e.g., in highly mobile scenarios, because the decision shall be made long before the actual transmission occurs. The paper proposes a novel MCS selection algorithm called ALPACA (Asymmetric Loss Prediction Algorithm for Channel Adaptation), which relies on a widely used class of convolutional-recurrent neural networks. However, in contrast to existing approaches, ALPACA explicitly considers the asymmetric error cost for channel prediction by utilizing quantile regression loss. Both real-life channel measurements and 3GPP channel models are used to evaluate the performance of ALPACA. Numerical results demonstrate the increase in the reliability and reduction in resource consumption compared with the existing MCS selection algorithms, which results in 40% growth of the network capacity.

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Smartphone-based measurements of LTE network performance

Cited by 9 publications

References 18 publications

Modeling and analysis of LTE connectivity in a high mobility vehicular environment

Modeling and analysis of LTE connectivity in a high mobility vehicular environment

Field Measurement for Antenna Configuration Comparison in Challenging NLOS Locations

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

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