Smartphones Reference Signal Received Power MDT Radio Measurement Statistical Analysis Reveals People Feelings during Music Events

Micheli, Davide; Muratore, Giuliano

doi:10.1109/piers-spring46901.2019.9017606

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Other uses of MDT reports also include analysis of solar radiations and their impact on RAN interference [12], hotspot detection and network capacity upgrades identification In summary, measures from MDT-capable devices (jointly with global/country-level traffic trends and network counters) could offer opportunities for data-driven optimizations and network traffic insights. However, even if in most of the cases MDT accuracy is satisfactory for the use cases described above, it is important to notice that the variability of RSRP measures leads sometimes to difficulty in building accurate models able to predict future signal values [16]. In such big-data contests, we expect MDT performance advances in the future, where AI is expected to play a role in extracting insights from measures to learn and make QoS predictions [17,18].…”

Section: Data Analysis From Operator Networkmentioning

confidence: 99%

The Power of Data: How Traffic Demand and Data Analytics Are Driving Network Evolution toward 6G Systems

Sabella¹,

Micheli

Nardini

2023

JSAN

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The evolution of communication systems always follows data traffic evolution and further influences innovations that are unlocking new markets and services. While 5G deployment is still ongoing in various countries, data-driven considerations (extracted from forecasts at the macroscopic level, detailed analysis of live network traffic patterns, and specific measures from terminals) can conveniently feed insights suitable for many purposes (B2B e.g., operator planning and network management; plus also B2C e.g., smarter applications and AI-aided services) in the view of future 6G systems. Moreover, technology trends from standards and research projects (such as Hexa-X) are moving with industry efforts on this evolution. This paper shows the importance of data-driven insights, by first exploring network evolution across the years from a data point of view, and then by using global traffic forecasts complemented by data traffic extractions from a live 5G operator network (statistical network counters and measures from terminals) to draw some considerations on the possible evolution toward 6G. It finally presents a concrete case study showing how data collected from the live network can be exploited to help the design of AI operations and feed QoS predictions.

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Section: Data Analysis From Operator Networkmentioning

confidence: 99%

The Power of Data: How Traffic Demand and Data Analytics Are Driving Network Evolution toward 6G Systems

Sabella¹,

Micheli

Nardini

2023

JSAN

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“…Minimization of Drive Tests (MDT) is a feature introduced in 3GPP Release 10 [7] that enables operators to utilize users' equipment (UE) to collect radio measurements and associated location information [8] (e.g., via GPS or WLAN localization (Release 16)). Collected MDT measurement reports can be aggregated into datasets with user location information (ULI) as well as statistical channel state information, providing a realistic description of the network state and allowing network troubleshooting and inference of human behavior-related statistics [9][10][11]. With this contribution, the authors aim to shed light on the potential of MDT data-driven algorithms for network automation.…”

Section: Introductionmentioning

confidence: 99%

Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning

Skocaj¹,

Amorosa²,

Ghinamo³

et al. 2022

Preprint

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Recent years witnessed a remarkable increase in the availability of data and computing resources in communication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent's sample efficiency, stability and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-first search in terms of long-term reward and sample efficiency. Our results indicate that MDT-driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks.

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“…This article, for the rst time, studies the solar radio disturbance of LTE services by means of innovative 3GPP [4] MDT (Minimization of Drive Test) standard radio measures and the richness of the R language environment for data elaborations [5]. The MDT [6][7][8][9][10][11][12][13] standard allows the mobile network operator (MNO) to collect georeferenced radio measurements anonymously and free of charge reported by mobile phones to the network, measurements including radioreceived BS cell power levels, interference levels and quality of radio links at the frequencies of mobile network operators serving BS cells. In this way, a huge amount of periodic radio measurements related to the territory around BS cells can be analysed with data science methods to extract statistical information on reference signal received power (RSRP) and interference signal to interference ratio (SIR) uctuation.…”

Section: Introductionmentioning

confidence: 99%

Solar Radio Emission as A Disturbance of Radiomobile Networks

Muratore

Giannini

Micheli

2022

Preprint

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This paper analyses the effects of solar radio emissions in the radiomobile context, for the first time leveraging massive ETSI (European Telecommunications Standards Institute) 3GPP (3rd Generation Partnership Program) MDT (Minimization of Drive Test) radio measures produced by 4G LTE (Long Term Evolution) terminals and by 4G LTE BS (Base Station) cells. A method to study solar noise effects starting from radiomobile 3GPP standard MDT measures is illustrated and correlated with the excellent 10.7 cm (2800 MHz) indicator of solar activity (National Research Council Canada). The effects of solar disturbance on the LTE radio access network (RAN) for mobile services are analysed, and possible countermeasures are presented from the perspective of radiomobile network evolution to 5G and 6G.

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Smartphones Reference Signal Received Power MDT Radio Measurement Statistical Analysis Reveals People Feelings during Music Events

Cited by 6 publications

References 2 publications

The Power of Data: How Traffic Demand and Data Analytics Are Driving Network Evolution toward 6G Systems

The Power of Data: How Traffic Demand and Data Analytics Are Driving Network Evolution toward 6G Systems

Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning

Solar Radio Emission as A Disturbance of Radiomobile Networks

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