Output Power Levels of 4G User Equipment and Implications on Realistic RF EMF Exposure Assessments

Joshi, Paramananda; Colombi, Davide; Thors, Björn; Larsson, Lars-Eric; Törnevik, Christer

doi:10.1109/access.2017.2682422

Cited by 61 publications

(51 citation statements)

References 9 publications

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“…The constant c B in the path loss formula is chosen so as to obtain an average SNR of 3 dB for eMBB at the reference distance d B,R = d B for both UL and DL with transmission powers P B in the UL and P in the DL [47]. The constant c U is instead chosen so as to obtain an average URLLC SNR equal to 10 dB at the reference distance d U,R = d U for both UL and DL, with transmission powers P U = 23 dBm for both UL and DL [47]. Finally, unless stated otherwise, we assume throughout this section the values C = 2 bit/s/Hz, γ = 3, a U = 0.5 × 10 −3 and L U = 2 for H-OMA.…”

Section: A Simulations Set-upmentioning

confidence: 99%

Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures

et al. 2019

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The fifth generation (5G) of cellular systems is introducing Ultra-Reliable Low-Latency Communications (URLLC) services alongside more conventional enhanced Mobile BroadBand (eMBB) traffic. Furthermore, the 5G cellular architecture is evolving from a base station-centric deployment to a foglike set-up that accommodates a flexible functional split between cloud and edge. In this paper, a novel solution is proposed that enables the non-orthogonal coexistence of URLLC and eMBB services by processing URLLC traffic at the Edge Nodes (ENs), while eMBB communications are handled centrally at a cloud processor as in a Cloud-Radio Access Network (C-RAN) system. This solution guarantees the low-latency requirements of the URLLC service by means of edge processing, e.g., for vehicle-to-cellular use cases, as well as the high spectral efficiency for eMBB traffic via centralized baseband processing.Both uplink and downlink are analyzed by accounting for the heterogeneous performance requirements of eMBB and URLLC traffic and by considering practical aspects such as fading, lack of channel state information for URLLC transmitters, rate adaptation for eMBB transmitters, finite fronthaul capacity, and different coexistence strategies, such as puncturing.

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Section: A Simulations Set-upmentioning

confidence: 99%

Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures

et al. 2019

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“…Transitions between the states are expressed as service rates and arrival rates, which also depend on the radio conditions. However, the model strongly depends on the distribution of required TX-power, which can be obtained either by ray-tracing simulations or from empirical data, such as [9]. While CoPoMo is perfectly suited to provide power estimations in offline simulations, an online application is prevented in most cases by the lack of knowledge about the instant TX-power at higher protocol layers.…”

Section: Related Workmentioning

confidence: 99%

“…For comparison, we also included three distributions from [9], which reflect the TX-power distributions of different environments in Sweden. That data was obtained from a large set of base stations with the support of a mobile network operator [9]. Our measurements match the distribution for urban environments from the reference set, although our the trajectory also includes suburban and rural areas.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks Using Passive Downlink Indicators

Falkenberg

Sliwa

Piatkowski

et al. 2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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Energy-aware system design is an important optimization task for static and mobile Internet of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles such as mobile robotic systems. For 4G/5G-based cellular communication systems, the effective transmission power of uplink data transmissions is of crucial importance for the overall system power consumption. Unfortunately, this information is usually hidden within off-the-shelf modems and mobile handsets and can therefore not be exploited for enabling green communication. Moreover, the dynamic transmission power control behavior of the mobile device is not even explicitly modeled in most of the established simulation frameworks. In this paper, we present a novel machine learning-based approach for forecasting the resulting uplink transmission power used for data transmissions based on the available passive network quality indicators and application-level information. The model is derived from comprehensive field measurements of drive tests performed in a public cellular network and can be parameterized for integrating all measurements a given target platform is able to provide into the prediction process. In a comparison of three different machine learning methods, Random-Forest models thoroughly performed best with a mean average error of 3.166 dB. As the absolute sum of errors converges towards zero and falls below 1 dB after 28 predictions in average, the approach is well-suited for long-term power estimations.

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“…Only a few studies have been conducted to assess LTE UE output power distributions under realistic exposure scenarios [9,10]. Moreover, authors found no studies of comparative UMTS and LTE analysis of UE power level distributions in everyday exposure situations.…”

Section: Introductionmentioning

confidence: 99%

“…In [9] authors present a series of uplink power measurements without targeting human exposure issues but rather for the purpose of deriving a power model of a commercial LTE network and comparing between 3G and LTE network power efficiency. Authors in [10] present result about power level distributions of 4G user equipment UE using data applications based on a very large number of samples collected over seven days in a LTE operating network. Their findings show that the mean output powers in all the environments was less than 1% of the maximum available output power and demonstrate once again that knowledge on realistic power levels is of crucial importance for accurate assessments of the radio frequency electromagnetic field exposure from mobile communication equipment.…”

Section: Introductionmentioning

confidence: 99%

Mobile Phone User Exposure Assessment to UMTS and LTE Signals at Mobile Data Turn on by Applying an Original Method

Sârbu

Bechet

Miclăuș

2017

International Conference KNOWLEDGE-BASED ORGANIZATION

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Output Power Levels of 4G User Equipment and Implications on Realistic RF EMF Exposure Assessments

Cited by 61 publications

References 9 publications

Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures

Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures

Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks Using Passive Downlink Indicators

Mobile Phone User Exposure Assessment to UMTS and LTE Signals at Mobile Data Turn on by Applying an Original Method

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