Jason Coder Adam Wunderlich

NIST/NASCTN NIST/NASCTN

jason.coder@nist.gov

adam.wunderlich@nist.gov

NASCTN Project Out-brief

Characterizing User Equipment Emissions – Sponsored 

by DSO

May 13th, 2021

https://www.nist.gov/ctl/nasctn
• Please Mute all connections

• Submit Questions in the Chat

▫ Questions will be addressed after all major components

• To be called upon by the moderator please raise hand

Audience Instructions

2
• Project Overview 

▫ Background & Problem Statement

• Part I: Factor Screening (TN 2069) ▫ Identification of Factors and Testbed Setup

▫ Design of Experiment

▫ Statistical Analysis & Results

▫ Engineering Interpretation

• Part II: Closed-Loop Power Control (TN 2147) ▫ Background, objective

▫ Measurements

▫ Statistical Analysis

• Engineering Analysis

▫ UE Antenna Pattern Measurements (TN 2056)

▫ UE Measured vs. Reported Power

▫ Scheduling Dynamics in Negative Power Headroom

▫ UE’s Measurement of Path Loss 

• Conclusions and Q&A

▫ (30 minutes)

Outline

Note: clickable links to reports included above 3
National Advanced Spectrum and Communications 

Test Network (NASCTN)

Established in 2015 by NIST, the U.S. 

DoD, and NTIA. In 2018, added 

NOAA, NSF, and NASA.

Organizes a national network of 

federal, academic, and commercial 

test facilities

Provides trusted spectrum testing, 

modeling, and analysis to develop 

and deploy spectrum-sharing 

technologies and inform future 

spectrum policy and regulations.
NASCTN MISSION

Develop scientifically rigorous test 

plans and new methodologies with 

independent experts

Access to key test facilities, and 

commercial and federal equipment 

and capabilities

Provide validated data and models 

for use within the spectrum sharing 

community

To provide, through its members,

robust test processes and validated

measurement data necessary to

develop, evaluate and deploy

spectrum sharing technologiesthat

can improve access to the spectrum

by both federal agencies and nonfederalspectrum users.

Operates as a trusted agent and protect proprietary, sensitive, and classified information
Test Team and Collaborators

6

Adam Wunderlich*

Technical Lead

Aric Sanders*

Data Analysis

Paul Blanchard

Data Parsing

Max Lees

Data Analysis

Michael Frey*, Jolene Splett, Lucas Koepke

Statistics

Testbed

Jason Coder*

Technical Lead 

Dan Kuester

Data Acquisition and Testbed Automation

Aziz Kord

LTE Engineering

John Ladbury

RF Metrology and Uncertainty Characterization

Rob Horansky*

Radiation Pattern Measurements

Duncan McGillivray

Final Testbed Construction

Shane Allman

Testbed Automation

Experimental Design, Data Processing & Analysis

Melissa Midzor*, Matt Briel, Amanda Hyman, Fabio Da Silva, 

Michael Janezic, Linda Derr – NIST; Joe Mruk, Keith Hartley, 

Mark Lofquist - MITRE 

Jeff Correia, Venki Ramaswamy - MITRE

JD Fevold, Shawn Lefebre, Jacob Johnson - MITRE

Arnab Das – JHU APL

Programmatic Support

External Technical Experts

*Speaker
• Design, demonstrate, & validate a test methodology to measure LTE UE 

emissions for use in aggregate interference calculations. 

▫ Key Elements:

 Collect measurements in a controlled laboratory setting 

 Control or mitigate uncontrolled variables present in field measurements

 Test a wide range of network configurations/morphologies

 Rigorous uncertainty assessment and statistical analyses

 Ensure the results are repeatable

Project Overview - Objective

7
• Develop a better understanding of LTE UE emissions for use in aggregate interference calculations.

▫ Ideally, everyone would like a perfect, predictive model that explains emissions in a variety of circumstances. 

Not easy!

• NASCTN’s approach: Controlled measurement of LTE equipment emissions in a laboratory 

environment 

▫ Cover a wide range of network configurations/morphologies 

▫ Publish the measurement method, data, and results

• Specific case

▫ AWS-3 Frequency Band Auction in 2015. ~$41B in net proceeds. Coordination with incumbent users required 

in certain geographic areas.

 Uplink: 1710 MHz – 1780 MHz

 Downlink: 2110 MHz – 2200 MHz

• Project divided into two phases:

▫ Factor Screening: “What factors influence the amount of energy a UE radiates?”

▫ Closed-Loop Power Control (CLPC): “Assuming CLPC is used, how well can we describe emissions behavior?”

8

Background
9

Project Deliverables

Primary

1. Distribution of EIRP from a UE in an active resource block, over an appropriate 

range of path loss values, UE settings, and LTE network settings 

2. Comparison of UE-reported and measured power distributions 

3. UE beam pattern measurements and TRP calculations

Secondary

1. Engineering Analysis and Interpretation

a) UE’s measurement of path loss

b) Scheduling dynamics

2. Ideas for future measurements (both laboratory and in-field)
Part I: Factor Screening

10

What factors influence the amount of energy a UE radiates?
• Brainstorm factors that may 

impact UE uplink emissions ▫ Based on LTE expertise, prior 

literature, and public comment

• Which factors have a 

statistically significant impact? ▫ “Engineering judgement” may 

be necessary in some cases

• 28 total factors: 

8 non-eNB, 20 eNB

11

Factor Selection
Cell B UEs

Cell A UEs

DUT UE

Legend

- Cell A UE

- Cell B UE 

- DUT UE Locations

12

Measurement Concept

Interference 

from adjacent cell

1) Cell A and Cell B are loaded with UEs

2) Cell A UEs load eNB scheduler

3) Cell B UEs increase noise at eNB

At different positions of DUT UE

1) Measure DUT UE emitted power

2) Measure DUT UE emitted spectrum

3) ...and many other parameters for 

analysis, error checking, and 

troubleshooting

DUT UE = Commercial-off-the-shelf phone
13

Measurement System
• Collecting, parsing and synchronizing data from three sources:

1) Vector Signal Analyzer (VSA) Spectrograms

 1 ms time-resolution – Raw spectrograms processed to remove noise and blurring

 Two consecutive 5 second captures for each test configuration

 Power in each physical resource block (PRB)

2) UE Traffic Generator (UTG) logs 

 0.5 sec time-resolution

 Number of UEs signaled per transmit time interval (TTI), distribution of PRB allocations across loading UEs

3) UE diagnostic software logs

 1 ms time-resolution

 Active PRBs (PUSCH, PUCCH, SRS), UE-Reported Tx Power, Power Headroom Report (PHR), Modulation and Coding Scheme 

(MCS) Index, Buffer Status Report (BSR), ...

• Data used for test verification and deliverables

14

Data Sources
Factor Screening:

- Design of Experiment -

15

Speaker: Adam Wunderlich
Factor List

16

Abbreviations: 

DUT = Device Under Test

UE = User Equipment (cell phone)

UTG = UE Traffic Generator

eNB = evolved node B (base station)

QCI = quality of service class ID

PUSCH = uplink shared channel

PUCCH = uplink control channel

SRS = Sounding reference signal
• 28 factors: 8 non-eNB and 20 for the eNB. Two 3-level factors

• Constraints on factors (I, J, L, M, X) – scheduler & SRS

▫ not all combinations of eNB settings are possible

• Design Goals: 

▫ Minimize number of eNB factor changes

▫ Ensure that main effects are not confounded by other main effects

 i.e., estimates of main effects are uncorrelated

Design Considerations

17
• 32-run design for eNB factors crossed with a 

32-run for non-eNB factors

▫ eNB design: resolution III orthogonal array 

 (I, J, L, M, X) combined into one 16-level factor

▫ Non-eNB design: resolution IV fractional factorial 

• To minimize eNB factor changes, change eNB

and non-eNB factors in two nested loops

▫ Outer loop: 32 eNB configurations

▫ Inner loop: 32 non-eNB configurations

• Known as a “split-plot” design in the 

experimental design literature

Experimental Design Overview

18

eNB subplot 1

non-eNB runs

eNB subplot 2

non-eNB runs

...
• Randomization

▫ Test the 32 eNB configurations in random order 

▫ Test each block of 32 non-eNB configurations in random order

• Physically change DUT UE every 4 eNB configs

▫ Interchange with another UE of same model (2 UEs of same model used)

▫ Enables estimates of variability across test conditions

• Test a reference eNB configuration every day

▫ Provides a baseline that can be tracked with time

▫ Same UEs used for Rounds 1 & 3 and 2 & 4, respectively

• Valid test time for full design + baselines ≈ 54 hours

• Repeat 4 times to maximize chance of conclusive findings

▫ Total valid test time ≈ 216 hours

Implementation Details

19
Factor Screening Experiment

- Statistical Analysis & Results -

20

Speaker: Mike Frey
21

Factor screening experiment

Purpose: Vary experiment factors among their different levels 

to discover which factors have statistically discernible, 

important effects on a measured variable called the response.

• The response, measured PUSCH power, is a distribution; we 

represent it by its 99 percentiles CC1, CC2, ... , CC99 and work with

CC50, CCSS = CC95 − CC5, CCQQ = (CC95−CC50) − (CC50−CC5).

• 22 two-level factors plus 1 sixteen-level factor Ω.

• The factor Ω is a combination of 5 two- and three-level factors.
22

What is a p-value?

A p-value is the probability, under a null condition, that a test 

statistic is more extreme than observed in experiment.

Intuition: A p-value is the strength of the evidence that an 

observed factor effect is not just random variation.

Range: A p-value is a number between 0 and 1.

Caution: The smaller the p-value, the stronger the evidence

for a discernible factor effect.

Decision rule: P-value ≤ αα = 0.05 ⟹ factor has a discernible 

effect on the response. αα is called the significance level;

it is our standard of evidence.
23

ANOVA and MANOVA p-values for factor effects
24

The sixteen-level factor Ω is significant

(statistically significant ⟺ p-value ≤ αα ⟺ some discernible effect)

Permutation-based

Multivariate Tukey 

Analysis

Changes between 

levels of Ω in the 

same group have

no statistically 

discernible effect.
Factor List

25

Abbreviations: 

DUT = Device Under Test

UE = User Equipment (cell phone)

UTG = UE Traffic Generator

eNB = evolved node B (base station)

QCI = quality of service class ID

PUSCH = uplink shared channel

PUCCH = uplink control channel

SRS = Sounding reference signal

No discernable 

statistically significant 

impact on PUSCH 

power in mean, 

spread, or skew
Factor Screening Experiment

- Engineering Analysis -

26

Speaker: Jason Coder
• Almost all factors shown to be significant in at least one dimension

▫ How does that statistical significance translate into practical significance?

▫ How likely is it that some of these features are actually used?

▫ How large of a change in median, spread, or skew is necessary before a factor needs to 

be accounted for in modeling/simulation/analysis?

• We have done a limited number of experiments to confirm the results of the 

analysis

▫ Results indicate good agreement

with the analysis...the results are 

real

▫ There may be false alarms 

and/or missed detections in the 

analysis....and other unknowns

27

What are the practical implications?
• What is the influence of second order effects? 

▫ Additional side experiments indicate that some of these factors may be 

influenced by others

 Open/Closed loop power control – when closed loop power control is activated, effect 

sizes may be different

 Path loss – effect sizes may scale with path loss – in combination with other factors

• How applicable are these results to other UEs? 

▫ Initial tests indicate that other UEs of the same generation may behave the same 

way

 Older UEs may not have newer features implemented – thus they don’t respond

• How applicable are these results to other eNBs?

• What eNB configuration are carriers actually using? 

28

Unknowns
29

Empirical CDFs for all Factor Screening Tests 
30

Empirical CDFs – Closed Loop vs. Open 

Loop Power Control

• Closed loop power control is much more tightly grouped

• May be easier to describe mathematically
Part II: Closed-Loop Power Control

31

When Closed-Loop Power Control is enabled, what factors

influence UE emissions? How well can we describe UE behavior?

Speaker: Jason Coder
• Part I: Ideas for future work

▫ 14 ideas in tech note

• What smaller set of experiments will 

have a large impact? 

• Emissions with closed loop power 

control enabled appear to be much 

more predictable

▫ What factors are significant in this case? 

• Difference from Factor Screening:

▫ Focus on realistic conditions

Part II: Closed-Loop Power Control

32
Objective: Characterize PUSCH power variations in closed-loop mode over a range of realistic 

conditions. 

Description: For closed-loop power control, investigate how PUSCH emissions are impacted 

over a range of realistic settings for path loss and P0. Evaluate magnitude of possible factor 

interactions. (Not possible in factor screening experiment)

Risk/Reward: Low risk/High reward. If closed loop power control is predominantly used, this 

experiment would clarify the variability that can be expected under realistic conditions. 

Type of Experiment: Confirmation/Characterization

Bonus: Additional insight into information on UE behavior (measured vs. reported values)

Closed-Loop Power Control: Objective

33
Test Circuit

34

• eNB Cell B traffic 

imposing on UTG Cell A

• In response to issues in 

factor screening, 

different approach to 

cross-talk
Closed-Loop Power Control

- Experimental Design -

35

Speaker: Adam Wunderlich
Two complementary experiments executed in parallel: modeling and monitoring

Modeling Experiment

Primary Objective: Develop descriptive model(s) for PUSCH EIRP per PRB under 

closed-loop power control over range of realistic conditions.

Secondary Objectives: Assess negative power headroom conditions and 

differences between measured and UE-reported power.

Monitoring Experiment

Objectives: Assess testbed stability, gauge the impact of different UEs, and 

explore negative power headroom in fuller detail than in the modeling 

experiment.

Experimental Design Overview

36
Modeling Experiment 

37

• Six factors: 3 non-eNB and 3 eNB

• Execute four rounds of testing, where each round uses a split-plot design

▫ 12 configurations for eNB factors

▫ 48 configurations for non-eNB factors 

▫ Full factorial designs for eNB and non-eNB factors All factor interactions resolved

• To test more settings of P0, use staggered values for Rounds 1&3 and 2&4 

• Test four copies of same UE model, different phone used for each round
Monitoring Experiment 

38

• Fixed “default” eNB configuration

• Three non-eNB factors: Path Loss, Crosstalk, Offered Load

•Increase number of Path Loss levels from 8 to 12 to better characterize 

emissions in negative power headroom conditions

 12x2x3= 72 non-eNB configurations 

• Monitoring design retested periodically during Modeling Experiment

▫ Repeated at start of every block of 4 eNB configurations and before any retests

 Fourteen repetitions executed over the course of testing

▫ Four UEs changed systematically between replications in the following order: 

1,2,3,4,1,2,3,4,1,2,3,4,2,3
Settings & Implementation Details

39

• Path loss only adjusted for DUT UE

o Additional PL values from 0 dB to 45 dB yield DUT 

RSRP from -72 dBm to -117 dBm

o Loading UEs have constant RSRP (-95 dBm)

• Constant data rate for all UEs (500 kbps)

• Offered Load: adjust number of loading UEs (4, 8, 16)

• Cross-talk implemented with variable 

attenuator and number of UEs in cell B

o High crosstalk yields ≈10 dB change in eNBreported SINR 

• Scheduling algorithm type impacts both cells

o Collateral setting adjustments required, e.g., 

enable/disable SRS
Closed-Loop Power Control Experiment

- Statistical Analysis & Results -

40

Speaker: Mike Frey
41

Descriptive model(s) of measured PUSCH EIRP per PRB power 

percentiles in terms of 6 study factors

Descriptive model

• Identify relationship

• Assess explanatory power

• Simple, easy-to-interpret

• Need not fit all the data

Predictive model

• Point predictions with uncertainties

• Assess prediction error

• Can be a “black box”

• Should fit all the data

A descriptive model is a useful roadmap

for creating a predictive model.

NASCTN CLPC Study Goal

Illustration: Radar equation - descriptive PPrrrrvv =

PPxxxxxxGG σσAAee

4ππ 2RR4
42

CLPC Study – two parallel experiments

Modeling experiment Monitoring experiment

Goal • Descriptive model(s) • Assess testbed stability

• Estimate UE-contributed variability

• Descriptive model for default conditions

Response 99 centiles of measured

PUSCH EIRP per PRB power 

99 centiles of measured

PUSCH EIRP per PRB power

Factors

A: Path loss (dB) 5, 10, 15, 20, 25, 30, 35, 40 0, 5, 10, 15, 20, 25, 30,

35, 37.5, 40, 42.5, 45

B: Crosstalk Low, High Low, High

C: Network loading (%) 10, 20, 40 10, 20, 40

D: Scheduler type Channel aware,

Interference aware Channel unaware

E: Nominal power, Po (dB) –105, –100, –95, –90, –85, –80 –85

F: Power fraction, α 0.8, 1.0 0.8

Replicates 1,2,3,4 1,2,3,4, 1,2,3,4, 1,2,3,4, 2,3
43

Key findings

F1: The testbed was stable (over nearly 2 months).

F2: UE-contributed variability is very low (≤ 1 dB, not discernible)

F3: Only factors A and E have meaningful, statistically discernible 

effects on the measured PUSCH EIRP per PRB power 

distribution. Factors C, D, and F do not.

F4: Models for percentiles of the measured PUSCH EIRP per PRB 

power distribution
44

F1: Stable testbed

Unanticipated 

phenomenon 

operating at 

HIGH crosstalk.
45

F2: No discernible UE-contributed variability

Model: YY = μμ + ββββ + UE + εε (for six B-C setting combinations)

Large numbers in red are estimated standard deviations (in dB).

Small numbers are the associated p-values for Ho: σσbb = 0
46

F3: Factors with important,discernible effects

ANCOVA model:

YY = γγ0 + γγ1 + γγ2EE + γγ3

+γγ4CC + γγ5DD + γγ6FF + γγ7CC + γγ8DD + γγ9CCFF + δδ + κκ

Effect P-value (50th

percentile)

P-value (90th

percentile)

0.00 0.00

EE 0.42 0.17

⨯ EE 0.00 0.00

CC 0.34 0.00

DD 0.15 0.83

FF 0.26 0.07

CC ⨯ DD 0.02 0.11

DD ⨯ FF 0.88 0.88

CC ⨯ FF 0.15 0.15

For the 50th power percentile, only A and the interactions 

A⨯E and C⨯D are discernible (green). But C⨯D is not 

practically important (max. abs. dev. = 0.54 dB).

For the 90th power percentile, only A and the interactions 

A⨯E and C are discernible (green). But C is not practically 

important (max. abs. dev. = 0.37 dB).

So, only factors A and E have statistically discernible, 

important effects.
F4: Descriptive model of power percentiles

47

Model: YY = ff , EE + εε

YY is a percentile of the measured PUSCH EIRP per PRB power.

εε is additive random variation.

is path loss (dB) and EE is nominal power (dB).

ff( , EE) is a two-region hyperbolic paraboloid

with parametric changeline.
48

Region of NPH – Descriptive model

Entry of the top of the PUSCH power distribution into

NPH is delayed relative to the center of the distribution.
Engineering Analysis

- Insight into Additional Questions -

49

Speaker: Rob Horansky & Aric Sanders
Engineering Analysis

- Antenna Pattern Measurements -

50

Speaker: Rob Horansky
Results of this work

51

•UE Antenna Pattern

▫ Large nulls

▫ Consistent polarization along top of phones

• Total Radiated Power (TRP)

▫ Distributions for LOS power

•UE Orientation Uncertainty

▫ Dominated by range loss and cable 

placement
1. Phone A

16cm x 7.8 cm (x2)

2. Phone B

16 cm x 7.5 cm (x2)

3. Phone C

13 cm x 6.55 cm

4. Phone D

13.8 cm x 6.7 cm

5. Phone E

15.8 cm x 7.8 cm

5 Types of Phones

Covering two OS types, slight 

variation in form factor for antenna 

design

NIST Broadband Interoperability Testbed (NBIT) facility
53

Patterns – Vertical Polarization

Top

Front

Bottom

vertical pol φ θ

Drawing: φ = 0; θ = 0
54

Phone TRP (dBm) Uncertainty 

(dB)

A 19.6 1.5

B 21.6 1.5

C 17.5 1.5

D 17.3 1.5

E 18.7 1.5

EIRP Distributions and Total Radiated Power (TRP)
55

Plug Alters Pattern Significantly Top Front

Bottom

vertical pol φ

θ

Drawing: φ = 0; θ = 0

Unplugged: Defined Peak

Phi Angles

Pattern Change = larger uncertainty due to UE orientation
56

Top

Front

vertical pol φ θ

Drawing: φ = 0; θ = 0 Cable Uncertainty > Phone Uncertainty

Solution: Cable Perpendicular to Antenna Polarization

Phone Variation Uncertainty = 0.5 dB Cable and Phone Var Uncertainty = 1 dB,

Not a Normal Distribution

Avg = 21.3 dBm, Unc. = 0.5 dB Avg = 20 dBm, Unc. = 1 dB
Engineering Analysis

- Measured vs. Reported Values -

57

Speaker: Aric Sanders
Test Circuit

58

• Reported power originates 

from UE diagnostics and is 

frequently used in 

interference models.

• Measured power is 

independent and corrected 

for losses directly outside the 

UE to the measurement 

device. 

• Reported power is greater 

than measured power by 

approximately 3 dB per PRB 

or 7 dB per TTI.

Measured Power

Reported Power
Reported vs. Measured (Measured per PRB)

Just Median Power 59

• Statistical analysis 

was on measured 

power per PRB.

• These plots use VSAcalibrated pathloss 

(78.1 dB added to 

attenuator value) 

PRB –Physical Resource Block

VSA – Vector Signal Analyzer
Path Loss (dB)* Applied 

Attenuation 

(dB)

Low Cross Talk 

Measured 

(dBm/PRB)

Low Cross Talk 

Reported 

(dBm/PRB)

High Crosstalk

Measured 

(dBm/PRB)

High Crosstalk

Reported

(dBm/PRB)

78.1 ± 1.6 0 ± 0.5 -30.3 ± 1.1 -26.0 ± 1.1 -25.3 ± 5.3 -21.0 ± 5.9

83.1 ± 1.6 5 ± 0.5 -25.4 ± 1.1 -22.0 ± 1.1 -20.1 ± 4.8 -16 ± 5.4

88.1 ± 1.7 10 ± 0.5 -20.3 ± 1.1 -17.0 ± 1.1 -14.7 ± 4.3 -11 . 0 ± 4.7

107.2 ± 4.2 30 ± 0.5 -0.3 ± 1.7 3.0 ± 1.7 1.7 ± 2.9 7.0 ± 3.4

112.5 ± 3.0 35 ± 0.5 3.1 ± 2.7 9.0 ± 2.7 1.9 ± 2.6 8.0 ± 4.1

117.9 ± 2.6 40 ± 0.5 1.5 ± 1.8 8.2 ± 2.1 7.6 ± 2.5 12.0 ± 2.3

Quantitative Differences (Monitoring per PRB) 

60

• Measured power is ~ 3-4 dB/PRB lower than reported power at path loss <110 dB
Path Loss (dB)* Applied 

Attenuation 

(dB) 

Low Cross Talk 

Measured 

(dBm/PRB)

Low Cross Talk 

Reported 

(dBm/PRB)

High Crosstalk

Measured 

(dBm/PRB)

High Crosstalk

Reported

(dBm/PRB)

78.1 ± 1.6 0 ± 0.5 -30.3 ± 1.1 -26.0 ± 1.1 -25.3 ± 5.3 -21.0 ± 5.9

83.1 ± 1.6 5 ± 0.5 -25.4 ± 1.1 -22.0 ± 1.1 -20.1 ± 4.8 -16 ± 5.4

88.1 ± 1.7 10 ± 0.5 -20.3 ± 1.1 -17.0 ± 1.1 -14.7 ± 4.3 -11 . 0 ± 4.7

107.2 ± 4.2 30 ± 0.5 -0.3 ± 1.7 3.0 ± 1.7 1.7 ± 2.9 7.0 ± 3.4

112.5 ± 3.0 35 ± 0.5 3.1 ± 2.7 9.0 ± 2.7 1.9 ± 2.6 8.0 ± 4.1

117.9 ± 2.6 40 ± 0.5 1.5 ± 1.8 8.2 ± 2.1 7.6 ± 2.5 12.0 ± 2.3

Quantitative Differences (Monitoring per PRB) 

61

• Measured power is ~ 3-4 dB/PRB lower than reported power at path loss <110 dB

• High crosstalk is ~ 5-6 dB/PRB higher than low crosstalk at path loss <110 dB
Path Loss (dB)* Applied 

Attenuation 

(dB) 

Low Cross Talk 

Measured 

(dBm/PRB)

Low Cross Talk 

Reported 

(dBm/PRB)

High Crosstalk

Measured 

(dBm/PRB)

High Crosstalk

Reported

(dBm/PRB)

78.1 ± 1.6 0 ± 0.5 -30.3 ± 1.1 -26.0 ± 1.1 -25.3 ± 5.3 -21.0 ± 5.9

83.1 ± 1.6 5 ± 0.5 -25.4 ± 1.1 -22.0 ± 1.1 -20.1 ± 4.8 -16 ± 5.4

88.1 ± 1.7 10 ± 0.5 -20.3 ± 1.1 -17.0 ± 1.1 -14.7 ± 4.3 -11 . 0 ± 4.7

107.2 ± 4.2 30 ± 0.5 -0.3 ± 1.7 3.0 ± 1.7 1.7 ± 2.9 7.0 ± 3.4

112.5 ± 3.0 35 ± 0.5 3.1 ± 2.7 9.0 ± 2.7 1.9 ± 2.6 8.0 ± 4.1

117.9 ± 2.6 40 ± 0.5 1.5 ± 1.8 8.2 ± 2.1 7.6 ± 2.5 12.0 ± 2.3

Quantitative Differences (Monitoring per PRB) 

62

• Measured power is ~ 3-4 dB/PRB lower than reported power at path loss <110 dB

• High crosstalk is ~ 5-6 dB/PRB higher than low crosstalk at path loss <110 dB

• High crosstalk has a higher variance 
Path Loss (dB)* Applied 

Attenuation 

(dB) 

Low Cross Talk 

Measured 

(dBm/PRB)

Low Cross Talk 

Reported 

(dBm/PRB)

High Crosstalk

Measured 

(dBm/PRB)

High Crosstalk

Reported

(dBm/PRB)

78.1 ± 1.6 0 ± 0.5 -30.3 ± 1.1 -26.0 ± 1.1 -25.3 ± 5.3 -21.0 ± 5.9

83.1 ± 1.6 5 ± 0.5 -25.4 ± 1.1 -22.0 ± 1.1 -20.1 ± 4.8 -16 ± 5.4

88.1 ± 1.7 10 ± 0.5 -20.3 ± 1.1 -17.0 ± 1.1 -14.7 ± 4.3 -11 . 0 ± 4.7

107.2 ± 4.2 30 ± 0.5 -0.3 ± 1.7 3.0 ± 1.7 1.7 ± 2.9 7.0 ± 3.4

112.5 ± 3.0 35 ± 0.5 3.1 ± 2.7 9.0 ± 2.7 1.9 ± 2.6 8.0 ± 4.1

117.9 ± 2.6 40 ± 0.5 1.5 ± 1.8 8.2 ± 2.1 7.6 ± 2.5 12.0 ± 2.3

Quantitative Differences (Monitoring per PRB) 

63

• Measured power is ~ 3-4 dB/PRB lower than reported power at path loss <110 dB

• High crosstalk is ~ 5-6 dB/PRB higher than low crosstalk at path loss <110 dB

• High crosstalk has a higher variance 

• Low crosstalk saturates and high crosstalk continues to increase
Reported vs. Measured (per TTI)

64

• Large span of reported power due to range of path loss values

• If UE reports 0 dBm, measured EIRP ≈ -7 ± 2 dBm

• Reflects instantaneous differences in reported and measured total power

TTI – Transmission Time Interval
Combined histogram of experimental results – all active TTIs

65

Details:

Monitoring Experiment: 2.8 Million Points, Mean: -7.5 dB, Standard 

Deviation: 1.3 dB

Modelling Experiment: 5.0 Million Points, Mean: -7.3 dB, Standard 

Deviation: 1.3 dB
66

• UEs are frequently broadcasting at maximum power, or in negative 

power headroom conditions.

• In our experiment, there is a decrease in MCS index and an increase in 

PRB grant size when the UE is in negative power headroom.

• Different measurement techniques produce significantly different path 

loss.

• This difference in path loss explains UE reported power behavior.

Negative Power Headroom and Path Loss
Negative Power Headroom

67

Negative 

Power 

Headroom

Positive Power 

Headroom

Transition Region

• Attenuator setting 

controls RSRP

• RSRP is estimated on DL 

• RSRP reflects small 

variations

RSRP – Reference Signal Received Power

DL - Downlink
Negative Power Headroom

68

Negative 

Power 

Headroom

Positive Power 

Headroom

Transition Region

• There is a strong 

dependence on 

crosstalk.
Negative Power Headroom

69

Negative 

Power 

Headroom

Positive Power 

Headroom

Transition Region

• We see a general 

increase in the grant 

size as the UE 

moves into the 

negative power 

headroom region. 
Negative Power Headroom

70

Negative 

Power 

Headroom

Positive Power 

Headroom

Transition Region

• In the negative 

power headroom 

region, we observe a 

mean MCS that 

decreases. 

MCS – Modulation Coding Scheme
Negative Power Headroom

71

Negative 

Power 

Headroom

Positive Power 

Headroom

Transition Region

• Without crosstalk, 

negative power 

headroom causes a 

decrease in mean MCS 

and an increase in 

grant size.
Five Ways to Assess Path Loss - Calibrated measurements vs UE-reporting 

72

• UE-reported DL path loss in 

power control packet 
Five Ways to Assess Path Loss - Calibrated measurements vs UE-reporting 

73

• UE-reported DL path loss in 

power control packet 

• DL path loss calculated from

RSRP (3GPP standard)
Five Ways to Assess Path Loss - Calibrated measurements vs UE-reporting 

74

• UE-reported DL path loss in 

power control packet 

• DL path loss calculated from

RSRP (3GPP standard)

• DL path loss calculated from 

RSRP with zero excess 

attenuation plus excess 

attenuator value
Five Ways to Assess Path Loss - Calibrated measurements vs UE-reporting 

75

• UE-reported DL path loss in 

power control packet 

• DL path loss calculated from

RSRP (3GPP standard)

• DL path loss calculated from 

RSRP with zero excess 

attenuation plus excess 

attenuator value

• UL path loss measured with

calibrated VNA

VNA – Vector Network Analyzer

UL - Uplink
Five Ways to Assess Path Loss - Calibrated measurements vs UE-reporting 

76

• UE-reported DL path loss in 

power control packet 

• DL path loss calculated from

RSRP (3GPP standard)

• DL path loss calculated from 

RSRP with zero excess 

attenuation plus excess 

attenuator value

• UL path loss measured with

calibrated VNA

• DL path loss measured with 

calibrated VNA
77

Path Loss

UE Estimation Independent Measurement
Conclusions 

- Challenges & Key Findings -

78

Speaker: Jason Coder
• Statistical Analysis - The response variable is a distribution, not a scalar or vector.

▫ PUSCH power per PRB distributions are frequently multimodal. Not a textbook problem. 

• Commercial equipment - Limited technical documentation, not intended for 

automated laboratory testing (e.g., frequent setting changes, time-alignment). 

• Automation - Makes testing a large number of equipment configurations practical, 

providing sufficient data for rigorous uncertainty assessment and statistical analysis.

▫ Acquisition + parsing + summary statistics  ~52,000 lines of code

▫ Parts I & II: 1,696 unique measurement configurations; 8,825 total configurations 

measured

• Data verification - 28 different automated checks during parsing and time 

alignment.

▫ Each 80 min test block auto-generated 294 pages of data verification plots for manual

inspection.

• Estimating real-world configurations

▫ What configuration are carriers using? 

79

Challenges (Parts I & II)
• PUSCH &PUCCH power per PRB distributions with OLPC spanned a much larger 

range than distributions with CLPC. 

▫ Closed-loop power control could enable better prediction of UE behavior.

• In some scenarios, the UE reported power was a poor metric of the actual radiated 

power. 

▫ UE never transmitted more power than it reported.

• In both the open-loop and closed-loop power control cases, the open-loop 

component of the power control equation was found to have little predictive utility. 

▫ Additional investigation is necessary.

• Performed 3-D radiation pattern measurements of several common LTE 

▫ The UE radiation patterns were not isotropic. 

• MITRE used an independent testbed to test configurations similar to some of those 

used in the screening experiment.

▫ The power distributions observed in the MITRE tests were consistent with the NASCTN 

laboratory measurements. 

80

Key Findings (Parts I & II)
• CLPC 

- Statistical analysis

▫ Descriptive model with two different 

regions

- No significant impact due to UE variation or 

network loading/offered load 

• Engineering analysis ▫ When using UE-reported path loss, UE

- reported power follows the power control 

equation.  UE-reported path loss often differs 

substantially (up to 15 dB) from calibrated 

measurements of path loss. 

▫ UE-reported power per TTI is on average 7 

dBm greater than the measured EIRP.

▫ In negative power headroom, UE has more 

scheduled PRBs and a lower MCS index.

Key Findings (Parts I & II)

81
• Effort produced three publications and sets of data:

▫ NIST Technical Note 2056: Antenna Pattern Measurements

▫ NIST Technical Note 2069: Factor Screening Experiment

▫ NIST Technical Note 2147: Closed-Loop Power Control Experiment

▫ All available free-of-charge: nist.gov; each publication contains a link to the data 

(data.nist.gov)

• Findings summarized in an Appendix of TN 2147

▫ Designed to make it easy to use and apply NASCTN findings

• Statistical analysis provides a starting point, should the community be interested in 

extending this work to a predictive model

• Provided insight into UE emissions behavior, which could be used to inform next 

generation interference models/assessments

• Provided insight into additional questions via the engineering analysis

▫ Results in the context of power control equation

▫ Measured vs. reported power

▫ Scheduling dynamics in negative power headroom

Summary

82 Note: Clickable links to the reports included above
Questions?
Back-up Material

84
• Developed an automated process to time align and parse the raw data files

▫ Time-domain cross correlation between self-reported and measured power

 RB allocations from diagnostic monitoring software compared with spectrogram

▫ Parsed data from each of the three sources

▫ Performed preliminary statistical analysis  “summary packets”

• Data are scrutinized during parsing to aid in identifying testbed issues

▫ 28 different checks during the time alignment process

 RACH attempts

 Time alignment errors

 Sequence of acquisition errors

 ....

85

Parsing/Alignment
86

1) Extract RB allocation from diagnostic 

monitoring software

2) Time align the reported and 

measured data

Note: preliminary data shown
• Some applications may be addressable in the report writing of this phase

▫ Others may be research projects (NIST vs. NASCTN)

▫ Others may be best done by the community with input from NASCTN

• The “recipe” 

▫ Use of NASCTN data (or approach) to get a reference set of UE transmissions for use in AIT tools.

▫ NASCTN data could be used to inform a recipe that includes other components (e.g., channel model)

• Use of NASCTN data to inform SSTD morphologies

• Use of NASCTN antenna pattern measurement data in simulations

▫ Conditional probability distributions to get power in the direction of a DoD asset (given a UE transmit power)

• Informing the accuracy or AITs

▫ Refinement of situations where transmit power significantly deviates from what is expected 

▫ CLPC data set may be most appropriate for this analysis

• How does the presence of closed loop power control effect expected transmit power.

▫ What can we say about small cell applications?

 Might the UE start at a lower power than is needed (due to being close to the base station), but is driven up by 

power control.

• UE variation between models and manufacturers

▫ Current data set only provides another clue

Potential applications of NASCTN data

87
• Goal: Provide a concise summary of the NASCTN findings and insights across all 

three measurements efforts

▫ Factor Screening

▫ UE Antenna Pattern Measurements

▫ Closed-Loop Power Control Characterization

• Inspired by the book of models, and the idea of developing a predictive model

• What can NASCTN about component of a model?

▫ What components does NASCTN think could be included in a model?

Appendix: Summary of Findings

88
• Green elements: NASCTN can provide insight 

• Other elements: No direct NASCTN insight

• Every elements will be discussed

▫ Summary of NASCTN’s contributions, and where details can be found

▫ Or, why is that element particularly challenging? 

Appendix: Summary of Findings

89
• Text can be dropped into 

book of models, or other 

resources as desired

• Doesn’t comment on the 

weighting or combination 

of individual components

Appendix Summary of Findings

90