Network-based UE mobility estimation in mobile networks

Network-based UE Mobility Estimation in Mobile

Networks

Dalia Georgiana Herculea, M. Haddad (Université Avignon), V. Capdevielle, C. S. Chen

Alcatel-Lucent Bell Labs FranceMobiCom 2015, Paris

Motivation

Small cells + macro cells -> HetNets

Cell densification + Heterogeneity + Mobility

•High handover frequency while the network must ensure

continuous service and high-quality user experience

•High call drop probability

•High network cost (signaling overhead, re-connect)

Cologne signal map provided by opensignal.com

Example of UE trajectory

Motivation

Speed estimation for:

•Mobility management

•Quality of User Experience

•Traffic Scheduling

•Spectrum and energy efficiency

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Agenda

1. Introduction2. Time-based Spectral Spreading Method (TSSM)

MethodologyImplementationPerformance evaluation

4. Conclusion

Time-based Spread Spectrum

Method



•Shadowing•Fast fading•Path loss atenuation

Measurements in LTE•Uplink Sounding Referece Signals

Propagation Model•Large-scale propagation model (Path Loss and Shadowing)•Small-scale propagation model (fast fading)

Fading (Radio Channel)

𝑟 𝑡 = 𝛽 𝑡 𝜓 𝑡

In Suzuki’s model, the amplitude of the channel is: 𝛽 𝑡 𝜓 𝑡

=Rayleigh process

=shadowing process

log(Pr/Pt)

Path loss

Shadow +Path loss

Multipath+Shadowing+Path loss

log(d)

Large-Scale Channel Model: Log-Normal Shadowing

Gudmundson’s correlation model : the shadowing is a first-order autoregressive process modeled in the spatial domain by a lognormal process:

=shadow standard deviation

=area mean

The spatial autocorrelation between shadow fading at two points separated by distance is characterized by:

=the correlation between two points separated by a fixed distance D.

𝜓(𝑡) = 𝑒𝜎𝜓𝑑𝐵

𝜓𝑑𝐵 (𝑡)+𝜇𝜓𝑑𝐵20

𝜎𝜓𝑑𝐵

𝜇𝜓𝑑𝐵

ℛ𝜓 𝛿 = 𝔼 𝜓 𝑑 − 𝛿 − 𝜇𝜓𝑑𝐵 𝜓 𝑑 − 𝜇𝜓𝑑𝐵

𝜌

𝛿

Gudmundson, M., “Correlation Model for Shadow Fading in Mobile Radio Systems”, Electron. Lett, Vol. 27, 23, 2145-2146), November, 1991.

D

dB

2

From experimental results, then becomes:

Remark: The decorrelation distance D =the distance at which the signal autocorrelation equals 1/e ofits maximum value

Mobile UE: => spatial autocorrelation translates into time autocorrelation => the shadowing behaves as a correlated, time-varying process

Large-Scale Channel Model: Log-Normal Shadowing

𝜌 = 1/𝑒

ℛ𝜓 𝜏 = 𝔼 𝜓 𝑡 − 𝜏 − 𝜇𝜓 𝜓 𝑡 − 𝜇𝜓

(1)

M. Marsan and G.C. Hess, “Shadow variability in an urban land mobile radio environment,” Electronics Letters, pp. 646–648, May 1990.

DdB

R

2)(

DeRdB

2)(

D

v

e

2

Time-based Spread Spectrum UE

Speed Estimation: The principle



Reasoning

1)We compute the Fourier transform of the autocorrelation function:

2)By replacing with its expression , we obtain:

which is a Lorentzian function with

Time-based Spectral Spreading Method (TSSM): Technical details

𝑆𝜓 𝑓 = ℛ𝜓𝜓 𝜏 𝑒−𝑗2𝜋𝑓𝜏 𝑑𝜏+∞

0

𝑓 =𝑣

𝐷.

ℛ𝜓𝜓 𝜏

𝑆𝜓 𝑓 =𝜎𝜓

2

𝜋

𝑓0

𝑓2 − 𝑓02

D

v

e

2



𝔼 𝑟 t 2 ∼

𝜎𝜓2𝑣2

𝐷2

𝑣 ∼ 𝐷 𝔼 𝑟𝑁 t 2

After some computation:

Time-based Spectral Spreading Method (TSSM)

𝜕2ℛ𝑟𝑟 𝜏

𝜕2𝜏 τ=0= 𝔼 𝑟 t

2

Using eq. (1):

𝜕2ℛ𝜓𝜓 𝜏

𝜕2𝜏 τ=0=

𝜎𝜓2𝑣2

𝐷2

(2)

(3)

From (2) and (3)

-> the second derivative of the autocorrelation of the shadowing is proportional to the square of the speed

𝑟𝑁 𝑡 = 𝑟(𝑡)/𝜎𝜓 where

D

v

etR

2)(Equation 1:

Implementation of TSSM



Per-block Speed Estimator•Normalization of the SRS power measurement sample

•Computation of derivatives of these measurement samples

•Root of the variance calculated on subsequent derivatives

Time-based Spectral Spreading Method (TSSM)

𝑿𝒌 = 𝑿𝒌/ 𝒏𝒐𝒓𝒎(𝐗𝐢)

𝑑𝑘 = 𝐸 𝑋𝑘 − 𝐸 𝑋𝑘−𝑛 /(𝑛. 𝑇)

Dispi = 1

K. (𝑑𝑘 − m)2

K

k=1

Blocki= [Xi+1…Xi+N]

.

휀𝑖 Dispersion_i

DB

Normalization

𝑑𝑘

Derivatives of order d block i

𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(. )

Comparison to Data Basis

Vi

Comparison to a Data Basis that is built off line.

Performance Evaluation

Setup:

• Channel model: ETU (Extended Typical Urban)

•Block Size: 256 samples

•SRS period: 40 ms

•Decorrelation distance: 10, 20, 50 and 100 m

•Speed: varying from 0 to 120 kmph

Database: TSSM Metric

TSSM simulations: ParametersScenario Input Description

Data Description L1-based Data Set

Carrier 2GHz

Multi Path 3GPP ETU

Path Loss 3GPP 36.942

Shadowing Shadowing Patzold Model

Fractional Power Control Configuration Alpha=0.8OLPC Period=80 msRSRP Period=80msL3 Filtering: k=8P0 nominal= -78dBmCLPC Period=80ms

UE speed variable between 0 and 120 kmph

Mobility Path Kolntrace mobility traces

TSSM Configuration UE Speed Estimation Period = 4s

Nr of users for tests 30

Duration of movement per user 16 16 minutes

Speed and mobilityestimation per user

UE 1: 90.41 %

88.88 %

93.75 %

UE2: 92.10 %

96.66 %

90 %

UE3: 86.27 %

89.18 %

100 %

UE4: 76.47 %

95.06 %

100 %

UE1 UE 2

UE 3

UE 4

Classification in three mobility classes:•[0-40] kmph Low Mobility Class (Class 1)•[40-90] kmph Medium Mobility Class (Class 2)•[>90] kmph High Mobility Class (Class 3)

Speed and mobility estimation•30 UEs from Kolntrace data•16 minutes per user•480 minutes of movement

Main

Functional

Elements

• Normalization operation

• Derivative computation

• Variance computation

To eNodeB

CPU

Around 10 operations per UE speed

estimation

To eNodeB

Memory

Circular buffer of 15 samples

Impact to the eNodeB

Class 1

85.7%

Class 2

93.5% Class 3

94.7%

Speed class

Pro

bab

ility

of

corr

ect

clas

s es

tim

atio

n

• Time-based Spread Spectrum Method:

-estimates the speed through means of physical layer information and signal, processing techniques, -exploits already existing signals, no modification at the UE side,

-high accuracy,

-intelligence and modifications only at the BS side.

Conclusions

Technology

Network-based UE mobility estimation in mobile networks