The co-existence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we demonstrate how sounding reference signal (SRS) measurements available at the base station (a.k.a. eNodeB in 4G systems) can be used with a low computational requirement to estimate the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE’s speed to the three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility and 94% for high mobility, respectively.

1. Network-based UE Mobility Estimation in Mobile
Networks
Dalia Georgiana Herculea, M. Haddad (Université Avignon), V. Capdevielle, C. S. Chen
Alcatel-Lucent Bell Labs France
MobiCom 2015, Paris

2. 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 

3. Motivation
Speed estimation for:
•Mobility management
•Quality of User Experience
•Traffic Scheduling
•Spectrum and energy efficiency

4. ALCATEL-LUCENT — PROPRIETARY AND CONFIDENTIAL
COPYRIGHT © 2015 ALCATEL-LUCENT. ALL RIGHTS RESERVED
Agenda
1. Introduction
2. Time-based Spectral Spreading Method (TSSM)
Methodology
Implementation
Performance evaluation
4. Conclusion

5. Time-based Spread Spectrum
Method

6. ALCATEL-LUCENT — PROPRIETARY AND CONFIDENTIAL
COPYRIGHT © 2015 ALCATEL-LUCENT. ALL RIGHTS RESERVED
•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)

7. 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) = 𝑒
𝜎 𝜓 𝑑𝐵
𝜓 𝑑𝐵 (𝑡)+𝜇 𝜓 𝑑𝐵
20
𝜎 𝜓 𝑑𝐵
𝜇 𝜓 𝑑𝐵
ℛ 𝜓 𝛿 = 𝔼 𝜓 𝑑 − 𝛿 − 𝜇 𝜓 𝑑𝐵
𝜓 𝑑 − 𝜇 𝜓 𝑑𝐵
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


8. From experimental results, then becomes:
Remark: The decorrelation distance D =the distance at which the signal autocorrelation equals 1/e of
its 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
ℛ 𝜓 𝜏 = 𝔼 𝜓 𝑡 − 𝜏 − 𝜇 𝜓 𝜓 𝑡 − 𝜇 𝜓
(1)
M. Marsan and G.C. Hess, “Shadow variability in an urban land mobile radio environment,” Electronics Letters, pp. 646–648, May 1990.
D
dBR

  2
)( 
D
eR dB

 

 2
)(
D
v
e



 2

9. Time-based Spread Spectrum UE
Speed Estimation: The principle

10. ALCATEL-LUCENT — PROPRIETARY AND CONFIDENTIAL
COPYRIGHT © 2015 ALCATEL-LUCENT. ALL RIGHTS RESERVED
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
𝑆 𝜓 𝑓 = ℛ 𝜓𝜓 𝜏 𝑒−𝑗2𝜋𝑓𝜏
𝑑𝜏
+∞
0
𝑓 =
𝑣
𝐷
.
ℛ 𝜓𝜓 𝜏
𝑆 𝜓 𝑓 =
𝜎 𝜓
2
𝜋
𝑓0
𝑓2 − 𝑓0
2
D
v
e



2

11. ALCATEL-LUCENT — PROPRIETARY AND CONFIDENTIAL
COPYRIGHT © 2015 ALCATEL-LUCENT. ALL RIGHTS RESERVED
𝔼 𝑟 t
2
∼
𝜎 𝜓
2
𝑣2
𝐷2
𝑣 ∼ 𝐷 𝔼 𝑟 𝑁 t
2
After some computation:
Time-based Spectral Spreading Method (TSSM)
𝜕2
ℛ 𝑟𝑟 𝜏
𝜕2 𝜏 τ=0
= 𝔼 𝑟 t
2
Using eq. (1):
𝜕2
ℛ 𝜓𝜓 𝜏
𝜕2 𝜏 τ=0
=
𝜎 𝜓
2
𝑣2
𝐷2
(2)
(3)
From (2) and (3)
-> the second derivative of the autocorrelation of the
shadowing is proportional to the square of the speed
𝑟 𝑁 𝑡 = 𝑟(𝑡)/𝜎 𝜓where
D
v
etR

 

 2
)(Equation 1:

12. Implementation of TSSM

13. ALCATEL-LUCENT — PROPRIETARY AND CONFIDENTIAL
COPYRIGHT © 2015 ALCATEL-LUCENT. ALL RIGHTS RESERVED
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)
𝑿 𝒌 = 𝑿 𝒌/ 𝒏𝒐𝒓𝒎(𝐗 𝐢)
𝑑 𝑘 = 𝐸 𝑋 𝑘 − 𝐸 𝑋 𝑘−𝑛 /(𝑛. 𝑇)
Dispi =
1
K
. (𝑑 𝑘 − m)2
K
k=1
Blocki=
[Xi+1…Xi+N]
.
𝜀𝑖
Dispersion_i
DB
Normalization
𝑑 𝑘
Derivatives of order d
block i
𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(. )
Comparison
to Data Basis
Vi
Comparison to a Data Basis that is built off line.

14. Performance Evaluation

15. 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

16. TSSM simulations: Parameters
Scenario 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=0.8
OLPC Period=80 ms
RSRP Period=80ms
L3 Filtering: k=8
P0 nominal= -78dBm
CLPC 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

17. Speed and mobility
estimation 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

18. 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
Probabilityofcorrectclassestimation

19. • 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