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Network-based UE mobility estimation in mobile networks

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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.

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Network-based UE mobility estimation in mobile networks

  1. 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. 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. 3. Motivation Speed estimation for: •Mobility management •Quality of User Experience •Traffic Scheduling •Spectrum and energy efficiency
  4. 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. 5. Time-based Spread Spectrum Method
  6. 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. 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. 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. 9. Time-based Spread Spectrum UE Speed Estimation: The principle
  10. 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. 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. 12. Implementation of TSSM
  13. 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. 14. Performance Evaluation
  15. 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. 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. 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. 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. 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
  • pgpus

    Oct. 8, 2015

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.

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