Femtocells are a promising approach to provide high data rates through autonomous configuration in indoor environments. However, due to the random and uncontrolled deployment of femtocells within users' premises, interference between femtocells themselves and with macrocell base stations is a major issue. In this work, we look into the interference management problem and work towards the development of an interference mitigation algorithm based on the localization of randomly positioned femtocells using radio environmental information. In particular, we show that based on building floor plans and basic information on the urban landscape, femtocells can accurately localize themselves using macrocellular base stations as anchor nodes. Based on the localized femtocell positions, various channel allocation schemes are employed to mitigate interference.

1. ENVIRONMENT-AWARE INTERFERENCE
MANAGEMENT IN FEMTOCELLS
Avishek Patra
Institute for Networked Systems, RWTH Aachen University

2. CONTENTS
1.
2.
3.
4.
5.
6.
MOTIVATION
INTERFERENCE PROBLEM IN FEMTOCELLS
INTERFERENCE MANAGEMENT
LOCALIZATION ALGORITHM
1. ENVIRONMENTAL MODELING & WINPROP
2. PROPAGATION MODELING
3. ALGORITHM DESCRIPTION & RESULTS
CHANNEL ALLOCATION SCHEMES
1. ALGORITHM DESCRIPTION
2. ALGORITHM RESULTS
CONCLUSION

3. MOTIVATION
●
Shift from voice-only to voice- & data-based traffic
●
Improved technologies – smart antennas, cell size reduction
●
Deadzone Problem – Poor indoor coverage and inability to
match required capacity
●
Solution – Femtocells – Small range, low power BSs with
better indoor coverage and higher capacity
●
Outdoor Macro-Network + Indoor Femto-Network =
Heterogeneous Networks

4. INTERFERENCE PROBLEM IN FEMTOCELLS
●
●
●
Co-channel Interference
Uncertainty of Placement due to User-Deployment
Degradation to and from other Femtocell and Macrocell
Basestations
INTERFERENCE SCENARIOS
1.
2.
3.
4.
5.
6.
Macrocell UE
Macrocell BS
Femtocell UE
Femtocell BS
Femtocell ‘A’ UE
Femtocell ‘A’ BS
Femtocell BS
Femtocell UE
Macrocell BS
Macrocell UE
Femtocell ‘B’ BS
Femtocell ‘B’ UE
Cross-Tier
Co-Tier

5. INTERFERENCE PROBLEM IN FEMTOCELLS
[CONTD.]
Fig. 1. Femtocell – Macrocell
Interference Scenarios

6. INTERFERENCE MANAGEMENT
SOLUTIONS IN LITERATURE
1.
2.
3.
4.
5.
[1]
[2]
[3]
[4]
[5]
Decentralized Spectrum Allocation [1]
Transmit Power Control [2]
Frequency Hopping [3]
Directional Antennas [4]
Hybrid Channel Allocation [5]
V. Chandrasekhar and J.G. Andrews, "Spectrum Allocation in Two-Tier Networks", IEEE Asilomar, Oct. 2008.
H. Claussen, "Performance of Macro- and Co-Channel Femtocells in a Hierarchical Cell Structure,“ PIMRC 2007.
IEEE 18th International Symposium, Sep. 2007
V. Chandrasekhar, J. Andrews, and A. Gatherer, "Femtocell networks: A Survey," IEEE Commun. Mag., vol. 46,
no. 9, pp. 59-67, Sep. 2008.
T. H. Kim, T. Salonidis, and H. Lundgren, "MIMO wireless networks with directional antennas in indoor
environments," INFOCOM, 2012 Proceedings IEEE , vol., no., pp.2941,2945, 25-30 March 2012.
Yong Ding, and Li Xiao, “Channel Allocation In Multi-channel Wireless Mesh Networks”, Computer
Communications, Volume 34, Issue 7, 16 May 2011, Pages 803-815.

7. INTERFERENCE MANAGEMENT
[CONTD.]
PROPOSED SOLUTION
Environment-aware
Femtocells
Interference
Management
in
SALIENT FEATURES
1.
2.
Localization :
1. Indoor localization using environmental information
2. Dependent on signal penetration loss through walls
Interference Management :
1. Dynamic channel allocation
2. Allocation using heuristic methods

8. 1. LOCALIZATION ALGORITHM
PROPOSED METHOD
●
●
●
●
Localize Femtocell within a
Room in an Urban
Environment through
triangulation
Based on RSSI (Received
Signal Strength Indicator)
Effect of different
penetration losses through
walls of different materials
Fixed Macrocell Base
Stations as Anchors
Fig. 2. Femtocell Localization by
Triangulation

9. ENVIRONMENT MODELING AND WINPROP
●
●
●
●
Received Signal degrades due to:
○ Path Loss in Urban Environment
○ Penetration Loss in Indoor Environment (Walls of
Building containing the Femtocell)
Environmental Modeling using WinProp Suite [6]
Urban Model :
1. Height of Buildings
2. Position of Buildings
Indoor Model :
3. Individual Wall Losses
4. Positions of Walls
[6] AWE Communication http://www.awe-communications.com

10. ENVIRONMENT MODELING AND WINPROP
[CONTD.]
Fig. 3(a). Indoor Environment Model

11. ENVIRONMENT MODELING AND WINPROP
[CONTD.]
Fig. 3(b). Signal propagation
through Indoor
Environment Model

12. ENVIRONMENT MODELING AND WINPROP
[CONTD.]
Fig. 3(c). Urban Environment Model

13. ENVIRONMENT MODELING AND WINPROP
[CONTD.]
Fig. 3(d). Signal
propagation through
Urban Environment
Model

14. PROPAGATION MODELING
●
Propagation Models to generate indoor Received Power
URBAN PROPAGATION MODEL
Parametric Model (COST 231 Walfisch Ikegami Model)
Empirical Model (Empirical Data from WinProp Suite)
1.
2.
INDOOR PROPAGATION MODEL
●
●
Based on material-dependent Wall Losses
Received Power, P_Rx at any point inside Building:
(in dB)

15. ALGORITHM DESCRIPTION
Flowchart: Database
Generation

16. ALGORITHM DESCRIPTION
[CONTD.]

17. ALGORITHM DESCRIPTION
[CONTD.]
LOCALIZATION ALGORITHM
●
●
●
RSSI Database Generation w.r.t. all anchor MBSs
Localization by referring to generated RSSI Databases
Location by Maximum Likelihood Estimation
Fig. 4. Maximum Likelihood
Estimation – 3D Plot

18. ALGORITHM RESULT
LOCALIZATION RESULTS
●
●
●
Pr(Room Correctness)
Pr(Position Correctness)
Average Distance Error
= 0.88(95% Shadow CI)
= 0.30(95% Shadow CI)
= 1.36 m
OBSERVATIONS
●
●
Variation due to different propagation model for
generating RSSI Databases
Variation in results due to different MBS Deployment
Scenario

19. ALGORITHM RESULT
8-MBS
COST 231
WI Model
8-MBS
WI-based
Curve-Fitting
[CONTD.]
6-MBS
COST 231
WI Model
Fig. 5(a). Box-Plots of Distance Errors for different Scenarios
6-MBS
WI-based
Curve-Fitting

20. ALGORITHM RESULT
[CONTD.]
Fig. 5(b). Histogram of Distance Error for Scenario with 8-MBS at
average distance of 400m

21. 2. CHANNEL ALLOCATION SCHEMES
●
Interference Management for OFDMA-based Femtocell
downlink scenario
ASSUMPTIONS
●
●
●
●
●
●
Location of Femtocells in Building known
Femtocells share fixed no. of OFDMA sub-channels
Femtocells have fixed transmit power
Users associate with Serving Femtocell Base Station
Co-channel Non-Serving Femtocell signals act as
interference
Target: Maximise Average Downlink SINR of Users

22. ALGORITHM DESCRIPTION
1.
●
●
●
●
GRAPH COLORING BASED METHOD (GCM)
Based on DSATUR Algorithm [7]
Interference Graph generation
Low available sub-channels to FBS served users ratio
Edge-Weight assignment: (Lower Weighing Edges dropped)
1.
2.
3.
[7]
Range-based
Distance based
Walls &distance based
∝ overlap (FBS i, FBS j)
∝ 1/dist (FBS i, FBS j)
∝ 1/[dist (FBS i, FBS j) x
walls (FBS i, FBS j)]
D. Brélaz, “New Methods to Color the Vertices of a Graph,” Comm. ACM 22, 251-256, 1979.

23. ALGORITHM DESCRIPTION
2.
●
●
●
●
[CONTD.]
SIMULATED ANNEALING METHOD (SAM)
Analogous to metal annealing [8]
Scenario Interference as Objective Function
Temperature decrease depends on Cooling Scheme
Linear Cooling Scheme
T – Temperature, N – Total Iterations
[8]
S. Kirkpatrick, C. Gelatt, Jr., M. Vecchi,
“Optimization by simulated annealing,”
Science, Vol220, No 4598, pp. 671-680, May 1983.
Fig. 6. Cooling Schemes

24. ALGORITHM RESULTS
●
●
●
Perfect Localization: (6 Scenarios)
○ Average SINR
= 30 - 52 dB
○ 05%-ile SINR
= 18 - 36 dB
○ 95%-ile SINR
= 42 - 85 dB
Imperfect Localization: (1 Scenario)
○ Average SINR
= 18 - 48 dB
○ 05%-ile SINR
= 02 - 32dB
○ 95%-ile SINR
= 38 - 110 dB
Error in SINR
= ~ 20 – 26 dB
(max. 112 dB)

25. ALGORITHM RESULTS
[CONTD.]
Scenario SINR [in dB]
70
60
50
40
30
C – Complete
Range
B – Range points
within
Building
R – Range points
within
Room
20
10
C
B
Range-based
GCM
R
C
B
Distance-based
GCM
R
C
B
Distance- and Walls
Based GCM
R
R
SAM
Fig. 8(a). Box-Plots for Channel Allocation Scenario (12-FBS 4-Channels) in a
single storiedmulti-room building using GCM and SAM

26. ALGORITHM RESULTS
[CONTD.]
R – Range points
within
Room
R
30-FBS 6-Channels
SAM
R
30-FBS 8-Channels
SAM
Fig. 8(b). Box-Plots for Channel Allocation Scenarios (30-FBS 6-Channels
and 30-FBS 8-Channels) in a double storied multi-room building using SAM

27. ALGORITHM RESULTS
[CONTD.]
OBSERVATIONS
●
●
●
●
●
Scenario SINR α Number of
available channels
Scenario SINR α 1/Number of
FBSs
Scenario SINR varies with
different FBS SINR
measurement methods
Fig. 7. Allocated Channels for 12Difference in average scenario FBS 3-Channel Scenario
SINR results due to inaccurate
localisation
GCM v/s SAM – No clear winner in channel allocation.
(GCM faster compared to SAM)

28. CONCLUSION
●
●
●
●
●
●
Localization with awareness of surrounding
environment
Localization within room with accuracy up to 88% and
minimum average distance error of 1.36m
Interference management through location-based
dynamic channel allocation
Average SINR (downlink) in range of:
○ Perfect Localization:
30 – 52 dB
○ Imperfect Localization: 18 – 48 dB
Easily extendable for co-tier uplink and cross-tier
scenarios
Study using IRT Propagation Model and complex
multiple material building

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