environment-aware localization of femtocells for interference management

ENVIRONMENT-AWARE INTERFERENCE MANAGEMENT IN FEMTOCELLS

Avishek PatraInstitute for Networked Systems, RWTH Aachen University

CONTENTS

1. MOTIVATION 2. INTERFERENCE PROBLEM IN FEMTOCELLS

3. INTERFERENCE MANAGEMENT ALGORITHM

4. LOCALIZATION ALGORITHM

1. ENVIRONMENTAL MODELING

2. PROPAGATION MODELING

3. ALGORITHM DESCRIPTION

4. ALGORITHM RESULTS

5. CHANNEL ALLOCATION SCHEMES (CAS)1. HEURISTIC METHODS DESCRIPTION

2. SIMULATION RESULTS

6. CONCLUSION

1. MOTIVATION

Shift from voice-only to voice- & data-based traffic

Dead zone Problem – Poor indoor coverage cannot match

required capacity

Solution – Femtocells – Small range, low power BSs with better

indoor coverage and higher capacity

Outdoor Macro-Net + Indoor Femto-Net = Heterogeneous

Network

Problem – CO – CHANNEL INTERFERENCE!!!

2. INTERFERENCE PROBLEM IN FEMTOCELLS

Co-channel Interference Uncertainty of Placement due to User-Deployment Degradation to and from other Femtocell and Macrocell BSs

INTERFERENCE SCENARIOS

1. CO – TIER – Amongst Femtocell BSs

2. CROSS-TIER – Between Femtocell BSs and Macrocell BSs

Fig. 1. Femtocell – Macrocell Interference Scenarios

2. INTERFERENCE PROBLEM IN FEMTOCELLS


PROPOSED SOLUTION

ENVIRONMENT-AWARE INTERFERENCE MANAGEMENT IN FEMTOCELLS

SALIENT FEATURES

1. INDOOR LOCALIZATION : Based on environmental information and dependence of signal penetration loss on wall material

2. INTERFERENCE MANAGEMENT : Dynamic channel allocation using heuristic methods








5. CHANNEL ALLOCATION SCHEMES



6. CONCLUSION

CONTENTS


PROPOSED METHOD

Localize Femtocell within aRoom in Urban Environmentthrough triangulation

Macrocell BSs = Anchors RSSI Measurement Effect of different

penetration losses through walls of different materials

MBS A

MBS B

MBS C

LOCATED FBS

Fig. 2. Femtocell Localization by Triangulation

4.1. LOCALIZATION ALGORITHM – ENVIRONMENT MODELING

Received Signal degrades due to: Path Loss – In Urban

Environment Penetration Loss – In Indoor

Environment

Environmental Modeling using WinProp Suite [1]

Urban Model : Height & Positions of Buildings

Indoor Model : Wall Positions & Losses based on material[1] AWE Communication http://www.awe-communications.com

http://www.awe-communications.com/

Fig. 3(a). Indoor Environment Model


Fig. 3(b). Signal propagation through Indoor

Environment Model


4.2. LOCALIZATION ALGORITHM – PROPAGATION MODELING

Propagation Models to generate indoor Received Power

URBAN PROPAGATION MODEL

1. Parametric Model (COST 231 Walfisch Ikegami Model)2. Empirical Model (Empirical Data from WinProp Suite)

INDOOR PROPAGATION MODEL

Based on material-dependent Wall Losses

Received Power, P_Rx at any point inside Building:

(in dB)

4.3. LOCALIZATION ALGORITHM – ALGORITHM DESCRIPTION

RSSI Database Generation w.r.t. all anchor MBSs Localization of FBS by referring to generated RSSI Databases Location estimation using Maximum Likelihood Estimation

Fig. 4. Maximum Likelihood Estimation – 3D Plot

4.4. LOCALIZATION ALGORITHM – ALGORITHM RESULT

LOCALIZATION RESULTS

Probability (Room Correctness) = 0.88 (95% Shadow CI)

Probability (Position Correctness) = 0.30 (95% Shadow CI)

Average Distance Error = 1.36 m

OBSERVATIONS

Variation due to different propagation model for generating RSSI Databases

Variation in results due to different MBS Deployment Scenario

0

2

4

6

8

1 2 3 4

Scenarios

Dis

tanc

e E

rror

[in

m]

A B C DScenarios

4.4. LOCALIZATION ALGORITHM – ALGORITHM RESULT [CONTD.]

Fig. 5(a). Box-Plots of Distance Errors for different Scenarios

8-MBS COST 231 WI Model

8-MBS WI-based

Curve-Fitting

6-MBS COST 231 WI Model

6-MBS WI-based

Curve-Fitting

0 1 2 3 4 5 60

50

100

150

200

250

300

350

Distance Error [in m]

No.

of S

ampl

es4.4. LOCALIZATION ALGORITHM – ALGORITHM RESULT [CONTD.]

Fig. 5(b). Histogram of Distance Error for Scenario with 8-MBS at average distance of 400m








5. CHANNEL ALLOCATION SCHEMES



6. CONCLUSION

CONTENTS

5. CHANNEL ALLOCATION SCHEMES (CAS)

Interference Management for OFDMA-based Femtocell in downlink scenario

ASSUMPTIONS

Location of Femtocells in Building known Fixed no. of OFDMA sub-channels & Transmit Power Users associate with ‘Serving’ Femtocell Base Station (#Sub-channels / ‘Serving’ Femtocell Base Station) < 1 Co-channel ‘Non-Serving’ Femtocell signals act as interference

Target: Maximise Average Downlink SINR of Users

1. GRAPH COLORING BASED METHOD (GCM)

Based on DSATUR Algorithm [2]

Interference Graph generation Edge-Weight, W assignment:

Low Weight Edges dropped (#Sub-channels / Serving FBS < 1)

[2] D. Brélaz, “New Methods to Color the Vertices of a Graph,” Comm. ACM 22, 251-256, 1979.

5.1. CAS – HEURISTIC SCHEMES DESCRIPTION

RANGE BASED DISTANCE BASED WALLS & DISTANCE BASED

W ∝ overlap (FBS i, FBS j)

(Complete range depending on Minimum Detectable Strength)

W ∝ W ∝

Fig. 6. Allocated Channels for 12-FBS 3-Channel Scenario



[CONTD.]

2. SIMULATED ANNEALING METHOD (SAM)

Analogous to metal annealing [3]

Scenario Interference as Objective Function Temperature decrease depends on Cooling Scheme Linear Cooling Scheme

T – Temperature, N – Total Iterations

[3] S. Kirkpatrick, C. Gelatt, Jr., M. Vecchi, “Optimization by simulated annealing,” Science, Vol220, No 4598, pp. 671-680, May 1983.

Fig. 7. Cooling Schemes

5.2. CAS – SIMULATION RESULTS

Perfect Localization: Average SINR = 30 - 52 dB 05%-ile SINR = 18 - 36 dB 95%-ile SINR = 42 - 85 dB

(max. 112 dB)

Imperfect Localization: Average SINR = 18 - 48 dB 05%-ile SINR = 02 - 32dB 95%-ile SINR = 38 - 110 dB

Error in SINR = ~ 20 – 26 dB

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10

Scenarios B1(1,2,3) B2(4,5,6) B3(7,8,9) B4(10)

Sce

nario

SIN

R [i

n dB

m]

B1(1) B1(2) B1(3) B2(1) B2(2) B2(3) B3(1) B3(2) B3(3) B4

Scenarios

Scen

ario

SIN

R [i

n dB

]5.2. CAS – SIMULATION RESULTS

[CONTD.]

Fig. 8(a). Box-Plots for Channel Allocation Scenario (12-FBS 4-Channels) in a single storiedmulti-room building using GCM and SAM

Range-basedGCM

SAM

70

60

50

40

30

20

10

Scen

ario

SIN

R [in

dB]

C B R | C B R

| C B R | RDistance- and Walls Based GCM

Distance-basedGCM

C – Complete RangeB – Range points within BuildingR – Range points within Room


[CONTD.]

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

10

20

30

40

50

60

1 2

Scenarios D1 & D2

Sce

nario

SIN

R [i

n dB

m]

D1 D2Scenarios

Scen

ario

SIN

R [in

dB]

30-FBS 6-ChannelsSAM

30-FBS 8-ChannelsSAM

R – Range points within Room

R | R


[CONTD.]

OBSERVATIONS

Scenario SINR α # of Sub-channels Scenario SINR α 1/Number of FBSs

Scenario SINR varies with SINR measurement methods Drop in scenario SINR results due to inaccurate localisation

GCM v/s SAM – No clear winner in channel allocation. (GCM faster compared to SAM)

CONCLUSION

Localization with awareness of surrounding environment Localization within room (Accuracy up to 88%; Avg. Distance

Error = 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

THANK YOU

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