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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
3. INTERFERENCE MANAGEMENT ALGORITHM
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
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
1. ALGORITHM DESCRIPTION
2. ALGORITHM RESULTS
6. CONCLUSION
CONTENTS
4. LOCALIZATION ALGORITHM
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
Fig. 3(a). Indoor Environment Model
4.1. LOCALIZATION ALGORITHM – ENVIRONMENT MODELING
Fig. 3(b). Signal propagation through Indoor
Environment Model
4.1. LOCALIZATION ALGORITHM – ENVIRONMENT MODELING
4.1. LOCALIZATION ALGORITHM – ENVIRONMENT MODELING
4.1. LOCALIZATION ALGORITHM – ENVIRONMENT MODELING
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
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
1. ALGORITHM DESCRIPTION
2. ALGORITHM RESULTS
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
5.1. CAS – HEURISTIC SCHEMES DESCRIPTION
5.1. CAS – HEURISTIC SCHEMES DESCRIPTION
[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
5.2. CAS – SIMULATION RESULTS
[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
5.2. CAS – SIMULATION RESULTS
[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
QUESTIONS?