cell design to maximize capacity in cdma networks/67531/metadc30929/m2/1/high_res… · cdma...
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Cell Design to Maximize
Capacity in CDMA Networks
Robert Akl, D.Sc.
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Outline
CDMA inter-cell effects
Capacity region
– Base station location
– Pilot-signal power
– Transmission power of the mobiles
Maximize network capacity
Mobility
Call admission control algorithm
Network performance
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CDMA Capacity Issues
Depends on inter-cell interference and
intra-cell interference
Complete frequency reuse
Soft Handoff
Power Control
Sectorization
Voice activity detection
Graceful degradation
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Relative Average Inter-Cell
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Soft Handoff
User is permitted to be in soft handoff to
its two nearest cells.
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Soft Handoff
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Inter-Cell Interference Factor
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Capacity Region
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Network Capacity
Transmission
power of mobiles
Pilot-signal power
Base station
location
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Power Compensation Factor
Fine tune the nominal transmission power of the mobiles
PCF defined for each cell
PCF is a design tool to maximize the capacity of the entire network
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Power Compensation Factor (PCF)
Interference is linear in PCF
Find the sensitivity of the network
capacity w.r.t. the PCF
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Sensitivity w.r.t. pilot-signal power
Increasing the pilot-signal power of one
cell:
– Increases intra-cell interference and decreases
inter-cell interference in that cell
– Opposite effect takes place in adjacent cells
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Sensitivity w.r.t. Location
Moving a cell away from neighbor A and
closer to neighbor B:
– Inter-cell interference from neighbor A
increases
– Inter-cell interference from neighbor B
decreases
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Optimization using PCF
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Optimization using Location
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Optimization using Pilot-signal Power
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Combined Optimization
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Twenty-seven Cell CDMA
Network
Uniform user
distribution profile.
Network capacity
equals 559
simultaneous
users.
Uniform placement
is optimal for
uniform user
distribution.
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Three Hot Spots
All three hot spots have a relative user density of 5 per grid point.
Network capacity decreases to 536.
Capacity in cells 4, 15, and 19, decreases from 18 to 3, 17 to 1, and 17 to 9.
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Optimization using PCF
Network capacity
increases to 555.
Capacity in cells 4,
15, and 19,
increases from 3 to
12, 1 to 9, and 9 to
14.
Smallest cell-
capacity is 9.
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Optimization using Pilot-signal Power
Network capacity
increases to 546.
Capacity in cells 4,
15, and 19,
increases from 3 to
11, 1 to 9, and 9 to
16.
Smallest cell-
capacity is 9.
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Optimization using Location
Network capacity
increases to 549.
Capacity in cells 4,
15, and 19,
increases from 3 to
14, 1 to 8, and 9 to
17.
Smallest cell-
capacity is 8.
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Combined Optimization
Network capacity
increases to 565.
Capacity in cells 4,
15, and 19,
increases from 3 to
16, 1 to 13, and 9 to
16.
Smallest cell-
capacity is 13.
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Combined Optimization (m.c.)
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Call Admission Control
Fix cell design parameters
Design a call admission control algorithm
– Guarantees quality of service requirements
– “Good” blocking probability
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Our Model
New call arrival process to cell i is
Poisson.
Total offered traffic to cell i is:
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Handoff Rate
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Blocking Probability
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Fixed Point
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Net Revenue H
Revenue generated by accepting a new
call
Cost of a forced termination due to
handoff failure
Finding the derivative of H w.r.t. the arrival
rate and w.r.t. N is difficult.
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Maximization of Net Revenue
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3 Mobility Cases
No mobility
qii= 0.3 and qi= 0.7
Low Mobility High Mobility
||Ai|| qij qii qi
3 0.020 0.24 0.7
4 0.015 0.24 0.7
5 0.012 0.24 0.7
6 0.010 0.24 0.7
||Ai|| qij qii qi
3 0.100 0.0 0.7
4 0.075 0.0 0.7
5 0.060 0.0 0.7
6 0.050 0.0 0.7
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Maximization of Throughput
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Conclusions
Solved cell design problem.
Formed general principles on cell design.
Designed a call admission control algorithm.
Calculated upper bounds on throughput for a given network topology and traffic distribution profile.