The lucent cellular optimization tool
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The Lucent Cellular Optimization Tool. Chandra Chekuri, Ken Clarkson, John Hobby, Howard Trickey, Lisa Zhang Larry Drabeck, John Graybeal, Georg Hampel, Paul Polakos Peiwen Hou, Bhushan Apte. Why “Ocelot”?. .*w.*o.*t.* blowout bowknot figwort madwort ragwort ribwort rowboat swot swotted

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The lucent cellular optimization tool

The Lucent Cellular Optimization Tool

Chandra Chekuri, Ken Clarkson, John Hobby, Howard Trickey, Lisa Zhang

Larry Drabeck, John Graybeal, Georg Hampel, Paul Polakos

Peiwen Hou, Bhushan Apte


Why ocelot

Why “Ocelot”?

  • .*w.*o.*t.* blowout bowknot figwort madwort ragwort ribwort rowboat swot swotted

  • .*t.*c.*o.* outcome outcrop portico stucco stuccos taco tacos taction talcose tobacco

  • .*t.*ce.*o.* trecento

  • .*t.*o.*ce.* thoraces toepiece trounce twopence

  • .*ce.*.*op.* acetophenetidin cellophane cephalopod cephalopodan mycetophagous

  • .*op.*ce.* copacetic coparcenary coparcener coppice opalescence opalescent opulence populace

  • .*s.*ce.*o.* saucebox scenario seicento

  • .*ce.*p.*t.* centripetal centripetally cephalization cephalometry cephalothorax chemoreception chemoreceptive concept cesspit


The problem

The Problem

  • We want to tune cellular systems for:

    • Contract requirements

    • Peak performance


We can change

We can change:

  • Antenna Power

  • Antenna Tilt (with difficulty)

  • Antenna Azimuth (ditto)

  • (GSM) frequency plan

  • Not antenna location

  • Plausible for a metropolitan area market.


Current practice drivetests

Current Practice: Drivetests

  • Drive around making measurements

  • adjust some parameters

  • repeat until done


Ocelot approach

Ocelot Approach

  • Model system numerically

  • Compute performance measures for model

  • Numerically optimize performance


Ocelot optimization

OCELOTOptimization

Ocelot-Optimized Design

Coverage: 98%

Initial Design

Coverage: 84%

Uncovered Areas.

Covered Areas.

Sectors colors: Tilt

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Performance measures

Performance measures

  • Coverage

    • should serve all of market, without “holes”

  • Capacity

    • should serve as many calls as possible

  • There is a tradeoff between these two goals


Max coverage vs max capacity

Low Coverage

High Coverage

High Pilot Pollution in Center

=> Large Coverage Hole

Reduced Pilot Pollution in Center:

=> Small Coverage Holes

Cells have equal traffic load

=> High Effective Network Capacity

Traffic load unbalanced

=> Small Effective Network Capacity

High Capacity

Low Capacity

Max Coverage vs. Max Capacity

Network coverage and network capacity

cannot be optimized at the same time =>Example: 5-Cell Scenario

Large

Antenna Tilt

Small


Capacity vs coverage

Max Capacity

Max Coverage

Compromise

Capacity vs. Coverage

50%

3%


Demo of ocelot in action

Demo of OCELOT in Action

  • Optimization of a CDMA Market


The lucent cellular optimization tool

ROAD MAP OF CITY X


The lucent cellular optimization tool

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CELLS OF WIRELESS NETWORK


The lucent cellular optimization tool

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OCELOT COVERAGE OPTIMIZATION STARTS


The lucent cellular optimization tool

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INITIAL COVERAGE

98

82

84

86

88

90

92

94

96

100 %


The lucent cellular optimization tool

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98

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

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The lucent cellular optimization tool

Sectors colors: Tilt

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98

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100 %


The lucent cellular optimization tool

Sectors colors: Tilt

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98

82

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90

92

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96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

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98

82

84

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88

90

92

94

96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

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98

82

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90

92

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96

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The lucent cellular optimization tool

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98

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90

92

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96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

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98

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90

92

94

96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

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98

82

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88

90

92

94

96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

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7 0

98

82

84

86

88

90

92

94

96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

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FINAL COVERAGE

98

82

84

86

88

90

92

94

96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

00

7 0

Initial Design

INITIAL COVERAGE

98

82

84

86

88

90

92

94

96

100 %


The lucent cellular optimization tool

Sectors colors: Tilt

00

7 0

Ocelot Optimized Design

FINAL COVERAGE

98

82

84

86

88

90

92

94

96

100 %


Ocelot performance

OCELOT Performance

  • Significant Performance Improvements using Ocelot

    • Coverage : 5% to 20%

    • Capacity : 20% to 80%

  • Field trials have Demonstrated Ocelot Optimization is Superior to Drive Test Optimization

  • Ocelot successfully used worldwide

    • Used in ~30 CDMA IS95 markets

    • Used in ~10 GSM markets

    • Demo UMTS markets


Technical challenges

Technical Challenges

  • Traffic modeling (pattern of calls)

  • Predicting pathlosses (signal strengths)

  • Modeling network performance

    • Hard: phones and base-stations interact

    • Computing derivatives (for optimization)

  • User interface should be:

    • Robust to errors

    • Responsive while computing


Outline

Outline

  • Traffic modeling and meshes

  • CDMA system modeling

    • Reverse-link interference and power control

    • Power amplifier sharing

  • The GUI

  • Theme: many applications of algorithmic ideas


Traffic modeling

Traffic modeling

  • Phone traffic pattern is modeled with a “mesh”

    • planar graph

  • Evaluate system based on calls from mesh edges

  • Mesh is from a street map (if available)

    • Street map density is roughly population density

    • People make calls from cars

  • Edges of mesh also have traffic weights


Geometric operations

Geometric operations

  • Import maps

    • Map overlay: line segment intersection

  • Clipping against polygons

    • user supplied, or

    • “Autoboundary”


Imported map

Imported map


Phone traffic on roads

Phone traffic on roads


Why we need clipping

Why we need clipping


Autoboundary

Autoboundary

  • Base-station locations imply phone traffic density

  • Code:

    • find a-shape

    • Minkowski sum with square

    • Cull


A shape

a-shape


Minkowski sum

Minkowski Sum


Culled autoboundary

Culled Autoboundary


Simplify mesh edge contractions

Simplify mesh: edge contractions

  • Keeping all details of a map can cost too much

  • Idea: contract short edges


Simplify continued

Simplify, continued

  • Code: speed via union-find, priority queue

  • Similar to Hoppe and others


Simplify cell ownership

Simplify: cell ownership

  • Limit contractions within cell so that it has “enough” vertices

  • Cheaper to keep fractional cell ownership: new vertex is half green, half blue


Result of simplify

Result of simplify


Traffic modeling fake maps

Traffic modeling: fake maps

  • user can generate grid, or

  • “Automesh”

    • Want: each cell to have enough mesh vertices

    • Idea: simplify a very fine mesh

    • Code: approximate cell by intersecting

      • inside autoboundary

      • within angular wedge visible to antenna

      • Voronoi region of basestation


An automesh

An automesh


Automesh continued

Automesh, continued

  • We don’t really compute intersection, but instead:

    • compute bounding box of Voronoi region

    • estimate area of cell using Monte Carlo, testing each point

    • generate grid such that enough grid points are in cell


Voronoi autoboundary

Voronoi + autoboundary


Cdma cellular systems

CDMA Cellular Systems

  • “Code Division Multiple Access”

  • spread spectrum, but not frequency hopping

    • spread spectrum was co-invented by 40’s movie star Hedy Lamarr

  • all conversations use common bandwidth

  • phone and antenna agree on random {+1, -1}-codevector v

  • phone sends bit: 1 sent as v, 0 sent as -v

  • calls by other phones are “random” noise


Why you can t get through

Why you can’t get through

  • Each phone’s call is carried by (at least) one antenna

  • Too much interference on:

    • Pilot signal (antenna to phone)

      • phone finds antenna using pilot

    • Reverse link (phone to ant)

    • Forward link (antenna to phone)

  • run out of:

    • Base-station power

    • Base-station Walsh codes

    • phase offsets


System modeling reverse link interference

System Modeling:Reverse-link interference

  • We model traffic activity with a multiplier t:

    • the number of attempted calls for antenna k is tDk

  • Capacity:

    • The max t with blocking <2%.


Power control and reverse link interference

Power control andReverse-link interference

  • Power control

    • Each phone adjusts its power, prodded by its antenna

    • So nearby phones emit less power

    • An antenna receives the same power from all its phones

    • Call this power gk, for some antenna k

    • For some Ajk, antenna j gets interference

      • AjkgktckDk, where

      • tckDk is the number of calls k is carrying

  • That is, we lump the phones of antenna k together in their effect on antenna j.


The interference model

The interference model

  • For antenna j, there is interference:

    • From other phones

    • From thermal and other external noise

  • Power levels are interrelated:

    • phone power depends on interference, but

    • interference depends on power of other phones


The interference model cont

The interference model, cont.

  • That is, the power control target is

  • Aj is the j’th row of A={Ajk}

  • gj is the signal received by antenna j

  • nj is the noise

  • Vector dj has dj=gjtjc , proportional to total power from antenna k phones


Phone power limits

Phone power limits

  • Phones can emit at most 200 mW

  • There is a bound gj¸gj

    • based on the phone farthest away

  • Our estimate of phone power is:


But also poisson loads

But also: Poisson loads

  • We have inequalities on vectors, a linear programming problem

    • note that this is to evaluate a design, not to optimize it

  • But really, traffic comes and goes

  • A better approach models the offered phone traffic as Poisson/exponential


Power amplifier loads a loss model approach

Power amplifier loads:a “loss model” approach

  • Base-station power amplifiers are costly

  • They may limit data calls in UMTS and WCDMA

  • We use an approximate “multi-service loss model” approach


Loss models

Loss models

  • Generically: a resource is shared by multiple services

  • Users arrive Poisson, use different amounts of resource

  • We want to know: probability that resource runs out

  • Markov-chain steady-state probabilities can be found cheaply

    • a simple recurrence (we also need derivatives)

    • n state chain needs O(n) time, not O(n3) time

  • We discretize PA power levels, power needs of services

  • We ignore random variation in power needs


The gui

The GUI

  • First cut: tcl/tk and c

  • Ported/Rewritten by Howard Trickey to Visual C++, MFC

    • much faster

    • prettier

    • platform familiar to developers

    • interface familiar to users

    • who cares about platform independence?

    • However: most code runs on Unix first


The gui internal

The GUI, internal

  • evaluation, pathloss, ampl child processes

    • pipes to child stdin, stdout, stderr

    • educational

  • function calls to threaded “plugins”

    • plugins also run standalone

  • throw and catch exceptions

    • interacts with threading


The gui theory challenges

The GUI: theory challenges

  • threads+exceptions+processes = buggy

  • state of the GUI might be modeled as an FSM,

    • not really finite, of course

    • what is the specification?


Other geometric techniques

Other geometric techniques

  • 2d convex hull

    • Radio strength computations

    • Could also do ray-tracing

  • Nearest neighbor searching

    • Pathloss lookup and GUI

  • 2d coordinate-wise extrema

    • Coverage/capacity tradeoff

  • Splines


Other techniques

Other techniques

  • Optimization and numerical linear algebra

  • Multi-service loss models

  • Theory of non-negative matrices

    • (Interference matrix A is non-negative)

  • Lossy compression techniques

  • briefly: string matching

    • for guessing the meaning of user-supplied column headers for data


Conclusions

Conclusions

  • A rich problem area

    • ideas from theory help a lot

      • started out to solve problem, not to apply theory

    • algorithms from theory help some

    • realistic system modeling is an endless challenge for engineering and theory

  • On the plus side, somebody cares

  • On the minus side, somebody cares


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