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The Lucent Cellular Optimization ToolPowerPoint Presentation

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
- .*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

- We want to tune cellular systems for:
- Contract requirements
- Peak performance

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

- Drive around making measurements
- adjust some parameters
- repeat until done

Ocelot Approach

- Model system numerically
- Compute performance measures for model
- Numerically optimize performance

OCELOTOptimization

Ocelot-Optimized Design

Coverage: 98%

Initial Design

Coverage: 84%

Uncovered Areas.

Covered Areas.

Sectors colors: Tilt

00

7 0

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

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 CapacityNetwork coverage and network capacity

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

Large

Antenna Tilt

Small

Demo of OCELOT in Action

- Optimization of a CDMA Market

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

- 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

- 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

- 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

- Import maps
- Map overlay: line segment intersection

- Clipping against polygons
- user supplied, or
- “Autoboundary”

Autoboundary

- Base-station locations imply phone traffic density
- Code:
- find a-shape
- Minkowski sum with square
- Cull

a-shape

Simplify mesh: edge contractions

- Keeping all details of a map can cost too much
- Idea: contract short edges

Simplify, continued

- Code: speed via union-find, priority queue
- Similar to Hoppe and others

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

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

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

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

- 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)

- Pilot signal (antenna to phone)
- run out of:
- Base-station power
- Base-station Walsh codes
- phase offsets

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 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

- 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.

- 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

- 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

- 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

- 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

- 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

- 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

- 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

- 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

- 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

- 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

- 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

- ideas from theory help a lot
- On the plus side, somebody cares
- On the minus side, somebody cares

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