spec markets v3

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SPECTRUM MARKETS

DySPAN Conference, Aachen, Germany

1

May 2011

Randall Berry, Michael HonigDepartment of EECSNorthwestern University


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Spectrum Management2

Policy

Economics

CommunicationsEngineering

May 2011Spectrum Markets Tutorial, DySPAN Conference


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Spectrum Management3

Policy

Economics

CommunicationsEngineering

R. Berry, MLHEECS

Rakesh VohraKellogg MEDS



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High Profile Issue


4

Chicago TribuneApril 15, 2011


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Why This Tutorial?

Timely and relevant

New policies are needed for spectrum allocation.

Markets are natural policy candidates.

Markets for spectrum pose uniquechallenges/questions.

Definition of property rights, interference externalities

Efficiency, incentives, wireless system design

Interplay between economics and engineering issues


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This Tutorial

Is NOT about

Large-scale spectrum auctions

Related policy issues

Analytical methodology

IS about

Fundamental technical and micro-economic issues andapproaches to defining spectrum rights, markets

Impact on wireless systems and servicesMay 2011Spectrum Markets Tutorial, DySPAN Conference

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Objectives

Put spectrum markets into policy context (US-centric)

Explain technical challenges and tradeoffs with

defining spectrum property rights

Illustrate with basic models

Describe different types of market structures

Contrast spectrum markets with other spectrumsharing models (commons/white space)


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Background and Motivation (MH)

Spectrum Market Design (RB)Market Organization (MH)

Concluding Remarks (MH)

Outline

May 2011

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Spectrum Markets Tutorial, DySPAN Conference


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History

Spectrum sharing modelsMotivation for spectrum markets

Background and Motivation

May 2011

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Limited Supply of Spectrum


10

good for cellular(300 MHz to 3 GHz)


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Spectrum Crunch


12

Pe

tabytespermon

th


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Regulation Prior to 1927: Open to All


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Earliest uses of wireless forship-to-ship, ship-to-shorecommunications.

Broadcast radio begins in 1921.

Licenses issued by theDepartment of Commerce.


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Two Landmark Cases


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Hoover vs Intercity Radio, 1923United States vs Zenith Radio, 1926

Herbert Hoover,US Sec. of Commerce

Department of Commerce has noauthority to regulate licenses.

Broadcasting boom:200 new stations appeared in < 6 months.

Interference created chaotic radioenvironment.


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Spectrum Property Rights: A False Start


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Congress subsequently passed legislation

prohibiting spectrum property rights

Licenses issued for 90 days.

Tribune vs Oak Leaves

Broadcasting, 1926

Property right allowed basedon homesteading

Interfering stations could be fined.


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Regulation since 1927: Command and Control

Federal Radio Commission (FRC)

established in 1927.

Federal Communications Commission (FCC)


Maintains authority to

Grant / renew / deny licenses for spectrum use. Assign applications to particular frequencies.

Police content and use


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May 2011


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Regulation since 1927: Command and Control

Federal Radio Commission (FRC)


Federal Communications Commission (FCC)


Maintains authority to

Grant / renew / deny licenses for spectrum use. Assign applications to particular frequencies.

Police content and use


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Wise old man approach to spectrum allocation


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The Spectrum Paradox

Spectrum is a scarce resource

Spectrum is underutilized


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May 2011


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Spectrum is a Scarce Resource


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beachfrontpropertyNearly $20B netted for 700 MHz auctions in 2008.


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Spectrum is Underutilized


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Spectrum measurements in New York City and Chicago conducted by Shared Spectrum Co.


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Problems with Command and Control

An economists critique

Requires excessive information overhead Difficult to estimate value (utility) of a frequency

assignment

Encourages rent-seeking and facilitates entry barriers


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An Economists Proposal


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Ronald Coase,

1991 Nobel Laureate in Economics

Introduce spectrum property rights, sell to highestbidders, do not restrict use.

R. Coase, The federal communications commission,

J. Law and Economics, pp. 140, 1959.

Coases Theorem: In the absence of transactioncosts, spectrum owners will trade rights so that theoutcome allocates spectrum to best use.

Role of government should be to minimize transaction costs.

May 2011


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An Economists Proposal


25

Ronald Coase,

1991 Nobel Laureate in Economics

Introduce spectrum property rights, sell to highestbidders, do not restrict use.

R. Coase, The federal communications commission,

J. Law and Economics, pp. 140, 1959.

Spectrum auctions finally introduced in the 1990s.Restrictions on use remain.

Coases Theorem: In the absence of transactioncosts, spectrum owners will trade rights so that theoutcome allocates spectrum to best use.

May 2011


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Problems with Command and Control

An economists critique:

Requires excessive information overhead

Difficult to estimate value (utility) of a frequency assignment

Encourages rent-seeking and facilitates entry barriers

An engineers critique:

Demand for different applications varies over time andgeographic locations.

Static assignments cannot exploit statistical multiplexing.

New technologies can facilitate more efficient spectrumsharing.


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May 2011

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Engineering Approach to Spectrum Crunch

Add intelligence to mobiledevices Frequency agility

Wideband sensing

Interference avoidance

Adaptive quality of service(context aware)

Enables spectrum scavengingCognitive Radio

Mitola and Maguire (1999)



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History

Spectrum sharing models

Motivation for spectrum markets

Background and Motivation

May 2011

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Spectrum Sharing Models

Exclusive use

Commons

Hierarchical


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May 2011

30 Exclusive Use


Spectrum owned by government

Licensed to particular application,service provider

Rigid use rules

Spectrum is private property

Applications, technical constraintsdecided by markets


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May 2011

31 Exclusive Use

Liberal licenses Spectrum publicly owned, but licenses can be transferred,

liberal use rules

Secondary markets (2003)


Spectrum owned by government

Licensed to particular application,service provider

Rigid use rules

Spectrum is private property

Applications, technical constraintsdecided by markets


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May 2011

32 Spectrum Commons

State-regulated Spectrum owned by government

Etiquette rules part of industry standard (802.11)

Privately owned Owner sets rules, polices band Revenue from selling approved equipment

Unlicensed Requires etiquette rules

for sharing



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May 2011

33 Hierarchical

State-regulated Spectrum owned by government Use rules for secondary users part of standard (802.22)

Private contracts with spectrum scavengers Interference levels/payments set by mutual agreement

Primary and secondary users

Secondary users must notdisrupt primary users

Relies on cognitive radio



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May 2011

34 Hierarchical: Technologies

Underlay: low-power, spread spectrum forsecondary users

Overlay: exploit white spaces left by primary users

Primary and secondary users

Secondary users must notdisrupt primary users

Relies on cognitive radio



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May 2011

35 Hybrid Models

Spectrum designated for exclusive use could beoperated as a commons and/or with secondaryusers.

Underlay/overlay can be used to facilitate furthersharing.

Spectrum scavenging can increase utilization.



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Current Allocations Mix of:

restricted use bands (e.g., broadcast TV) liberalized licenses (cellular)

state-regulated commons (WiFi)

Active trading of liberalized licenses amongcommercial service providers About 10 billion MHz-pops annually since 2003

[Mayo & Wallsten `10]

US Policy trends have favored assignments of unlicensedspectrum over liberalized licenses 955 MHz unlicensed vs 422 MHz licensed in the US (2008)


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May 2011

37 Why Unlicensed Spectrum?

Pushed by DARPA



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May 2011

38 Why Unlicensed Spectrum?

Pushed by DARPA Military needs distributed, dynamic methods for

spectrum sharing across military units

Also by Google, Apple Facilitates 3rd-party software applications

Success of WiFi Interference not a major issue for local coverage,

light loads



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May 2011

40 Engineering Issues

Difficult to guarantee Quality of Service Limits applications

Problems with secondary user model Sensing problematic, constraints compromises utility

WiFi does not scale; inappropriate for wide-area

data in urban settings



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May 2011

41 Economic Issues

No means for reallocating spectrum to applicationswith higher utility

e.g., wide-area data with coordinated interferencemanagement

No direct means to move incumbent applications toanother band/wireline service

e.g., wireless mics, broadcast TV

Congestion effects may adversely affect competitionamong licensed service providers (more later)



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May 2011

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The Case for Liberal Licenses

Provides incentives for service providers to invest in

infrastructure for wide-area coverage, interferencemanagement [Hazlett, 2010]

Over $20B annual network capital expenditures

Virtually no infrastructure investments for unlicensedbands (U-PCS, 3.5 GHz WiMax band)

Previous issues substantial opportunity costs for

unlicensed spectrum Liberal licenses allow private commons, scavenging

So far, not economically attractive



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Are Secondary Markets Active?

Service providers are not issuing short-term leases.

Companies with spectrum (e.g., Boeing) are notreselling.

But There are active markets for:

transferring large blocks of spectrum among

service providers, wholesale use of spectrum and infrastructure

(e.g., Kindle)


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Current State of Affairs

Large parts of the useful spectrum remain underutilized.

Restricted supply of spectrum with liberalized licenses.

Cellular spectrum is extremely expensive.

Service providers encouraged to build out national footprint.

Fosters the development of expensive (spectrally efficient)systems.

Unlicensed spectrum is increasing.


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Do We Need Spectrum Markets?48

NU, April 2009

A more fundamental question:

Is spectrum scarce or abundant?

Spectrum is abundantuse Commons Model

Spectrum is scarce:

Commons modeltragedy of the commons



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Rate Calculation

Extensive spectrum sharing

Roughly 1 GHz between 150 MHzand 3 GHz

Cellular Infrastructure

System Assumptions

No intra-cell interference

(time-division multiplexing)

Limited inter-cell interference.

All users are active all the time.


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May 2011


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S S Ab d ?


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Is Spectrum Scarce or Abundant?


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2 Mbps per user seems like a lot, but recall the

assumptions:

1 GHz of shared bandwidth, no fading

Infrastructure of access points (200 m radius)

Optimized frequency reuse

Spectrally efficient modulation

Also, less expensive spectrum encourages lower-cost,spectrally inefficient systems.

May 2011


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C M k


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Commons vs Market


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May 2011

Spectrum price ($/Hz)

cost of interference < market transactions costs Use commons model

Quantityofspectrum(Hz

)Spectrum marketCommons

supply

demand

p*


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Asset Design

Market Mechanisms

Examples

Market Design

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Interference


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Interference

A key issue for spectrum is defining propertyrights regarding interference.

Some possibilities:

Limits on received power

Limits on transmitted power Cognitive approaches

Flexible limits negotiated via bargaining.


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or


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Asset Specifications


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Asset Specifications

Spatial/temporal scales Frequency scale

Power allocation

Amount of sharing Interference management

Device or

Technology


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Asset Design

Market Mechanisms

Examples

Market Design

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Pathological Example?


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g p

No.

Myerson-Satterthwaite theorem shows that withprivate information, under very general conditions

there is no way for two parties to trade that is

efficientand individually rational.

Suggests market design matters.


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Example: 2nd Price Auction


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Mechanism:

Users submit bids.Mechanism allocates good to highest bidder

Users pay 2nd highest bid.

Users can be viewed as playing a non-cooperativegame.

Use equilibrium concepts from game theory to studyperformance.


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Outcome


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2nd Price

Auction

$4

$2

Pays $2

Pays noting

A Little Terminology


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2nd price auction is a direct revelation mechanism.

Ask agents to bid their valuation

A direct revelation mechanism is incentive

compatible if truth-telling is weakly dominant.


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Revelation Principle


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Loosely, an equilibrium obtained under anymechanism can be obtained by an incentivecompatible, direct revelation mechanisms.

In terms of characterizing possible outcomes, wlogwe can consider only direct revelation, incentivecompatible mechanisms.


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Vickrey-Clarke-Groves (VCG)


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VCG mechanisms generalize 2nd price auction toarbitrary goods.

Incentive compatible, direct revelation mechanism with the

efficient outcome.


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Dynamic Interference Free Allocation9595


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Let agents bid on every asset.

Allocate an interference free set of assets with thehighest bids.


9595

2

1 3

4

5

$3, $5, $2

$1, $2, $1

$1, $1, $1

$7, $5, $3

$2, $1, $2


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VCG payments9797


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Consider Agent 1?


9797

2

1 3

4

5

$3, $5, $2

$1, $2, $1

$1, $1, $1

$7, $5, $3

$2, $1, $2


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VCG Payments9999


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Consider Agent 1

Remove Agent 1s bids

Re-calculate allocation

Payment = (6-2) + (2-0) = $6


2

1 3

4

5

$5, $2

$2, $1

$1, $1

$5, $3

$1, $2

Dynamic Interference Free Allocation100


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Overhead: linear in number of assets.

Complexity: NP-hard!

Need to find multiple maximum weight independent sets.


2

1 3

4

5


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Approximations102


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Consider a greedy approximation:

Order assets by bids and assign from highest to lowest

if possible.


2

1 3

4

5

$3, $5, $2

$1, $2, $1

$7, $5, $3

$2, $1, $2

2

$1, $1, $1

5


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Truthful Approximation104


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Issue with previous algorithm is that VCG paymentsare not suitable for approximate allocations.

For some cases can get truthful approximations by

changing payments charged to each agent.

E.g. VERITAS (Zhou,Gandhi,Sur,Zheng 08)



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Costs110


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Costs:

Remains NP-hard even if interference costs are

constrained to be no greater than an arbitrarily smallfraction of the revenue.

Reduction from graph partitioning.

Constraining the topology?


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Linear Relaxation112


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Prop. If G is a line, the LP is totally unimodular. Also can solve efficiently if G is a ring.

Some Simple Approximations

A 1/((G) +2) approximation

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A 1/( (G) +2)-approximation: Solve LP.

Divide zjji

variables into (G) +1 sets {Wi} so that adjacentedges are in different sets. Let W0 be set of all xij variables.

Find the set of variables that contribute the most to the objectiveof the LP.

Round these to best integer solution & set other variables to anyconsistent values.

A greedy approximation: give each asset j to agent with largest rij. gives (1+ 2) approx. where = max ratio of interference costs

to rijs.


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Secondary User Model115


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Boundary region Zsupports only secondary usersthat must notinterfere with a different primary users in A or B.

Agent igets revenue rij from assetj {A, B}

Agent igets revenue ABi from cell boundary Z

Agent igets additional revenue ZAi from owning both Zandneighboring asset A.

Asset A Asset BBoun

dary

Z

Spectrum Markets Tutorial, DySPAN Conference May 2011

Secondary User Model116


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In full network, create one

boundary region for eachpair of assets.

Secondary users occupyone or more boundaries.

Allow users to be bothprimary or secondary.

Again optimal allocation issolution to an integerprogram.



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Another Alternative Model

S f did t if h i t f d d

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So far did not specify how interference reducedbetween assets.

Suppose assets correspond to cells.

Interference controlled by adapting the boundary ofcells.

But this effects all neighbors.



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Interference Region120

L


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L

coverage radiusRiA [0, L/2]

Agent can adjust coverage radius by changing the power.

Value is proportional to area: riA = 4wiA RiA2 .

Area reduced by interference footprint of neighbor.

Cell A



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Bit Pipe Model

Wholesale contract with cellular provider

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p


Kindle


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Bit Pipe Model: Properties

Wide-area coverage, high mobility

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Interference management

Quality of Service guarantees

Facilitates new wide-area wireless services

Well-matched to lower frequency assignments


Higher Frequencies

Wide-area coverage becomes difficult

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Interference management becomes easier

Possibility for distributed, dynamic spectrum

assignments



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Owning vs Leasing135


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Owned spectrum asset has

unlimited time duration;traded as property (e.g., land).

Leased spectrum asset has limited

time duration;available through local spot market


Owning vs Leasing136


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Owned spectrum asset has

unlimited time duration;traded as property (e.g., land).

Leased spectrum asset has limited

time duration;available through local spot market

Owners can deploy services or rent / lease spectrum assets. Service providers need not be spectrum owners!


Two-Tier Spectrum Market137

location 3.5 GHz 3.7 GHz3.6 GHzband

1


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Owner A Owner B Owner A

Owner A Owner B Owner A

Owner A Owner C Owner C

cell

Owners A, B, C,

Spectrum Broker

Service providers(Acme Wireless)

Service requests

1

5

34

2

8

67

9

10

cell

cell



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Lower-Tier Spot Market: Properties139

Spectrum Broker


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Owners A, B, C,

Spectrum Broker


Immediate access, rapid (automated) transactions

Low transaction costs

Facilitates local services No need to build out large footprint



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Spectrum Contracts141

Spectrum Broker


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Owners A, B, C,

p



Contracts can be arranged across: Frequency (spread spectrum, underlay)

Locations (mesh networking)

Time (time-of-day, futures, scavenging)

Variable QoS guarantees (statistical)

Integration142

Spectrum Broker


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Owners A, B, C,

p



Broker may integrate allocation of Access points (property leases)

Equipment

Spectrum Lowers entry barriers for new service providers

Commons versus Market143

) Spectrum marketCommons


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Spectrum price ($/Hz)

Commons/market boundary depends on associatedcosts.

Quan

tityofspectrum

(Hz Spectrum marketCommons

supply

demand

p*

May 2011


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z) Spectrum marketCommons


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Spectrum price ($/Hz)Quan

tityofspectrum

(Hz Spectrum marketCommons

supply

demand

p*

cost of interference < market transactions costs Use commons model

May 2011


z) Spectrum marketCommons


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Can we shift the boundary to the right with distributedinterference management schemes?

Spectrum price ($/Hz)Quan

tityofspectrum

(Hz p

supply

demand

p*

May 2011

Local Transactions147


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Routers use the same channel, cause little interference


Local Transactions148


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Would cause excessive interference.


Deterence Price149


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$ $

Pay new user to not setupaccess point in exchange forsharing capacity.


Usage Price150


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$ $

Set up community ofaccess points, charge fee forsharing capacity (Fonera).


Pricing and Efficiency151

$ ??? $


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Deployment game: each user decides whether or not to setupan access point given a fixed deterrence price from neighbors.

Deterrence pricing can substantially increase efficiency,

mitigate interference [Bae et al, DySPAN `09] .


$

Setup access point or share?

??? $

May 2011

Competition with a Commons152


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Analysis of white space policy


TV White Space

FCC recently announcedrules for use as unlicensed

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u es o use as u ce sed

commons Devices must check data

base to see if spectrum is

available before using.


TV White Space

FCC recently announcedrules for use as unlicensed

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commons Devices must check data

base to see if spectrum is

available before using.

Advocates: lowers entry barriers for new services

Detractors: tragedy of the commons


Observations

Lower frequencies than WiFi longer propagation

better coverage

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more interference

Incumbents will compete with services in TV whitespace.


Observations

Lower frequencies than WiFi longer propagation

better coverage

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more interference

Incumbents will compete with services in TV whitespace.

How will additional white space affect service

providers and consumers?


Scenario157

SP 3SP 1 SP 2


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frequency

Incumbent service providers (SPs) have exclusivelicensed bands.

Scenario158

SP 3SP 1 SP 2 commons


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Incumbent service providers (SPs) have exclusivelicensed bands.

All incumbents and new entrants have access to commons(unlicensed band).

How does this additional spectrum affect total welfare?

Analyze using framework for competition in congestedmarkets [Acemoglu, Ozdaglar `07]

frequency

Model: Summary159



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Each SP competes for pool of customers byannouncing prices for licensed and unlicensed

services.

Customers choose SP based on

Total price = Announced price + Congestion cost

frequency

Results: Summary160



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The equilibrium price of commons spectrum is zero.(Total price = congestion cost)

Adding unlicensed spectrum can decrease totalwelfare (consumer + SP revenue).

Happens over a substantial range of unlicensed

bandwidth.

frequency

Model: Revenue161



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frequency

Each SP i chooses prices to maximize revenue:

i= p

iq

i+p

i

wqi

w

pi : price for licensed band

piw : price for unlicensed band

qi , qiw : quantity of customers served

Model: Congestion162

frequency

SP 3SP 1 SP 2commons


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Customers in band i experience a congestion (latency)cost l(q

i

), which is increasing convex.

Customers in commons experience a congestion costlw(q

w), where qw includes all unlicensed users.

Total (delivered) price in band i:piw+ l(qi)

frequency

`(q3)`(q1) `(q2)`w(P

i qwi )


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Total Welfare: Monopoly SP164

Demand curvePrice


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Quantity q(customers served)

To

talp

rice

*

q*

*maximizes revenue

Total Welfare: Monopoly SP165

Demand curvePrice


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Quantity q(customers served)

To

talp

rice

*

q*

`(q)

`(q)

*maximizes revenue


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Total Welfare: Two SPs167

Demand curvePrice


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*maximizes revenue

To

talpr

ice

*

P1*

P2*

q1* q2* Quantity q(customers served)

consumersurplus

`(q)

SP 2s revenue

Price of Unlicensed Spectrum

In equilibrium announced price is zero.*

Otherwise SP can increase revenue by lowering.

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Total price of unlicensed spectrum is congestion cost.


*Similar observation in [Maille, Tuffin, Vigne `10]

Total Welfare: SPs Plus Commons169

Demand curvePrice


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*maximizes revenue

To

talpr

ice

*

P1* P2*

q1* q2*Quantity q(customers served)

consumersurplus

`w(q)

SP 2s revenue

qw*

Total Welfare vs Commons Bandwidth170


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Multiple identical incumbents

Box-shape demand


commons bandwidth

Tota

lWe

lfare

Single Incumbent

Box inverse demand

171

Demand curve`(q)


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Linear latency


qw*q1*

To

talprice

*

`w(q)

Single Incumbent

Theorem: As the capacity C of the commons spectrum increases,there exists constants C1, C2 such that

1. For C


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12. For C1


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The SP increases its revenue by raising itsannounced price:

Extracts additional surplus from its remaining customers

Increases congestion in the commons (zero welfare)

For a single incumbent consumer welfare increaseswith bandwidth.


Heterogenous Customers

Customers may have different trade offs betweenannounced price and delay.

C h ld h l diff i d l l

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Commons should help differentiate delay-tolerantfrom delay-sensitive customers.

Does the previous scenario still occur?


Heterogenous Customers

Customers may have different trade offs betweenannounced price and delay.

C h ld h l diff ti t d l t l t

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Commons should help differentiate delay-tolerantfrom delay-sensitive customers.

Does the previous scenario still occur?

Yes, SP may raise price to offload low-end customers towhite space (zero welfare), extract more surplus from

high-end customers

Here customer surplus can also decrease.


Example: Total Welfare176

Total welfare


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Capacity

Single incumbent Box demands

Observations

Decrease in total welfare is analogous to Braesssparadox in transportation networks.

T id t t i t t t i

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To avoid, must restrict entry to commons, or priceentry.

Possible enhancements of model:

More general demand/latency functions

Investment costs

Deployment geometry


Implications for wireless system design

Concluding Remarks178


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Implications for wireless system design


Spectrum Management: Two Views

Spectrum is abundant

It is just poorly managed

Previous computation:

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Previous computation:1 to 2 Mbps available in urban areas,

but did not account for shrinking cell sizes,

offloading traffic to WiFi, Femto-cells

Unlicensed commons should meet future needs

Latency in commons band will be small enough so that

previous inefficiencies do not arise.


Spectrum Management: Two Views

Spectrum is abundant

It is just poorly managed

Unlicensed commons should meet future needs

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Unlicensed commons should meet future needs Latency in commons band will be small enough so that

previous inefficiencies do not arise.

Spectrum will remain scarce

Applications will be generated to use new spectrum

Shift to cheaper, spectrally inefficient technologies

Unlicensed model is unsuitable for low frequencies


Managing Spectrum Scarcity181

Bit Pipe(< 1 GH ) Commons(> 3 GH )


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Coordinatedinterferencemanagement

Expensive

infrastructure


Bit Pipe(< 1 GHz) Commons(> 3 GHz)

Distributedinterferencemanagement

(random access)

Inexpensive

Frequency


Bit Pipe(< 1 GH ) Dynamic SpectrumM k t Commons(> 3 GH )


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Coordinatedinterferencemanagement

Expensive

infrastructure


Bit Pipe(< 1 GHz)

Distributedinterferencemanagement

(random access)

Inexpensive

Dynamic SpectrumMarkets

Local interferencemanagement

Spectrum servers

Rapid transactions

Frequency

Commons(> 3 GHz)


Bit Pipe

(< 1 GH ) Dynamic SpectrumMarketsCommons

(> 3 GH )


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Macro-cells Wide-area

coverage

Expensiveservice plans


(< 1 GHz)

Local coverage Femto-cells/WiFi

Inexpensive

Dynamic SpectrumMarkets

Micro-cells Limited coverage

Local services

Frequency

(> 3 GHz)

Spectral Efficiency Objective

Wireless systems engineering has put a premium onspectral efficiency (bits/sec/Hz).

Remarkable progress:

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Remarkable progress: Practical coding techniques that achieve close to the

Shannon bound

Creation and exploitation of degrees of freedom:frequency (OFDM), multiple antennas (MIMO),cooperative relays

Opportunistic resource allocation

Advances in signal processing capabilities


Transition to Spectrum Abundance

Tradeoff between spectrum efficiency and powerefficiency

Shift emphasis towards low-power, inexpensive widebandi li h i

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p p , psignaling techniques

Efficiency becomes limited by transaction costs

Distributed interference management(pricing, auctions, local exchange)

Transparent (standardized?) mechanism for spot markets

Wireless devices: frequency agile, compatible with spotmarket mechanism, used by multiple service providers


Many Remaining Challenges186

Policy

Economics


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co o cs

Engineering

Interference managementIncentives, efficiencyMarket design

Many Remaining Challenges187

Policy

Economics


187/187


Engineering

Interference managementIncentives, efficiencyMarket design

Transition to spectrum markets??

spec markets v3

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