VT Symposium Pres toPubl · Carlos E.Caicedo Bastidas 2012 Wireless Symposium@Virginia Tech 7...

Dynamic Spectrum Assignment via Spectrum Markets(Techno‐economic analysis and modeling)

Virginia Tech Wireless Symposium ‐May/2012

Carlos E. Caicedo ‐ Syracuse University 1

Dynamic Spectrum Assignment via Spectrum Markets

( Techno‐economic analysis and modeling )

Tutorial for the Wireless Symposium@Virginia Tech

Carlos E. Caicedo BastidasAssistant Professor

Director ‐ Center for Convergence and Emerging Network Technologies (CCENT)School of Information Studies

Syracuse University

May / 2012

2

Outline

• Introduction– Spectrum Management

• DSA via Spectrum markets: Spectrum Trading (ST)– Technical architectures

– Market participants

• Agent Based Modeling– Concepts

– Tools

• Modeling of Spectrum Markets– Focus on ST

• Conclusions

Carlos E. Caicedo Bastidas 2012 Wireless Symposium@Virginia Tech




Spectrum Management

• Goal: Maximize the gains to society from the use of the radio spectrum [1]

– Allow as many efficient users as possible

– Manage/minimize the interference between users

3Carlos E. Caicedo Bastidas 2012 Wireless Symposium@Virginia Tech

Spectrum Management

Spectrum Assignment

Command and Control

Market Model

Commons Model

Spectrum Allocation

Auctions‐basedMarket

Secondary Spectrum Market

Direct Trading

Policy/RegulationConcerns

Pricing Methods

Technical Architecture

Market Mechanisms

…..…..





Spectrum Management

Spectrum Assignment

FlexibleSpectrum Access

FixedSpectrum Access

Exclusive Use

Shared Use

Spectrum Allocation

Access

Use

Spectrum Management (2)


Motivation

Carlos E. Caicedo Bastidas 6

Source: BBN Technologies, “The XG Vision – RFC v.2.0”, 2006

Traditional spectrum assignment mechanisms have created an artificial “spectrum scarcity”

2012 Wireless Symposium@Virginia Tech




Dynamic Spectrum Assignment (DSA)

• Increase access and use of spectrum by:– Service providers

• Relieve peak traffic congestion• Test/roll out new services

– Small businesses– Innovative users/companies

• Lead to higher competition, faster technology innovation cycles

• Lower the costs of access to spectrum• Changes to business models

– Customer centric, not provider centric


Market‐based DSA

• Market coordinating entity

– Exchange

– Regulator

• Market facilitators

– Brokers

– Market makers

– Exchanges

8

t

f

s





9

Market‐based DSA: Spectrum Trading

Spectrum Trading:

Market based mechanism where, ideally, buyers and sellers determine the assignments of spectrum and its uses.


10

Motivation

• Traditional spectrum assignment mechanisms have created an artificial “spectrum scarcity”

• Potential benefits of ST:

– Spectrum efficiency

– Assignment to the most valuable uses

• Spectrum trading (ST) markets can provide fast and economic access to spectrum resources. – Support peak demand periods

– Allow for the provision of new services to customers.

– Obtain economic gains from spectrum that is unused





Enabling factors

• Technical– Software radios

• Legal infrastructure– USA / FCC (2000 ‐ ..)

– UK / Ofcom (2004 ‐ ..)

– Guatemala, New Zeland, EU, South Korea and others

• Economic (research still needed here)– Sufficient spectrum relative to demand

– Adequate liquidity


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Software Radio Systems

• Many radio functions performed in software

• Reconfigurability

• Support for many air interfaces





Cognitive Radio

• Adapts its tx/rx parameters based on user, network and environment parameters.

– It knows where it is

– It can determine what services are available/accessible

– It knows what services interest the user, and knows how to find them

– Learns and recognizes service usage patterns from the user


Secondary spectrum markets: Regulatory initiatives

• Since the year 2000, the FCC has been issuing policy statements to promote the development of secondary markets– In 2003, 2004 and 2007 the FCC issued regulation on spectrum leasing

– 2005: Proceeding to explore the uses of cognitive radio technology to facilitate improved spectrum access.

– 2009: NOI – Fostering Innovation and Investment in the Wireless Communication Market

– 2010: NOI – Promoting more efficient of spectrum through dynamic spectrum use technologies






• New Zeland: First country to do economically‐based spectrum allocations

• Guatemala: Titulo de Usufructo de Frecuencia(TUF)

• Australia: Spectrum trading framework has been updated several times since 1997

• Japan, South Korea: Market‐friendly regulatory policies are being implemented/considered



• Ofcom in the UK established trading for certain sets of spectrum frequencies in December 2004

– 2009: Consultation on simplifying spectrum trading

– 2011: (Final statement) Simplifying spectrum trading: Spectrum leasing and other market enhancements





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Trading Interactions: A Classification [2]

• Mode: Range of actions that a buyer has at his disposal with the acquired spectrum– Change of ownership– Change of use

• Extent: Degree to which a spectrum licensees’ rights and obligations are transferred to the buyer– Complete– Shared

• Duration: Length of time of the trade– Short or long term lease– Sale and buy back– Permanent


Participants in a ST market

Entity CommentsSpectrum license holder (SLH)

Entity that owns a spectrum license and can potentially put it for sale in a ST market

Spectrum licenserequestor (SLR)

Entity that needs spectrum and can potentially buy licenses in a ST market

Spectrum Exchange Centralized entity that gathers and matches bids and asks for spectrum.

Spectrum Broker Present in over‐the‐counter spectrum markets It matches bids and asks of spectrum and receives a fee for each trade matched. A spectrum broker does not hold any spectrum.

Market Maker Entity that provides liquidity to the marketSpeculator Entity that tries to obtain short term gains

from price variations. Provides liquidity.Spectrum Regulator Manages a spectrum availability and

assignment database.

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Wireless Service Provider

(SPECTRUM USER)





Are ST markets viable ?

• Under what conditions might a spectrum trading market emerge?– How much spectrum is necessary ? (for enough liquidity)

– What technical architectures allow a sustainable ST market?

– Is this “viability region” likely in practice?

• What do policymakers need to know?


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Architectures for ST: A Classification [3]

• Infrastructure

– Shared (Pooling points)

– Not‐shared





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Architectures for ST: A Classification

• Configuration method

– Centralized

– Distributed


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•Activation– Provider Initiated

– Provider + user initiated

•Flexibility– Multi‐protocol

– Single protocol Frequency







Implementation Issues

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• Which architecture to use ? (Which combination of parameters to use?)

– Infrastructure Shared

+ Infrastructure costs for each provider are reduced

‐ Optimal placement/coverage may not be achieved

Not shared

+ Better coverage

‐ Higher cost






25


– Configuration MethodCentralized

• Range of configurations is limited by the central exchange

Distributed

• More freedom in configuration setup



26


– Activation• Provider initiated

• Provider + user initiated

All require the addition of configuration channels in the ST infrastructure






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– Flexibility

•Multi‐protocol

•Single protocol

As fewer protocols are supported, interference prediction improves but trading is less attractive


Architecture example: #1 Shared, Centralized, Provider Initiated


Frequency

Wireless Service

Provider 1

Wireless Service

Provider 2

Spectrum Exchange

Frequency

Frequency

2012 Wireless Symposium@Virginia Tech




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Architecture example: #2 Not Shared, Distributed, Provider Initiated

Frequency

Wireless Service

Provider 1

Wireless Service

Provider 2

Frequency

Frequency

Spectrum Broker


Typology of spectrum trading markets[4]

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*

POOL

NOPOOL

BM

NOBM





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Exchange type CharacteristicsPOOL_BM Pooling point + band manager functionality

Use of traded spectrum is enabled and configured throughequipment/infrastructure owned by the exchange.

All tradable spectrum is held by the exchange All tradable spectrum returns to and is given by the exchange

POOL_NOBM Pooling point only, no band manager functionality Use of traded spectrum is enabled and configured through

equipment/infrastructure owned by the exchange. Different segments of spectrum can be activated and configured through the

equipment/infrastructure of the exchange No spectrum inventory is held by the exchange

NOPOOL_BM Non‐pooling point + band manager functionality All tradable spectrum is held by the exchange All tradable spectrum returns to and is given by the exchange Exchange grants authorizations for use of spectrum (no equipment

configuration is done by the exchange)NOPOOL_NOBM Non‐pooling point, no band manager functionality

Exchange grants authorizations for use of spectrum (no equipment configuration is done by the exchange)

No spectrum inventory is held by the exchange

ST Exchange Types


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Architecture Supported Market TypeInfrastructure

typeConfiguration

method OTC Exchange

Shared Centralized Yes POOL_BM, POOL_NOBM

Shared Distributed Yes POOL_BM, POOL_NOBM

Not shared Centralized Yes NOPOOL_BM, NOPOOL_NOBM

Not shared Distributed Yes NOPOOL_BM, NOPOOL_NOBM

Technical Architecture vs. Market Structure





Summary (so far): ST market architectures

• Technical classification

– 4 dimensions (Infrastructure, configuration method, activation, flexibility)

• Market structure

– OTC

– Exchange based (BM, NOBM)


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Technical Architecture & parameters

Regulatory framework

Market structure & behaviors





Agent Based Modeling and Simulation

Introduction

• Agent Based Modeling and Simulation is an approach to modeling complex adaptive systems comprised of autonomous and interacting agents [5]– Models are built form the ground‐up

• Focus on capturing diversity of agents– Behaviors– Attributes

– Emphasis on determining/studying “emergent” phenomena• New behaviors and/or patterns that arise from the agent interactions

• ABM has been used in the following areas Stock market models Study of supply chains Predicting the spread of epidemics ….





What is an Agent?

• Agents represent the decision making entities (people/organizations) of a complex adaptive system– Behaviors (sets of rules)

• Behaviors/rules define how it will update/change its state, interact with the environment/other agents and achieve its goals/functions

– Autonomous and self‐contained• Clearly identifiable• Uses information from its environment and its interactions with other agents

• Characteristics– Adaptive– Can learn and modify their behavior– Autonomous– Heterogeneous


Agent‐Based Modeling (Basics)

• Defining the agentsIdentifying the agent types (classes)Identifying agent behaviorsIdentify agent interactionsWhen, how and with whom

– Connections/network (topology)

– Neighborhood (if applicable)

• Agents act on local information !!– Bounded rationality (more on this later)

– Interactions are with a subset of all agents at a given time





ABMS Toolkits/Platforms• Dedicated Agent‐based Prototyping Environments: – Repast Simphony, NetLogo, StarLogo

• General Computational Mathematics Systems: – MATLAB, Mathematica

• Typical programming languages used– Java– Python– C++

• Other toolkits:• Swarm • MASON • AnyLogic


Repast Symphony Suite• A set of advanced, free, open source agent based

modeling and simulation platforms. – Repast Simphony 2.0 released on March 5/2012

• Models can be built in:– Java

– ReLogo

– Groovy

– Using flowcharts

– NetLogo models can be imported

• Repast High Performance Computing 2.0 is also available.– C++ based modeling system.

• Tools can be downloaded at http://repast.sourceforge.net/download.html





Repast IDE (WorkBench)(Based on the Eclipse IDE)

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3

Projects WorkSpace: Placeholder where all the projects reside.

Panel for editing source files.

Console Panel: displays console messages, runtime, compile time errors.Carlos E. Caicedo Bastidas 412012 Wireless Symposium@Virginia Tech

Simple (tutorial) examples/models

• Predator‐prey

• Zombies

• SugarScape

Carlos E. Caicedo Bastidas 2012 Wireless Symposium@Virginia Tech 42

Zombies model execution example




Repastcity


Another example: Virtual city and road network

Agent‐based Computational Economics (ACE)

• ACE = “the computational study of economic processes modeled as dynamic systems of interacting agents”[7]– Application of ABMS to economic systems

– Agents represent entities participating in a market

– Market participants have bounded rationality

– Adequate for studying “emerging” behavior





• Traditional microeconomic theory vs. ACE

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Agent‐based Computational Economics (ACE)

Microeconomic theory ACE

Economic agents are rational‐ Clear goals‐ Optimizing behavior

Agents have bounded rationality‐ Heuristic based decisions (satisfycing)

Economic agents are homogenous (same behavior rules and characteristics)

Economic agents are diverse

The long‐run equilibrium state of the system is the only result of interest

Knowing what happens to get to get to equilibrium (optimal or not) is of interest


Modeling aSpectrum Trading Market




47

– Model the activities of market participants

– Incorporate techno‐economic restrictions• Interference, propagation effects, system architecture

• Traffic to spectrum requirement mapping

• Spectrum costs (per basic bandwidth unit – BBU)

– General operation• Populate model with agents

• Allow agents to interact and make transactions– Spectrum users are not all the same

• Spectrum users learn from transactions, which affects subsequent transactions– Use reinforcement learning

Modeling a ST market


Modeling (The steps.. overview)

• Characterize the agents– Behaviors/rules

– Population size

• Determine interactions between agents

• Generate model & simulate– Validate model (pre)

– Run model• Run over many scenarios/parameter variations

– Validate results

• Perform sensitivity analysis





SPECTRAD Agents

49

SPECTRAD

Model parameters:‐ Distribution of Spectrum Users‐ Amount of tradable spectrum‐ Liquidity providers‐Market structure‐ Opportunistic behavior

Agent Comments

Spectrum User

This agent models a wireless service provider that participates in the ST market as a seller of spectrum (SLH) or buyer (SLR)

Market Maker

Entity that provides liquidity to the market. It will be present only in scenarios in which the exchange does not act as a band manager (NOBM scenarios)

Spectrum Exchange

Centralized entity that gathers and matches bids and asks for spectrum. It will act as a band manager in BM scenarios and not in this capacity in NOBM scenarios

Spectrum Regulator

Manages a spectrum availability and assignment database.


Modeling tool ‐ Architecture





(Objective.. Again) Are ST markets viable ?

• Under what conditions might a spectrum trading market emerge?– How much spectrum is necessary for liquid markets?

– What technical architectures allow a sustainable ST market?

– Is this “viability region” likely in practice?

• What do policymakers need to know?


Model assumptions

• One wireless standard, one service area

• Exogenous, time variant demand

• Agents learn and have bounded rationality

• When demand increases, agents decide whether – To purchase perfectly fungible spectrum units (BBUs)

– To purchase Alternate Technology (AT) units

– Opportunity cost of not serving traffic is infinite





Parameters affecting market behavior

53

• We are interested in the “running” behavior of the market

– Assuming that infrastructure has been setup

Analyzed Techno‐Economic set of architectures based on:

•Trading costs•Infrastructure costs•Information overhead•Wireless technologies

For studying the running behavior, the exchange structure (behavior) is relevant

•Exchange acting as a band manager (BM)•Exchange that is not a band manager (NOBM)


• Traffic demand to be served C

C ~ exponential distribution, mean λ1

• Interarrival time for new traffic demands

T ~ exponential distribution, mean λ2

54

Time

Characteristics of the Model (1)

C





Characteristics of the Model (2)

• Tradeoff between investing in wireless or an alternate technology– Use of Basic Bandwidth Units (BBU)

– Use of Alternate Technology transmission Units (AT)

• Cell sectoring

• Traffic off‐loading

• (Economic) Agent behavior/type

– ZIP (Zero‐Intelligence plus)


(Economic) Agent behaviors/Types

• ZI : Each agent generates random asks or bids depending on whether he is a seller or a buyer.

– Asks or bids are distributed uniformly and independently over the entire range of trading prices.

– Agent does no seek to maximize profit, does not observe the market, remember or learn.

• ZI‐C : ZI agent with budget constraint which forbids the agent to buy or sell at a loss.

– ZI‐C seller submits an ask as a random value that is less than the highest acceptable price of the market and more than the reservation price of this unit.

– ZI‐C buyer submits a bid as a random value that is more than the lowest acceptable price of the market and less than the reservation price of this unit of goods.

• ZIP (Zero‐intelligence‐plus): each agent has a profit margin that determines the difference between it’s reservation price and the ask or bid to be submitted.





NOBM exchange based market

• At initialization the total amount of spectrum (BBUs) is distributed evenly among SUs

• Initialization phase looks for an initial trading price among SUs

• Continuous market phase: Exchange matches bids and asks

– Continuous double auction

• Exchange updates the market quote information

– SUs use the quote to update their prices


BM Exchange based market

• Exchange leases BBUs in its managed band for tleasetime periods

• After tlease a SU must submit a new bid

• Bids are organized based on price

• BBUs are assigned to the SUs whose bid price is above the cutoff price

– SUs with winning bids pay the cutoff price

– If demand is not greater than supply, then PminCutoff is paid





SU behavior

• Receives a traffic demand for Ttraffic (Mbps) from the SU’s service area customers

• Calculates the maximum traffic it can serve with its current inventory and determines whether to buy or sell* BBUs

• (Assumption) Opportunity cost of not serving traffic is high. If BBUs can’t be acquired, AT units are acquired instead– ATs are not immediately active

– ATs have a finite lifetime

– NOBM : Buy expensive spectrum, sell back after ATs are active

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* Only for NOBM case


Market Maker Behavior

• Present in NOBM scenarios. Intervenes in the market to keep the market going

• Intervenes when:

– No bids

– No asks

• Reactive behavior for market intervention

• Has an initial spectrum inventory

– Tries to maintain its reference inventory level





Scenario parameters

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Parameter Symbol Value

General parameters

Size (bandwidth) of a BBU BWBBU 200 KHz

Traffic capacity of a BBU CBBU 384 Kbps

Traffic capacity of an ATtransmission unit

CAT 384 Kbps

AT lifetime ATLife Uniformly distributed between (90,110) time ticks.

Total simulated marketlifetime

Tmax 5000 time ticks (3000 time ticks forwarmup period, 2000 time ticks foractive data collection of marketbehavior)

SU Parameters

Mean traffic demand µtraffic 4.0 Mbps

Number of time ticks betweentraffic demand changes

µtchange Uniformly distributed between (10, 25) time ticks


Experimental Design

62Carlos E. Caicedo Bastidas

(UserDist)




NOBM scenario example

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0 500 1000 1500 20000

50

100

150

BBUs=100 NumUsers=10

Time (ticks)

MM

Pric

ePer

BB

U

0 500 1000 1500 20000

5

10

15

20

25BBUs=100 NumUsers=10

Time (ticks)

Bid

/Ask

spr

ead


NOBM – Selected parameters for viability [6]

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Factor Symbol

Relative bid/ask spread relBA

Mid-point BBU price MidPriceBBU

Relative difference of the MM’s inventoryto its reference level

mmInvDiff

Percentage of spectrum being offered forsale

bbuOffered%

Percentage of completed market runs numMkt %





Viability parameters (NOBM)


Results ‐ (NOBM)

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R=NumberOfSU

BBUsrumTotalSpect )(

Average number of BBUs per SU

“Balanced” at R=10(average traffic load occurs at R=10, traffic load =4 Mbps)

More spectrum than needed

Less spectrum than needed





Results ‐ (NOBM)


Results ‐ (NOBM)





Viability criteria (NOBM)

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Criteria Factor Symbol Pass FailScore

Pass/Fail

C1Percentage of completedmarket runs

numMkt % >=70% <=50% 2/-2

C2 Relative bid/ask spread relBA <=20% >=50% 1/-1

C3 Mid-point BBU price MidPriceBBU >=100 <=25 1/-1

C4Relative difference of theMM’s inventory to itsreference level

mmInvDiff <=25% >=100% 1/-1

C5Percentage of spectrumbeing offered for sale

bbuOffered% N/A >=38% 0/-1


Scores (NOBM)





Viability implications

71

• With little or no oversupply of spectrum and with a number of SU>=6, most NOBM scenarios are viable for 5 R 15– Wireless service areas with 6 or more WSP would support trading if there is no excessive oversupply or undersupply of spectrum

• A simple market maker can keep the market “working”.– Regulators don’t need to specify complex market maker rules


Viability implications

72

• Behavior trends are independent of user distribution

• (Excessive) Oversupply of spectrum affected all markets negatively (R 20)

• With little or no oversupply of spectrum and with a number of SU>=6, most NOBM scenarios are viable for 5 R 15

• In NOBM: A simple market maker can keep the market “working”.

• Spectrum efficiency

– For viable NOBM scenarios: 51% ‐ 77%





Sensitivity Analysis

• Three parameters:

– AT lifetime• Low (40, 60) High (140, 160)

• No effect on selection of viable scenario

– Market Maker spread• Low (5) High (15)

• Moderate effect on scenario behavior. However, not substantial enough to change viable scenario selection

– Average traffic per SU• Low (2 Mbps) High (6 Mbps)

• Results shift to “new” reference R value


NOBM


(Reference case:Avg Traffic per SU = 4Mbps)




Conclusions

• The number of market participants required for a ST market can be found in the current and future wireless environment

• Aim for no spectrum oversupply

• Market maker – needs simple rules

• The dynamics of ST markets could be an incentive for “non‐traditional” wireless service providers to enter the market


Conclusions (2)

• Market based DSA can enhance future wireless service delivery

– Assignment of spectrum to those who value it more

– Create incentives for innovation in devices and services





Future work

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• What is the effect of additional technical and environmental restrictions?

• Effect of regulatory frameworks on Market based DSA– Constraints on “liberalized” spectrum

– Limits on speculative behavior

• How does having to support more than one wireless technology (GSM, LTE, etc) affect the liquidity and sustainability of ST markets?

• Design of protocols for enabling ST over specific wireless technologies

• How can market based DSA enhance next generation wireless service provision and mobility scenarios?


References

[1] M. Cave, C. Doyle, and W. Webb, Essentials of modern spectrum management. Cambridge ; New York: Cambridge University Press, 2007.

[2] Radio communications Agency, "Implementing Spectrum Trading," July, 2002.

[3] C. Caicedo, M. Weiss, “Spectrum Trading: An Analysis of Implementation Issues”,Proceedings of the IEEE Symposium on Dynamic Spectrum Access Networks (DYSPAN 2007), Dublin, Ireland, 2007

[4] C. Caicedo, M. Weiss, “An Analysis of Market Structures and Implementation Architectures for Spectrum Trading Markets”, Telecommunications and Policy Research Conference‐TPRC, Arlington, VA, 2008.

[5] C. M. Macal and M. J. North, "Tutorial on agent‐based modeling and simulation," Journal of Simulation, vol. 4, pp. 151‐162, 2010.

[6] C. Caicedo, M. Weiss, “The Viability of Spectrum Trading Markets”, IEEE Communications Magazine – March 2011

[7] L. Tesfatsion, "Agent‐Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, vol. 2, pp. 831‐880, 2006.





Contact info:

– Carlos Caicedo [email protected]

Thank you !


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