Energy-Efficient Cognitive Heterogeneous Networks

Powered by the Smart Grid

Authors: Shengrong Bu, F. Richard Yu and Yi Qian

Presenter: Ran Zhang

Main Reference

Shengrong Bu, F. Richard Yu, and Yi Qian, “Energy-efficient cognitive heterogeneous networks powered by the smart grid,” INFOCOM’13, IEEE Proceedings, 2013.

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OUTLINE Introduction

• Background and Contributions System Model

• Cognitive Heterogeneous Mobile Network Model• Electricity Consumption Model for BSs• Real-Time Pricing in Demand Side Management (DRM)• Complete System Model

Problem Formulation• Three Stage Stackelberg Game• Utility Functions for Different Levels

Analysis of the Proposed Game• Backward Induction Method

Simulations Conclusions

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OUTLINE Introduction

• Background and Contributions System Model

• Cognitive Heterogeneous Mobile Network Model• Electricity Consumption Model for BSs• Real-Time Pricing in Demand Side Management (DRM)• Complete System Model

Problem Formulation• Three Stage Stackelberg Game• Utility Functions for Different Levels

Analysis of the Proposed Game• Backward Induction Method

Simulations Conclusions

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Introduction – Background Energy Efficiency

• Energy cost account for almost half of its annual operating expenses (cellular)• CO2 emissions

Heterogeneous Networks (HetNets)• Smaller cells overlaid with macrocell – effective solution to energy efficiency• Macrocell: large coverage and mobility management• Femtocell: user-deployed, share the same channel with macrocell BS

• Higher data rates, more energy efficient• Increase the handoff rates, inter-cell interference

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Introduction – Background Cognitive Radio Technology

• Originally proposed to improve spectrum efficiency• Mitigate interference and improve energy efficiency

Smart Grid (SG)• Create two-way information exchange flows via communication technologies,

greater flexibility and more important role for customers.• Electricity price can be dramatically fluctuating (negative price)• What kind of information should be sensed in HetNets powered by SG?

• Radio spectrum environment• Smart grid environment

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Introduction – Contributions Real-time pricing for demand-side management

• Multiple retailers, real-time prices;• BSs of both marcro- and femto- cells dynamically decide from which retailer and

the amount of electricity they will buy (energy-efficient power allocation) Price decision model

• Homogeneous Bertrand game with asymmetric costs• Electricity cost formulation

Interference price• Offered by macro BSs to femto BSs to mitigate\control interference from

femtocells Three-level Stackelberg game

• Electricity price decision (retailer level)• Power allocation of Macro BS and interference price decision (MBS level)• Power allocation of Femtocell BS (FBS level)• Backward induction method is proposed to achieve the equilibrium solution

OUTLINE Introduction

• Background and Contributions System Model

• Cognitive Heterogeneous Mobile Network Model• Electricity Consumption Model for BSs• Real-Time Pricing in Demand Side Management (DRM)• Complete System Model

Problem Formulation• Three Stage Stackelberg Game• Utility Functions for Different Levels

Analysis of the Proposed Game• Backward Induction Method

Simulations Conclusions

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System Model Cognitive Heterogeneous Mobile Networks with Femtocells

Powered by Smart Grid

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System Model – Cognitive HetNets Model One MBS and multiple FBSs (wiredly connected) MBS is aware of spectrum access of FBSs and FBSs can monitor the spectrum environment and randomly access the spectrum Slotted manner Multiple subchannels. users use OFDMA to communicate with BSs Assumptions

• Macro- and femto- cells share spectrum• There is one scheduled active femtocell user in each slot in each femtocell• No interference between femtocells, only interference between femtocells and macrocell –

scarcely distributed

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System Model – Electricity Consumption Model

For energy efficient communications• Energy-efficient metric: the weighted transmission rate minus the

weighted electricity cost• Electricity cost: amount of consumed electricity times the real-time price• Amount of consumed electricity (transmission power and other

consumptions)

total tx power

Efficiency of PA

Dynamic Power Consumption

Static Power Consumption

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System Model – Real-Time Pricing

Demand-Side Management (DSM)• A set of programs implemented in utility companies• Help utilities operate more efficiently, reduce CO2 emissions, decrease

the cost of customers• Each retailer competes with each other and aims to maximize its own

utility given the prices offered by other retailers

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System Model – Real-Time Pricing

Complete Model• R retailers, K femtocells, one macrocell user• How the system operates:

Retailer: offer real-time price to MBS and FBS

MBS: Energy-efficient power allocation, lowest price, issue interference price to FBS

FBS: Energy-efficient power allocation, electricity price and interference price

OUTLINE Introduction

• Background and Contributions System Model

• Cognitive Heterogeneous Mobile Network Model• Electricity Consumption Model for BSs• Real-Time Pricing in Demand Side Management (DRM)• Complete System Model

Problem Formulation• Three Stage Stackelberg Game• Utility Functions for Different Levels

Analysis of the Proposed Game• Backward Induction Method

Simulations Conclusions

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Problem Formulation

Three-level Stackelberg Game Goal: maximize the utility of retailers, MBS and FBS. Stage I

• Leader: retailers; follower: MBS and FBSs• Retailers offer real-time price xr to MBS and FBSs

Stage II• Leader: MBS; follower: FBSs• MBS decides which retailer it buys electricity from and the amount of electricity

(i.e., transmission power pm decision), based on real-time price xr.• Offer interference price y based on the received interference from FBSs

Stage III• Each FBS decides which retailer to buy from, the amount, based on xr and y.

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Problem Formulation

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Problem Formulation- Smart Grid Level Goal: maximize the utility function

• cr: electricity cost (e.g., purchase cost, CO2 taxes)• Pmf: additional power consumption• Bmv: 1/η• pm: MBS tx power; pk: FBS k tx power• Srm, Srk: {0,1}

Maximize its individual benefit

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Problem Formulation- MBS Level Goal: maximize the utility function (three parts)

• W: subchannel bandwidth• hm: channel gain from MBS to macrocell user• gkm: channel gain from FBS to macrocell user• y: interference price• α,β: relative weight over transmission rate• Tradeoff: interference revenue and transmission rates

Maximize its individual benefit

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Problem Formulation- FBS Level Goal: maximize the utility function (three parts)

• hk: channel gain from FBS to FBS user• srk: {0,1} indicates whether FBS k buys electricity from retailer r• μk,λk: relative weight over transmission rate

Maximize its individual benefit

OUTLINE Introduction

• Background and Contributions System Model

• Cognitive Heterogeneous Mobile Network Model• Electricity Consumption Model for BSs• Real-Time Pricing in Demand Side Management (DRM)• Complete System Model

Problem Formulation• Three Stage Stackelberg Game• Utility Functions for Different Levels

Analysis of the Proposed Game• Backward Induction Method

Simulations Conclusions

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Analysis of the Proposed Game Goal: to obtain the stackelberg equilibrium of the three-level game Method

• Dependencies among different stages• Propose a backward induction method to capture the sequential dependence of the

decisions• FBSs MBS Retailer

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Analysis – Power Allocation Game for FBSs Already known: interference price y, electricity price xr. Action: choose which retailer r*k, decide the transmission power p*k. Solution:

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Analysis – MBS Level Game Already known: FBS tx power p*k, electricity price xr, Action: choose which retailer r*m, decide the transmission power p*m, give

interference price y Solution:

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Analysis – Electricity Retailers Already known: tx power p*m and p*k, prices xr of other retailers Action: choose best price to maximize individual profits. Solution:

OUTLINE Introduction

• Background and Contributions System Model

• Cognitive Heterogeneous Mobile Network Model• Electricity Consumption Model for BSs• Real-Time Pricing in Demand Side Management (DRM)• Complete System Model

Problem Formulation• Three Stage Stackelberg Game• Utility Functions for Different Levels

Analysis of the Proposed Game• Backward Induction Method

Simulations Conclusions

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Simulations How each FBS makes its power allocation decision based on the

interference price y

Observations• Decrease tx power with higher interference price• Given interference price y, tx power is lower if the lowest electricity price is higher

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Simulations Utility of MBS vs. interference price

Observations• Piece-wise concave• When interference large enough, the utility tends to be stable

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Simulations Tx power of BSs vs. lowest offered price

Observations• Tx power decreases with the increase of price for both kinds of BSs• MBS decrease more significantly as it consumes much more energy than FBSs

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Simulations Stackelberg equlibrium

Observations• Tx power Converge due to the convergence of price offered by the retailers• Equilibrium: retailer 2 set its price equal to its cost, retailer 1 sets its price a little smaller

than retailer 2

OUTLINE Introduction

• Background and Contributions System Model

• Cognitive Heterogeneous Mobile Network Model• Electricity Consumption Model for BSs• Real-Time Pricing in Demand Side Management (DRM)• Complete System Model

Problem Formulation• Three Stage Stackelberg Game• Utility Functions for Different Levels

Analysis of the Proposed Game• Backward Induction Method

Simulations Conclusions

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Conclusions Heterogeneous mobile networks with cognitive radios and femtocells,

powered by smart grid.

Multiple retailers sell electricity and MBS and FBSs adjust their tx powers based on electricity price and interference price

Three-level Stackelberg game is used to model the whole system and homogeneous Betrand Game is used to model the price decision

A backward induction method is used to achieve the Stackelberg equilibrium

Simulations show that the dynamics of smart grid can have significant impact on the decision process of power allocations.