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
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Background and Contributions
 System Model
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•
•
•
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
3
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
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Energy cost account for almost half of its annual operating expenses (cellular)
CO2 emissions
 Heterogeneous Networks (HetNets)
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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
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Higher data rates, more energy efficient
Increase the handoff rates, inter-cell interference
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Introduction – Background
 Cognitive Radio Technology
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Originally proposed to improve spectrum efficiency
Mitigate interference and improve energy efficiency
 Smart Grid (SG)
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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?
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Radio spectrum environment
Smart grid environment
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Introduction – Contributions
 Real-time pricing for demand-side management
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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
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Homogeneous Bertrand game with asymmetric costs
Electricity cost formulation
 Interference price
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Offered by macro BSs to femto BSs to mitigate\control interference from
femtocells
 Three-level Stackelberg game
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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
7
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
8
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
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•
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
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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
14
Problem Formulation
Three-level Stackelberg Game
 Goal: maximize the utility of retailers, MBS and FBS.
 Stage I
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Leader: retailers; follower: MBS and FBSs
Retailers offer real-time price xr to MBS and FBSs
 Stage II
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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
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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)
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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)
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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
19
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
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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:
24
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
25
Simulations
 How each FBS makes its power allocation decision based on the
interference price y
 Observations
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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
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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
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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
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•
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
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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
30
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.
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