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Energy-Efficient Cognitive Heterogeneous Networks Powered by the Smart Grid

Energy-Efficient Cognitive Heterogeneous Networks Powered by the Smart Grid. Authors: Shengrong Bu, F. Richard Yu and Yi Qian Presenter: Ran Zhang. Main Reference.

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Energy-Efficient Cognitive Heterogeneous Networks Powered by the Smart Grid

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  1. Energy-Efficient CognitiveHeterogeneous Networks Powered by the Smart Grid Authors: Shengrong Bu, F. Richard Yu and Yi Qian Presenter: Ran Zhang

  2. 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.

  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

  4. 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

  5. Introduction – Background • Energy Efficiency • Energy cost account for almost half of its annual operating expenses (cellular) • CO2emissions • 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

  6. 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

  7. 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

  8. 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

  9. System Model • Cognitive Heterogeneous Mobile Networks with Femtocells Powered by Smart Grid

  10. 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

  11. 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 Dynamic Power Consumption Efficiency of PA Static Power Consumption

  12. 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

  13. 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

  14. 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

  15. 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.

  16. Problem Formulation

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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:

  23. Analysis – MBS Level Game • Already known: FBS txpower p*k, electricity price xr, • Action: choose which retailer r*m, decide the transmission power p*m, give interference price y • Solution:

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

  25. 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

  26. 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

  27. Simulations • Utility of MBS vs. interference price • Observations • Piece-wise concave • When interference large enough, the utility tends to be stable

  28. 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

  29. Simulations • Stackelbergequlibrium • 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

  30. 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

  31. 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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