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Spectrum trading in cognitive radio networks: A market-equilibrium-based approach

Advisor Advisor ： Wei-Yeh Chen

StudentStudent ：楊　 于　 世 Reference D. Niyato , E. Hossain, “Spectrum trading in cognitive radio n

etworks: A market-equilibrium-based approach,” IEEE Wireless Commun., vol. 15, no. 6, pp. 71 - 80 , Dec. 2008 .

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Outline

Introduction Spectrum sharing and spectrum trading Spectrum trading : Structure Spectrum trading : Related issues Spectrum trading : Solution approaches System model Equilibrium in spectrum sharing and pricing Expectation and learning Conclusion

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Introduction(1/2)

Frequency spectrum is the scarcest(很稀少 ) radio resource in wireless communication networks.

The concept of cognitive radio was introduced to improve the frequency spectrum utilization in wireless networks

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Introduction(2/2)

We introduce a market-equilibrium-based spectrum trading mechanism that uses spectrum demand and supply of the primary and secondary users, respectively(分別地 ).

Since spectrum supply is stochastic(隨機的 ) in nature, a distributed and adaptive learning algorithm is used for the secondary users to estimate(估計 ) spectrum price and adjust the spectrum demand accordingly so that the market equilibrium can be reached.

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Spectrum sharing and spectrum trading(1/2)

Two major steps in spectrum sharing are spectrum exploration(探測 ) and spectrum exploitation(利用 ).

The objectives of spectrum exploration are to discover and maintain(保持 ) the statistics(統計 ) of spectrum usage, and identify the spectrum opportunities.

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Spectrum sharing and spectrum trading(2/2)

Spectrum trading is the process of exchanging spectrum, which can be performed based on the exchange of different resources or money.

Spectrum trading determines the structure of radio resource selling and buying.

Pricing is a major issue in spectrum trading that determines the value of the spectrum to the spectrum seller and buyer.

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Spectrum trading : Structure

Single Seller (Monopoly) :The simplest structure of spectrum trading arises when there is only a single seller in the system.

Multiple Sellers (Oligopoly) :This market structure consists of multiple sellers offering radio spectrum to the market.

No Permanent Seller (Exchange Market) :In this market structure there is no permanent(永久的 ) spectrum seller, and all users have the right to access the spectrum.

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Spectrum trading : Related issues

Spectrum Pricing

Spectrum Supply and Cost of Spectrum Sharing

Utility Function and Spectrum Demand

Competition and Cooperation in Spectrum Sharing

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Spectrum pricing

Price plays an important role in spectrum trading since it indicates the value of spectrum to both the seller and buyer.

For the buyer, the price paid to the spectrum seller would depend on the satisfaction achieved through the usage of that spectrum.

For the spectrum seller, the price determines its revenue(收入 ).

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Spectrum supply and cost of spectrum sharing

In a cognitive wireless system this spectrum supply can be in terms of the number of frequency channels, the number of time slots , or transmit power given the price charged to the buyer.

There are two types of cost : Fixed cost is incurred due to the investment(投資 ) in infrastructure, variable cost is incurred due to performance degradation(下降 ) resulting fro sharing/selling the spectrum.

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Utility function and spectrum demand

In spectrum trading, spectrum demand determines the amount of spectrum the buyer wants to access for a given price so that its satisfaction is maximized.

The spectrum demand function can be derived(取得 ) based on maximization of utility of secondary users for a given price.

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Competition and cooperation in spectrum sharing(1/2)

A competition occurs when each of the cognitive radio entities has its self-interest and is rational(合理的 ) about maximizing its own benefit.

Competition can be among multiple spectrum sellers to attract(引起 ) more buyers or among spectrum buyers to obtain the best quality/quantity(量 ) of spectrum at the lowest possible price.

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Competition and cooperation in spectrum sharing(2/2)

Entities involved(包含 ) in spectrum trading may have a choice to cooperate so that a better solution can be achieved.

The sellers can cooperate to choose higher prices so that they earn a profit higher than that in case of competition

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Spectrum trading : Solution approaches

Microeconomic Approach

Classical Optimization Approach

Noncooperative Game

Bargaining(交易 ) Game

Auction(拍賣 )

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Microeconomic approach

The solution of this approach is based on market equilibrium, which denotes(表示 ) a price for which spectrum demand equals spectrum supply.

At a market equilibrium, the seller’s profit and buyer’s satisfaction are maximized.

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Classical optimization approach

A classical optimization formulation(規劃 ) consists of an objective to be maximized/minimized and a set of constraints(限制 ).

A classical optimization problem can be formulated by the controller entities for spectrum trading to maximize the profit of the spectrum owner by adjusting(調整 ) the specrum price.

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Noncooperative game

In spectrum trading, multiple primary users offer prices to sell spectrum to secondary users intending to maximize their profits.

Noncooperative game formulations are widely used to analyze and obtain an equilibrium solution that satisfies all of the entities.

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Bargaining game

A bargaining game formulation can be used in situations where players can cooperate, and a player can influence(影響 ) the action of other players in trading the radio spectrum.

In this game the players can negotiate(協商 ) and bargain(交易 ) with each other.

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Auction

Auction is performed by buyers who submit(屈服 ) their bids(出價 ) to a seller.

The seller decides how much of and to whom to sell the spectrum.

This auction is suitable for a situation where the price of the resource is undetermined(未確定 ) and is variable with the buyers’ requirements.

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System model

The primary service controller broadcasts price information to all secondary service controllers.

Then spectrum demands from the secondary service controllers are fed back to the primary service controller to update the price based on spectrum supply function.

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Equilibrium in spectrum sharing and pricing

The objective of the primary service is to maximize profit through selling/sharing the radio spectrum with the secondary service, the aim of the secondary service is to maximize the satisfaction of the connections.

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Expectation and learning(1/2)

We consider a learning algorithm, namely, GFM.

This algorithm uses recursive(遞迴 ) updating to obtain the actual information under uncertainty(不確定的 ), which is the price offered by the primary service.

pe [t + 1] = pe [t] + α[t](p[t]-pe[t])

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Expectation and learning(2/2)

pe [t] is the estimated(估計 ) price by the secondary service at time t, and p[t ]is the current price from the primary service.

In this learning algorithm the estimated price in the previous(先前的 ) iteration(重複 ) is corrected in the direction of error weighted by the learning rate, which is a function of the observed(注意 ) price.

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Spectrum demand and supply functions

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convergence to the equilibrium price and size of allocated spectrum

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Conclusion

這篇論文主要是根據頻譜之間的交易行為與一些模式做一個概念性的介紹，看完之後也提供了我ㄧ些對頻譜分享的一些新的想法，可以透過交易的方式來達到頻譜分享的效果，往後也可以根據此篇論文為理論的基礎，幫助以後的研究。