Cognitive Virtual Network Operator Games by Lingjie Duan, Jianwei Huang, Biying Shou

Cognitive Virtual Network Operator Games by Lingjie Duan, Jianwei Huang, Biying Shou

By Lingjie Duan, Jianwei Huang, Biying Shou

This SpringerBrief offers an summary of cognitive cellular digital community operator’s (C-MVNO) judgements below funding flexibility, offer uncertainty, and industry festival in cognitive radio networks. it is a new examine region on the nexus of cognitive radio engineering and microeconomics. The authors specialize in an operator’s joint spectrum funding and repair pricing judgements. The readers will the way to tradeoff the 2 versatile funding offerings (dynamic spectrum leasing and spectrum sensing) lower than provide uncertainty. in addition, if there's multiple operator, we current research of the contest between operators in acquiring spectrum and pricing companies to draw clients. The short is designed for execs operating with C-MVNOs. Succinct and functional, it is going to support them in making optimum investments and pricing judgements. it is going to even be of curiosity to researchers.

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Next we derive the operator’s optimal pricing decision in Stage III. 15 shows two possible intersection cases of S(π ) and D(π ). 468. , S1 (π ) in Fig. 468. The total supply is no smaller (and often exceeds) the total demand. , S2 (π ) in Fig. 15), the optimal price occurs at the unique intersection point of S(π ) and D(π ) (where D(π ) has a negative first-order derivative). The total supply equals total demand. • Now we are ready to show the threshold structure of the leasing decision. – If the sensing result from Stage I satisfies Bs α ≥ Bth1 , then the operator will not lease.

The payoff linearly increases in α when α becomes larger than a threshold, in which case the equilibrium price becomes lower than 1 + Cl . A smaller sensing cost Cs leads to more aggressive sensing and thus more benefits to the users. 08 CS1/2 II Normalized Realized Profit R Fig. /o. /o. sensing l * i Fig. 6 Learning the Distribution of Sensing Realization Factor α Our previous analysis assumes that the operator knows the distribution of sensing realization factor α beforehand. When such information is not available, the operator can learn the distribution through machine learning [70].

2 Such independency is good for the development of spectrum market, since a user does not need to worry about how variations of user population and wireless characteristics change its performance in spectrum trading. 1 Background 39 • Fair service quality achieved by users: We show that each user achieves the same signal-to-noise (SNR) that is independent of the users’ population and wireless characteristics. • Impact of competition: We show that the operators’ competition leads to a maximum 25 % loss of their total profit compared with a coordinated case.

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