Equilibrium Analysis for Spectrum Access in Cognitive Radio Networks with Traffic Prioritization

Joint pricing and service selection for service‐differentiated duopoly in cognitive radio networks

“…By taking the derivative of π 2 in (25) with respect to p 2 and letting ∂π 2 /∂p 2 = 0, we can obtain the optimal price p 2 * as a function of p 1 , which is seen in (20).…”

“…In stage I, given the optimal price p 2 * , PO 1 makes the decision to maximise its revenue by (see (26)) where ε = αE(Y e 2 ). Note that with (20) we can acquire the p 1 is the function as…”

“…where A and B are defined in (10), p 2 is the function of (p 1 − p 2 ) in (20). Finally, similar to stage II, PO 1 can determine its optimal price p 1 * by solving ∂π 1 /∂p 1 = 0.…”

“…The pricing in a queueing system has been studied recently in cognitive radio networks [9][10][11][12][13][14][15][16][17][18][19][20][21] and other research areas [22][23][24][25]. Do et al [9] studied pricing strategy in two scenarios of share-use and exclusive-use DSA, and analysed the queueing delay in a M/G/1 queueing system with breakdown.…”

“…Do et al [23] formulated a price competition in a heterogeneous market cloud computing by using a M/M/ ∞ and a M/M/1 system. The queueing model in the existing studies usually consists of at most two queues, either one queue in a monopoly competition [10,18] or two queues in a monopoly competition offering two differentiated services [20,22], or two queues in a duopoly scenario with a single service [9,11]. Unlike the queueing models developed in the literature, we consider a duopoly case with two differentiated services on the basis of a M/G/1 system with four queues.…”