Reward and Risk for Opportunistic Spectrum Accessing in Cognitive Radio Networks

Abstract

Abstract-Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary service providers (PSPs) set price for the vacant licensed bands of primary users (PUs) and sell them for monetary gains, and the secondary service provider (SSP) can buy the bands and opportunistically use them to satisfy the service demands of secondary users (SUs) when the primary services are not active. However, when there are multiple bands available, the SSP confronts the challenges of how to choose bands and how to split the overall traffic on them considering both his monetary reward and the potential risk, from the unpredictable activities of the primary services, for opportunistic spectrum accessing (OSA). In this paper, we propose a reward and risk based band-mix selection algorithm to address these concerns of the SSP, and help the SSP to make appropriate decisions of traffic splitting over available spectrum band-mix, consisting of both the band belonging to SSP itself and the bands from PSPs. By numerical simulations, we verify our theoretical analysis and show that the proposed spectrum band-mix selection effectively improves the spectrum utilization as well as the satisfactory degree of SUs.

Spectrum Harvesting and Sharing in Multi-Hop CRNs Under Uncertain Spectrum Supply

“…It indicates that since the ratio of the upper bound to the lower bound is close to 1 at any (α, β) level, and the optimal bandwidth required at α is between those bounds, the solution found by the coarse-grained fixing algorithm must be close to the optimum. Figure 5 shows the comparison between the proposed (α, β) based approach and the expected bandwidth based approach in which the expected value of bandwidth is used to characterize both the objective of the optimization and corresponding constraints [24]. For illustrative purposes, we compare the solution of the expected bandwidth based approach with the solutions obtained by the coarse-grained fixing algorithm at (α, β) = (80%, 80%), (α, β) = (90%, 90%) and β = 80% with the expected value of required bandwidth as the objective, respectively.…”

“…It is common in the literature [24] that one may attempt to use E(W m ), the first order statistics of W m [5] to predict the white space as shown in Fig. 2.…”

Joint routing and link scheduling for cognitive radio networks under uncertain spectrum supply

“…As shown in Fig. 2, generally speaking, people [22], [23] would like to use E(w m ), the first order statistics of w m [4] to predict the white space. Although this measurement 5 Taking the least-utilized spectrum bands introduced in [12] …”

“…(21) Thus, hwc(wC) is the convolution of hwa(wa) and hWb (wb) [26]. It can be written as hwc(wC) = hwa(wa)*hwb(Wb) = ® hwrn(wm), (22) m E{a,b} where ® denotes the operator for the convolution of a sequence. From the calculation of hwc(wC), we find that the sum of two independent random variables is associative and commutative.…”

Using homomorphic encryption to secure the combinatorial spectrum auction without the trustworthy auctioneer

“…The available S spectrum bands are supposed to have different characteristics to different nodes (in the subsequent development, we use the nodes and bidders interchangeably) in terms of the frequency, the segment type of the band (i.e., contiguous segment or discontinuous one), and the location of the bidders, etc. [32][33][34], so that bidders may submit different bids for different combinations of the spectrum bands. Considering the frequency reuse [21,20], i.e., adjacent nodes must not use the same bands simultaneously while geographically well-separated ones can, we represent the interference relationship among bidders by a conflict graph, which can be constructed from either physical model [35] or protocol model [36] as described in Zhou et al [8], Zhou and Zheng [9], Wu et al [11], Gandhi et al [20].…”