Exploiting Context-Awareness for Secure Spectrum Trading in Multi-Hop Cognitive Cellular Networks

Lorenzo, Beatriz; Kovacevic, Ivana; González-Castaño, Francisco J.; Burguillo, Juan C.

doi:10.1109/glocomw.2015.7413995

Cited by 7 publications

(14 citation statements)

References 18 publications

(41 reference statements)

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“…is the signal to interference noise ratio at any relaying user, ij G  is the channel gain between user i and j on channel ψ, nψ is the number of concurrent transmissions using that channel, uj G  is the channel gain between interfering users u and j on channel ψ and χr is the background noise power. The detailed calculus of the interference given the geometry of the cell is provided in [6]. Then, the capacity on the multi-hop route R is cR = minl∈R cl.…”

Section: Tessellation Factor and Schedulingmentioning

confidence: 99%

“…where pb is the probability that the SU has b channels available given in [6], p is the probability that in the adjacent subcell there is a user willing to relay, and pfree is the probability that there will not be a PU return to the channel, pfree = 1preturn, and preturn is obtained as in [6]. The probability that the SU will not be able to transmit to any adjacent user is p0…”

Section: A Description Of the Protocolmentioning

confidence: 99%

“…This optimization provides the optimum number of channels bm and the optimum availability probability p such that the SU obtains the maximum utility for a given value of βm and θm. The valuation function defined by 2 The SUs calculate an estimation of their link capacity [6] considering that in the worst case there will be N/K secondary users transmitting in the same slot on any of the b available channels, where N is the number of subcells and K the resource reuse factor. Due to the symmetry of the scenario, the same capacity is assumed per hop.…”

Section: B Joint Bidding and Tipping Schemementioning

confidence: 99%

“…Therefore, SUs may need more than one channel as backup to mitigate these effects and keep delay at reasonable levels. The importance of backup channels to increase link robustness in cognitive networks is addressed in [6]. The impact of the number of channels on sale is studied in [7] by considering a three-layer spectrum market consisting of the spectrum holders, service provider and end users.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Joint Resource Bidding and Tipping Strategies in Multi-Hop Cognitive Networks

Lorenzo

Kovacevic

Peleteiro

et al. 2016

IEEE Trans. Cogn. Commun. Netw.

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In multi-hop secondary networks, bidding strategies for spectrum auction, route selection and relaying incentives should be jointly considered to establish multi-hop communication. In this paper, a framework for joint resource bidding and tipping is developed where users iteratively revise their strategies, which include bidding and incentivizing relays, to achieve their Quality of Service (QoS) requirements. A bidding language is designed to generalize secondary users' heterogeneous demands for multiple resources and willingness to pay. Then, group partitioning-based auction mechanisms are presented to exploit the heterogeneity of SU demands in multi-hop secondary networks. These mechanisms include primary operator (PO) strategies based on static and dynamic partition schemes combined with new payment mechanisms to obtain high revenue and fairly allocate the resources. The proposed auction schemes stimulate the participation of SUs and provide high revenue for the PO while maximizing the social welfare. Besides, they satisfy the properties of truthfulness, individual rationality and computational tractability. Simulation results have shown that for highly demanding users the static group scheme achieves 150% more winners and 3 times higher revenue for the PO compared to a scheme without grouping. For lowly demanding users, the PO may keep similar revenue with the dynamic scheme by lowering 50% the price per channel as the number of winners will increase proportionally. 1

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Section: Tessellation Factor and Schedulingmentioning

confidence: 99%

Section: A Description Of the Protocolmentioning

confidence: 99%

Section: B Joint Bidding and Tipping Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Resource Bidding and Tipping Strategies in Multi-Hop Cognitive Networks

Lorenzo

Kovacevic

Peleteiro

et al. 2016

IEEE Trans. Cogn. Commun. Netw.

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“…The importance of backup channels to increase link reliability and robustness in multi-hop cognitive networks is addressed in [11]. They show that high reliability requirements can be met when there is traffic misbalance between primary and secondary networks.…”

mentioning

confidence: 99%

A Novel Collaborative Cognitive Dynamic Network Architecture

Lorenzo

González-Castaño

Fang

2017

IEEE Wireless Commun.

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Abstract-Increasing mobile data demands in current cellular networks and proliferation of advanced handheld devices have given rise to a new generation of dynamic network architectures (DNAs). In a DNA, users share their connectivities and act as access points providing Internet connections for others without additional network infrastructure cost. A large number of users and their dynamic connections make DNA highly adaptive to variations in the network and suitable for low cost ubiquitous Internet connectivity. In this article, we propose a novel collaborative cognitive dynamic network architecture (CDNA) which incorporates cognitive capabilities to exploit underutilized spectrum in a more flexible and intelligent way. The design principles of CDNA are perfectly aligned to the functionality requirements of future 5G wireless networks such as energy and spectrum efficiency, scalability, dynamic reconfigurability, support for multi-hop communications, infrastructure sharing, and multi-operator cooperation. A case study with a new resource allocation problem enabled by CDNA is conducted using matching theory with pricing to illustrate the potential benefits of CDNA for users and operators, tackle user associations for data and spectrum trading with low complexity, and enable self-organizing capabilities. Finally, possible challenges and future research directions are given.

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