A Truthful Auction Mechanism for Dynamic Allocation of LSA Spectrum Blocks for 5G

Chouayakh, Ayman; Bechler, Aurélien; Amigo, Isabel; Nuaymi, Loutfi; Maillé, Patrick

doi:10.1007/978-3-030-10880-9_13

Cited by 2 publications

(2 citation statements)

References 14 publications

(26 reference statements)

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“…In [21], the authors form groups by extracting all maximum independent sets; however finding the maximum independent sets in a graph is an NP-Hard problem. In [22], we have proposed a heuristic for group construction which is easy and fast (quasi-linear complexity with the number of base stations), first sorting BSs in an ascending order of their degree of interference, and treating them in that order while grouping them together as much as possible. The intuition behind this is to create large groups to get closer to the maximum independent sets.…”

Section: System Model and Bidder Preferencesmentioning

confidence: 99%

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Multi-Block Ascending Auctions for Effective 5G Licensed Shared Access

Chouayakh

Bechler

Amigo

et al. 2022

IEEE Trans. on Mobile Comput.

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Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are oneshot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction.

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Section: System Model and Bidder Preferencesmentioning

confidence: 99%

“…We denote by S MEC the vector composed of all the groupbid vectors sorted in a non-increasing order. The size of S MEC is M × K. In Example 5, S VCG = (22,17,16,15,14,13,10,9,9,5,3,1). In the following we provide the convergence rate of the auction.…”

Section: Implementation With Representativesmentioning

confidence: 99%