Joint Resource Bidding and Tipping Strategies in Multi-hop Cognitive Networks
Beatriz Lorenzo, Ivana Kovacevic, Ana Peleteiro, Francisco J., Gonzalez-Castano, Juan C. Burguillo

TL;DR
This paper introduces a joint resource bidding and tipping framework for multi-hop cognitive networks, optimizing auction mechanisms to enhance revenue, fairness, and social welfare while satisfying key economic properties.
Contribution
It develops a novel auction framework with a bidding language and group partitioning schemes that improve resource allocation and revenue in multi-hop secondary networks.
Findings
Static group scheme increases winners by 150% and triples PO revenue for high-demand users.
Dynamic scheme maintains revenue while reducing prices for low-demand users.
Proposed mechanisms satisfy truthfulness, individual rationality, and computational efficiency.
Abstract
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…
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