Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network

TL;DR

This paper proposes a trusted cognitive dynamic network architecture that enables efficient data and spectrum trading through various policies, a novel matching algorithm, and trust-based access control, significantly improving reconfiguration speed and reducing computational complexity.

Contribution

It introduces a new architecture with flexible trading policies, a matching-with-pricing algorithm, and a trust mechanism to enhance network efficiency and security.

Findings

Hybrid scheme is ten times faster than centralized scheme.

Matching algorithm reconfigures network up to 1000 times faster.

Trust mechanism incentivizes honest user behavior.

Abstract

Future wireless networks will progressively displace service provisioning towards the edge to accommodate increasing growth in traffic. This paradigm shift calls for smart policies to efficiently share network resources and ensure service delivery. In this paper, we consider a cognitive dynamic network architecture (CDNA) where primary users (PUs) are rewarded for sharing their connectivities and acting as access points for secondary users (SUs). CDNA creates opportunities for capacity increase by network-wide harvesting of unused data plans and spectrum from different operators. Different policies for data and spectrum trading are presented based on centralized, hybrid and distributed schemes involving primary operator (PO), secondary operator (SO) and their respective end users. In these schemes, PO and SO progressively delegate trading to their end users and adopt more flexible cooperation agreements to reduce computational time and track available resources dynamically. A novel matching-with-pricing algorithm is presented to enable self-organized SU-PU associations, channel allocation and pricing for data and spectrum with low computational complexity. Since connectivity is provided by the actual users, the success of the underlying collaborative market relies on the trustworthiness of the connections. A behavioral-based access control mechanism is developed to incentivize/penalize honest/dishonest behavior and create a trusted collaborative network. Numerical results show that the computational time of the hybrid scheme is one order of magnitude faster than the benchmark centralized scheme and that the matching algorithm reconfigures the network up to three orders of magnitude faster than in the centralized scheme.