A Matching Game for Data Trading in Operator-Supervised User-Provided Networks

TL;DR

This paper introduces a distributed matching game approach for data trading in user-provided networks, improving user utility and network efficiency through a stable, adaptive market-based algorithm.

Contribution

It formulates the data trading as a one-to-many matching game with externalities and proposes a distributed algorithm for stable, self-organizing data exchange.

Findings

Up to 25% increase in average user utility.

Up to 50% improvement over random matching.

Effective dynamic price adaptation.

Abstract

In this paper, we consider a recent cellular network connection paradigm, known as user-provided network (UPN), where users share their connectivity and act as an access point for other users. To incentivize user participation in this network, we allow the users to trade their data plan and obtain some profits by selling and buying leftover data capacities (caps) from each other. We formulate the buyers and sellers association for data trading as a matching game. In this game, buyers and sellers rank one another based on preference functions that capture buyers' data demand and QoS requirements, sellers' available data and energy resources. We show that these preferences are interdependent and influenced by the existing network-wide matching. For this reason, the game can be classified as a one-to-many matching game with externalities. To solve this game, a distributed algorithm that combines notions from matching theory and market equilibrium is proposed. The algorithm enables the players to self-organize into a stable matching and dynamic adaptation of price to data demand and supply. The properties of the resulting matching are discussed. Moreover, the price benchmark for the users to join the UPN and the operator gain are also determined. Simulation results show that the proposed algorithm yields an improvement of the average utility per user up to 25% and 50% relative to random matching and worst case utility, respectively.