Bargaining-Based Mobile Data Offloading

Abstract

The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks, and calls for novel network architectures and communication solutions. Data offloading through third-party WiFi or femtocell access points (APs) can effectively alleviate the cellular network congestion in a low operational and capital expenditure. This solution requires the cooperation and agreement of mobile cellular network operators (MNOs) and AP owners (APOs). In this paper, we model and analyze the interaction among one MNO and multiple APOs (for the amount of MNO's offloading data and the respective APOs' compensations) by using the Nash bargaining theory. Specifically, we introduce a one-tomany bargaining game among the MNO and APOs, and analyze the bargaining solution (game equilibrium) systematically under two different bargaining protocols: (i) sequential bargaining, where the MNO bargains with APOs sequentially, with one APO at a time, in a given order, and (ii) concurrent bargaining, where the MNO bargains with all APOs concurrently. We quantify the benefits for APOs when bargaining sequentially and earlier with the MNO, and the losses for APOs when bargaining concurrently with the MNO. We further study the group bargaining scenario where multiple APOs form a group bargaining with the MNO jointly, and quantify the benefits for APOs when forming such a group. Interesting, our analysis indicates that grouping of APOs not only benefits the APOs in the group, but may also benefit some APOs not in the group. Our results shed light on the economic aspects and the possible outcomes of the MNO/APOs interactions, and can be used as a roadmap for designing policies for this promising data offloading solution.

Contract Design for Traffic Offloading and Resource Allocation in Heterogeneous Ultra-Dense Networks

“…2) Fairness and monotonicity: Theorem 1 demonstrate that, for both of the service requester and service providers, i.e., the MBS and SBSs, respectively, the proposed contractbased traffic offloading and resource allocation mechanism as (14) guarantees the fairness and incentive property of the transmission resource market, in the scenario of information asymmetry and that service providers are heterogeneous. On the one hand, monotonicity of (20a) and (20b) implies that for the SBSs with higher θ, they are more suitable for offloading traffic, and their best choice to achieve highest payoff is offloading larger amount of traffic.…”

“…The feasibility conditions of the traffic offloading contacts formulated in (14) will be analyzed and derived in Section V.…”

“…Then the optimization problem of traffic offloading processed by the SDWN controller can be formulated as (14), with the IC constraint being removed. We provide the optimal traffic offloading solution for the HetUDN as Lemma 1.…”

“…Then for each contract {T n (y n ) , y n }, the amount of traffic offload y allocated to each SBS and payment T (y) obtained by (14) have the monotonicity. Specifically, if and only if θ 1 < θ 2 …”

“…For traffic offloading in SDWN-based HetUDNs, competition and cooperation among resource providing and utilizing entities can be modeled and analyzed with the help of economics theory [13]. Game theory is a valid tool to model the supply-demand relationship of resource for traffic offloading in HetNets, and many different game theory based offloading approaches have been applied, such as Nash bargaining game [14], [15], coalition game [16], Stackelberg game [17], etc. In HetNets with densely distributed BSs, the computational complexity of such approaches grows exponentially, so that mean-field games are needed for low-complexity tractable partial differential equation based solutions to deal with traffic offloading [18], [19], [20].…”