Mobile Data Offloading with Uniform Pricing and Overlaps

Mobile data offloading can help the mobile network operator (MNO) cope with the explosive growth of cellular traffic, by delivering mobile traffic through third-party access points. However, the access point owners (APOs) would need proper incentives to participate in data offloading. In this paper, we consider a data offloading market that includes both price-taking and price-setting APOs. We formulate the interactions among the MNO and these two types of APOs as a three-stage Stackelberg game, and study the MNO’s profit maximization problem. Due to a non-convex strategy space, it is in general a non-convex game. Nevertheless, we transform the strategy space into a convex set and prove that a unique subgame perfect equilibrium exists. We further propose iterative algorithms for the MNO and price-setting APOs to obtain the equilibrium. Employing the proposed algorithms, the APOs do not need to obtain full information about the MNO and other APOs. Through numerical studies, we show that the MNO’s profit can increase up to three times comparing with the no-offloading case. Furthermore, our proposed incentive mechanism outperforms an existing algorithm by 18 percent in terms of the MNO’s profit. Results further show that price competition among price-setting APOs drives the equilibrium market prices down.

Mobile data offloading is a promising approach to alleviate network congestion and enhance quality of service (QoS) in mobile cellular networks. In this paper, we investigate the economics of mobile data offloading through third-party WiFi or femtocell access points (APs). Specifically, we consider a market-based data offloading solution, where macrocellular base stations (BSs) pay APs for offloading traffic. The key questions arising in such a marketplace are following: (i) how much traffic should each AP offload for each BS? and (ii) what is the corresponding payment of each BS to each AP? We answer these questions by using the non-cooperative game theory. In particular, we define a multi-leader multi-follower data offloading game (DOFF), where BSs (leaders) propose market prices, and accordingly APs (followers) determine the traffic volumes they are willing to offload. We characterize the subgame perfect equilibrium (SPE) of this game, and further compare the SPE with two other classic market outcomes: (i) the market balance (MB) in a perfect competition market (i.e., without price participation), and (ii) the monopoly outcome (MO) in a monopoly market (i.e., without price competition). Our results analytically show that (i) the price participation (of BSs) will drive market prices down, compared to those under the MB outcome, and (ii) the price competition (among BSs) will drive market prices up, compared to those under the MO outcome.

Mobile data offloading is a cost-effective and promising approach to alleviate the burden of the cellular network. In this paper, we focus on designing a truthful incentive mechanism that the auction cloud platform motivates mobile users with deadline constraints and capacity constraints to offload data. First, we formulate the mobile data offloading problem as a reverse auction. Then, we reduce this social welfare maximization reverse auction for data offloading as an integer linear programming (ILP) problem. To solve this problem, we propose a data offloading auction greedy algorithm to choose the most cost-effective bids. We decompose the data offload auction greedy algorithm into the winner determination problem (WDP) and the threshold payment scheme. Moreover, we propose a delay winner winning algorithm based on the data offloading auction greedy algorithm. Extensive simulation experiments demonstrate that the data offloading auction mechanism dramatically reduces the burden of the cellular network while meeting the offloading tasks deadline constraints and mobile user capacity constraints.

Dynamic pricing strategies: Uniform or discrimination for multi-channel retailing under platform-based supply chain competition

Existing cellular network infrastructure will likely be compromised in the future by the increase of mobile users and mobile data services. Continuous improvements of network infrastructure and several mobile data offloading strategies are promising solutions for cellular providers to cope with the demand increase of mobile data. However, these options require a lot of cost and time to implement. Few studies have been conducted to assess whether Pocket Switched Network (PSN) can be applied to support mobile data offloading. In this paper, we present a PSN mobile data offloading scheme that utilizes mobile users with available connectivity to deliver content-aware data to other mobile users. This paper also aims to evaluate whether PSN routing schemes can be applied to improve the current strategies in mobile data offloading. The simulation study exhibits promising results in terms of message delivery and latency reduction.

Competitive nonlinear pricing with product differentiation

With increasing demands for mobile data traffic, many base stations (BSs) consume a significant amount of electrical power with a lot of electricity bill. Many practical solutions include sharing BSs among mobile network operators (MNOs), in which an MNO’s BS allows to serve traffic from nearby users subscribing to other MNOs with being paid a certain roaming fee. However, without assurance of gains, MNOs would not agree to BS sharing. In this paper, we study pricing and user association policies that assure actual gains to each MNO in the BS sharing. We model this with a game that jointly involves the strategic decision of roaming price and user association, where we consider the flow-level dynamics of traffic. We assume a time-scale separation where pricing decision is made at a slower time scale than user association, as often done in practice. First, for a fixed roaming price we analyze the user association game, where we prove that (i) it is a potential game, (ii) there exists a unique pure Nash equilibrium (NE), and (iii) a distributed algorithm inspired by an approximate version of Jacobi play converges to the NE. We study the slower time-scale pricing decision game and prove that there exists a pure NE with achieving almost the efficiency of full cooperation (without roaming fee). We demonstrate that there exists a significant degree of energy saving, once an appropriate competition rule is provided, through numerical simulations under a variety of scenarios.

The opportunistic utilization of access devices to offload mobile data from cellular network has been considered as a promising approach to cope with the explosive growth of cellular traffic. To foster this opportunistic utilization, we consider a mobile data offloading market where mobile network operator (MNO) can sell bandwidth made available by the access points (APs) to increase MNO's profit. We formulate the offloading problem as a multi-item auction and study MNO's profit maximization problem. We discuss the conditions to (i) offload the maximum amount of data traffic, (ii) foster the participation of mobile subscribers (MSs) (individual rationality), (iii) prevent market manipulation (incentive compatibility) and (iv) preserve budget feasibility of MSs. Then, we propose a robust optimization based method to implement multi-item auction mechanism. We further propose two iterative algorithms that efficiently solve the offloading problem. The simulation results show the efficiency and robustness of our proposed methods for cellular data offloading.

In this paper, an incentive proactive cache mechanism in cache-enabled small cell networks (SCNs) is proposed, in order to motivate the content providers (CPs) to participate in the caching procedure. A network composed of a single mobile network operator (MNO) and multiple CPs is considered. The MNO aims to define the price it charges the CPs to maximize its revenue while the CPs compete to determine the number of files they cache at the MNO's small base stations (SBSs) to improve the quality of service (QoS) of their users. This problem is formulated as a Stackelberg game where a single MNO is considered as the leader and the multiple CPs willing to cache files are the followers. The followers game is modeled as a non-cooperative game and both the existence and uniqueness of a Nash equilibrium (NE) are proved. The closed-form expression of the NE which corresponds to the amount of storage each CP requests from the MNO is derived. An optimization problem is formulated at the MNO side to determine the optimal price that the MNO should charge the CPs. Simulation results show that at the equilibrium, the MNO and CPs can all achieve a utility that is up to 50% higher than the cases in which the prices and storage quantities are requested arbitrarily.

In this paper, we establish a game theoretic framework of the auction based dynamic spectrum access for multitype buyers (i.e., the secondary users that have different risk preferences) in cognitive radio networks where the auction mechanisms of the primary user are parameterized and can be adaptively designed. We design the utility functions of the multitype secondary users. The proposed game is a discrete game having at least one mixed or pure strategy Nash equilibrium. Based on the concept of learning automata, we propose a distributed algorithm to learn the Nash equilibrium of the proposed game with limited feedback information and prove its convergence to the Nash equilibrium of our proposed game with properly designed utility function. Simulation results show that our proposed algorithm is efficient and can achieve much higher performance than the traditional fixed auction mechanism schemes and the random algorithm.

In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches.

Multi-access Edge Computing (MEC) has emerged as a flexible and cost-effective paradigm, enabling resource constrained mobile devices to offload, either partially or completely, computationally intensive tasks to a set of servers at the edge of the network. Given that the shared nature of the servers' resources introduces high computation and communication uncertainty, in this paper we consider users' risk-seeking or loss-aversion behavior in their final decisions regarding the portion of their computing tasks to be offloaded at each server in a multi-MEC server environment, while executing the rest locally. This is achieved by capitalizing on the power and principles of Prospect Theory and Tragedy of the Commons, treating each MEC server as a Common Pool of Resources available to all the users, while being rivarlous and subtractable, thus may potentially fail if over-exploited by the users. The goal of each user becomes to maximize its perceived satisfaction, as expressed through a properly formulated prospect-theoretic utility function, by offloading portion of its computing tasks to the different MEC servers. To address this problem and conclude to the optimal allocation strategy, a non-cooperative game among the users is formulated and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal data offloading, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation, while useful insights about the users' data offloading decisions under realistic conditions and behaviors are presented.

Recent years have witnessed a surge in the mobile data traffic which is only expected to grow exponentially in the coming years. To cope up with this huge data demand, mobile operators have restored to a novel scheme called on-the-spot mobile data offloading wherein mobile data is offloaded onto Wi-Fi network whenever the user enters a Wi-Fi coverage area. Further reports indicate that a sizable portion of the mobile data traffic is multimedia in nature that demands good quality of service. In this context, we design and analyze a scheme to prioritize different data traffic encountered in on-the-spot mobile data offloading. Using a Markov chain model for on-the-spot mobile data offloading scheme, we derive the packet transmission delay of different traffic categories that carry different priorities. We validate out analytical results against simulation and demonstrate the effectiveness of the proposed prioritization scheme.

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-to-many 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.

A model of duopolistic spatial price discrimination on a network is extended by adding capacity constraints on firms' outputs. With zero-one consumer demands, some configurations of capacity levels and locations do not have pure strategy Nash equilibria in price schedules, in particular when firms have some excess capacity but not enough to serve the entire market individually. An alternative to the mixed strategy Nash equilibrium in price schedules—the core of the market game among producers and consumers—is described and shown to have allocations corresponding to pure strategies for all location and capacity configurations. If both firms have some excess capacity, price schedules are identical to those in the uncapacitated problem. Equilibrium prices are no higher than the lowest prices in the mixed strategy Nash equilibria.