Traffic offloading scenarios for heterogeneous networks

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.

Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market

Mobile data offloading using unlicensed WiFi spectrum represents one of the innovative approaches that can be adopted to cope with the huge amount of mobile traffic demand. However, the main challenge in such approach lies in the ability of the system to discover the WiFi coverage for users who may need to offload their data. For this, the access network discovery and selection function (ANDSF) has been introduced by 3GPP to provide the mobile user equipment (UE) with the discovery information and the list of available WiFi hotspots. However, the current ANDSF discovery mechanism depends on the geographical location of the UE, which need to be accurately determined and continuously transmitted to the ANDSF server. In this paper, an enhanced ANDSF WiFi discovery technique is proposed and validated. The proposed technique is designed to use the reference signal received power (RSRP) information measured at UEs from their surrounding cellular base stations to build fingerprints for various WiFi hotspot areas. Based on the constructed fingerprints, the UE's WiFi coverage state can be identified using the proposed decision tree (DT) machine learning approach. The conducted tests and generated results validate the operation and performance of the proposed machine learning-based discovery technique with accuracy reaches up to 95%.

On the competitive effects of mobile virtual network operators

On UDP continuity over vertical handovers

The collapse of old print media and journalism happened when the Internet, its solutions, services and communities became mature and mobile devices reached the market. The reader abandoned printed dailies for free and mobile access to information. The business of core industries of the early Internet and mobile communication, the mobile network manufacturers and operators are also in stagnation and decline. Therefore these industries may have similar interests to improve or even restructure their own businesses as well as to establish totally new business models by going into media and journalism.This paper analyses, first, the production flows and business models of the old and present media species. Second, it analyses the current market positioning of the network manufacturers and operators. Third, the paper suggests two avenues for media and journalism and the network manufacturers and operators, the Trio, to join their forces to update journalism and make all three stagnating industries great again. Last, we propose further research, development and discussion on the topic and envision possible futures for journalism, if the three would engage in cooperation. We see that the discussion should consist of ethical, societal and philosophical subjects because the development of the Internet solutions are based on technology first actions.We find and outline a tremendous opportunity to create a new industry with new actors through combining the interests of the network manufacturers, network operators and journalism in a systemic solution through a strategic alliance and collaboration Fig. 1. Software startuppers with their applications and communities will be the drivers for this abstraction shift in media and journalism.Our experiences in the media, journalism, mobile network, mobile phone manufacturing and startups provide the basis for our formulations on the future of those industries.

Recently, hybrid satellite-terrestrial networks (H-STNs) are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing and interference control technology paradigms. By achieving an efficient spectrum sharing among H-STN, traffic offloading is a promising solution for boosting the capacity of traditional cellular networks. In this paper, a software-defined network-based spectrum sharing, and traffic offloading mechanism is proposed to realize the cooperation and competition between the ground base stations (BSs) of the cellular network and beam groups of the satellite-terrestrial communication (STCom) system. Assume that all BSs are operated by the same mobile network operator (MNO). Under the cooperation mode, all the BSs stop occupying a corresponding channel, and a selected beam group of the satellite helps offload the traffic from the BSs by exclusively using this channel. To facilitate the offloading negotiation between the MNO and satellite, we design a second-price auction mechanism which presents positive allocative externalities, i.e., other uncooperative beam groups of the satellite can benefit from the cooperation between BSs and the beam group performing offloading. Meanwhile, the unique optimal biding strategies for different beam groups of the satellite to achieve the symmetric Bayesian equilibrium as well as the expected utility of the MNO are derived and obtained in this paper. The performance of the proposed traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MNO to achieve the maximum expected utility.

Fifth generation (5G) cellular networks demand high throughput with low levels of delay and congestion within the core network. Excessive throughput demands at the core network with the exponential increase in the number of mobile users and devices have become major challenges for 5G. Several techniques have been developed for offloading traffic at the front line of cellular networks. For example, Local IP access (LIPA) and selective IP traffic offload (SIPTO) are current data offloading technologies. In this article, we propose femtocell IP access (FIPA) and selective local controller traffic offloading (SLCTO) technique to offload traffic at the edge of the cellular network without forwarding it through the core network. Our proposed FIPA and SLCTO technologies rely on our previously proposed 3-Tiered software defined networking (SDN) architecture for dense networks (DenseNets). According to our simulations, FIPA and SLCTO over a 3-Tiered SDN architecture outperform existing offloading techniques with low delay, high throughput, and low cost.

Mobile data offloading through third-party Femtocell access points (FAPs) is an emerging technology which is used to alleviate congestion in cellular network. We propose a distributed algorithm that combines college admissions game with auction theory to achieve the maximum social welfare of offloading and guarantee the Quality of service (QoS) of Mobile users (MUs). As the optimal problem is NP-hard, the college admissions game is developed to solve the sub-problem of matching between FAPs and MUs. The Second revenue sealed-bid auction with a reserve price (SRSARP) algorithm is developed to solve another sub-problem of matching between Wireless operators (WOs) and FAPs. The simulation results reveal that the distributed algorithm we proposed achieves the performance very close to that of the centralized method and significantly better than those of the random and Maxprice algorithms.

In this paper, we propose an improved IP-based handover scheme, named the Heterogeneous Network Handover by using 3GPP ANDSF (HNH3A for short) for S-PMIPv6 where S-PMIPv6 is a MIPv6 family protocol developed in one of our previous studies, and ANDSF (standing for Access Network Discovery and Selection Function) is an entity within an evolved packet core (EPC) of the system architecture evolution (SAE) for 3GPP compliant mobile networks. The purpose of using the ANDSF is to assist user equipment (UE) to discover non-3GPP access networks, such as WiFi or WIMAX. The HNH3A can help the handover among mobile WiMAX (i.e., Worldwide Interoperability for Microwave Access), 3GPP (i.e., 3rd Generation Partnership Project) family (including 3GPP systems, 3G LTE and 4G LTE-A) and WiFi based on existing handover techniques among the three heterogeneous networks. The analytical results demonstrate that the HNH3A can effectively mitigate handover delays and handover signaling costs.

Network Offloading schemes play a vital role when there is a massive growth in the capacity of Next Generation Networks. The aggregation of LTE (Long Term Evolution) and WLAN (Wireless Local Area Network) is meant to provide service with seamless connectivity for the User Equipment (UE). There are two major issues that have been identified in existing RSSI (Received Signal Strength Indicator) based offloading scheme such as: i) it fails to provide better performance ii) it requires frequent reconnection for establishing dual connectivity. UE (User Equipment) based offloading approach requires a lot of signaling between UE and Radio access network and thus lacks in resource utilization and energy efficiency. Network based offloading approach needs a network specific entity. In the proposed LWA-SA (LTE WLAN Aggregation-SDN Assisted) which is an SDN (Software Defined Networking) based offloading scheme. In this, a controller keeps track of ongoing sessions after offloading. Here, an algorithm is used based on Access Network Discovery and Selection Function (ANDSF) offloading scheme for the purpose of selecting an efficient Wi-Fi access point which increases the throughput and reduces the delay and also provides seamless connectivity to the UE.

The paper analyzes gross upward pricing pressure indices—texttt {iGUPPI}—to assess the anti-competitive effects of mergers between vertically integrated firms where independent rivals are active in the downstream market. Such indices could be used, for example, to screen mergers between mobile network operators that compete with mobile virtual network operators in the downstream retail market. It is shown that the texttt {iGUPPI} for the downstream divisions of the merging firms corresponds to the sum of two well-known upward pricing pressure indices: the texttt {GUPPI} concept of Salop and Moresi (Georget Law J, 2009), and the texttt {vGUPPI} concept of Moresi and Salop (Antitrust Law J 79(1):185–214, 2013). However, this simple decomposition does not hold for their upstream divisions a priori. Here, additional effects that are not included in the two concepts arise. Further assumptions with respect to the price reactions of the downstream divisions to increases in the input prices are imposed so that the texttt {iGUPPI} for the upstream divisions is decomposable into an upstream market version of the texttt {GUPPI} and the texttt {vGUPPI}.

Competition in Wireless Telecommunications: The Role of MVNOs and Cable’s Entry into Wireless

This paper analyzes the spectrum trading problem in virtualized fifth generation networks to enhance the network performance with respect to the spectrum utilization. The problem is modeled as a many‐to‐many matching game with utility‐based preferences and determines the matching between mobile network operators and mobile virtual network operators. The two proposed versions of utility functions for each set aim at maximizing the satisfaction of both sets with conflicting interests and improving the overall spectrum efficiency. In the simulation evaluation, the proposed scheme is compared with three different schemes in terms of the system utility, individual, and pair matching satisfaction. We also investigate the scalability aspects, the strategy plan impact on the matching performance of our proposed scheme, and at the same time, we attempt to make appropriate assumptions closer to reality. Our proposed scheme shows much better performance than the other schemes achieving a quite high level of satisfaction for the matching result on both sets.

Smartphone users' requirements for data plans from mobile network access services have become individualized. To cope with users' requirements, global mobile network operators have been replacing flat-rate data plans with tiered data plans in the mobile market. In addition, mobile virtual network operators who provide data plans to their own customers at retail prices have emerged in the mobile market. In today's mobile market, customers can therefore choose their preferred data plan from a number of alternatives. There is, however, limited knowledge about how sensitively customers react to changed pricing for a data plan. In this paper, we focus on tiered data plans, i.e., specific pairing of price and data capacity, and empirically analyze the utility for customers of the tiered data plans by applying choice-based conjoint analysis. We also evaluate customer behavior through trace-driven simulations to quantify the market share of the given data plans.