Multiple Mobile Operators Research Articles

Trusted Cooperation Among Virtual Base Stations in C-RAN

Cloud radio access network (C-RAN) has become one of the hot-spot research directions in both industry and academia to facilitate 5G development. Its architecture is mainly divided into three parts: 1) Virtual base station (VBS) pool including a number of base band units that apply for real-time cloud computing technology to carry out digital processing tasks; 2) Remote radio heads (RRH) that are used to collect the wireless signals from all wireless devices; 3) Front-haul network connecting the RRH to the VBS pool. The information of base stations (BS) resides in a centralized VBS pool, multiple operators can allow their VBS pools to exchange control data and cooperate with each other to provide high-quality 5G services. In this process, trustworthy cooperation and platform-trust authentication among multiple mobile operators are required to realize high networking performance and secure 5G environment. However, this topic has been scarcely studies and is still in its infancy. In this paper, we first propose a C-RAN inter-operator cooperation scheme (IOCS) to support the cooperation of multiple operators in C-RAN and allow them to share resources in a trustworthy and secure way based on trusted computing platform. An access trust management pool including VBS trust managers is set up in IOCS to administrate an operator’s VBS pool. It allows an end user to consume other operators’ network resources in a trustworthy manner when its own operator’s network cannot satisfy the quality of service requirements. Furthermore, we design a trusted cooperative platform based on OpenStack to implement IOCS. Performance evaluation and simulation results illustrate the high operation efficiency in IOCS comparing with other schemes. Game theoretical analysis shows the condition of inter-operator cooperation by taking operator trust into concern.

User Modeling on Demographic Attributes in Big Mobile Social Networks

Users with demographic profiles in social networks offer the potential to understand the social principles that underpin our highly connected world, from individuals, to groups, to societies. In this article, we harness the power of network and data sciences to model the interplay between user demographics and social behavior and further study to what extent users’ demographic profiles can be inferred from their mobile communication patterns. By modeling over 7 million users and 1 billion mobile communication records, we find that during the active dating period (i.e., 18--35 years old), users are active in broadening social connections with males and females alike, while after reaching 35 years of age people tend to keep small, closed, and same-gender social circles. Further, we formalize the demographic prediction problem of inferring users’ gender and age simultaneously. We propose a factor graph-based WhoAmI method to address the problem by leveraging not only the correlations between network features and users’ gender/age, but also the interrelations between gender and age. In addition, we identify a new problem—coupled network demographic prediction across multiple mobile operators—and present a coupled variant of the WhoAmI method to address its unique challenges. Our extensive experiments demonstrate the effectiveness, scalability, and applicability of the WhoAmI methods. Finally, our study finds a greater than 80% potential predictability for inferring users’ gender from phone call behavior and 73% for users’ age from text messaging interactions.

Hybrid Spectrum Sharing in mmWave Cellular Networks

While spectrum at millimeter wave (mmWave) frequencies is less scarce than at traditional frequencies below 6 GHz, still it is not unlimited, in particular if we consider the requirements from other services using the same band and the need to license mmWave bands to multiple mobile operators. Therefore, an efficient spectrum access scheme is critical to harvest the maximum benefit from emerging mmWave technologies. In this paper, we introduce a new hybrid spectrum access scheme for mmWave networks, where data is aggregated through two mmWave carriers with different characteristics. In particular, we consider the case of a hybrid spectrum scheme between a mmWave band with exclusive access and a mmWave band where spectrum is pooled between multiple operators. To the best of our knowledge, this is the first study proposing hybrid spectrum access for mmWave networks and providing a quantitative assessment of its benefits. Our results show that this approach provides major advantages with respect to traditional fully licensed or fully unlicensed spectrum access schemes, though further work is needed to achieve a more complete understanding of both technical and non technical implications.

Joint Demand-Side Management in Smart Grid for Green Collaborative Mobile Operators Under Dynamic Pricing and Fairness Setup

In this paper, the interactions between multiple mobile operators, owning heterogeneous cellular networks, and energy retailers existing in the smart grid are investigated. Energy procurement decisions of the cellular networks sharing common energy sources are jointly optimized while considering both dynamic energy pricing and varying pollution levels of energy sources. The objective is to enable collaboration among mobile operators in their demand-side management (DSM) to achieve economical and environmental goals. This is performed by solving a unified optimization problem aiming at maximizing a metric based on mobile operators’ profits while limiting the amount of carbon dioxide (CO2) emitted by all networks. In this paper, three utility metrics, namely, Sum, Max-min, and Proportional Fair utilities, are considered to reflect the degree of fairness among mobile operators in the optimized DSM. Closed-form expressions of the optimal procured energy from each retailer used to power the marcocell and active small cell base stations (BSs) are derived for pricing profiles and utility metrics. Furthermore, the BS sleeping strategy is applied, in a centralized or a decentralized manner, in order to reduce the network energy consumption during low traffic periods. Finally, the impact of renewable energy generation uncertainty on the energy procurement decision is examined. The behavior of the different actors in the energy procurement decision is investigated through simulations. Results show that significant CO2 emissions reduction can be achieved thanks to the optimized DSM and the BS sleeping strategy.

Modelling Multi-Operator Base Station Deployment Patterns in Cellular Networks

Stochastic models of base station infrastructure deployment by multiple mobile operators can be an invaluable tool for deriving fundamental results about wireless network sharing. In this paper, we study stochastic geometry models for a shared cellular network consisting of base stations deployed by multiple mobile operators, based on real cellular network data coming from three European countries. Relying on a statistical approach as well as the evaluation of wireless network performance metrics, we show that the log-Gaussian Cox process provides the most compelling fitness results with real multi-operator base station deployment patterns and a model that offers some degree of analytical tractability. The model captures the fact that, in urban areas, there is strong correlation between the locations where the base stations of different operators are deployed. In contrast to that, in rural areas we observe some repulsion between antenna locations of different operators. Moreover, we observe that the behavior which can be modelled with the help of these processes occurs over and over again for similar areas in different countries, which suggests universality of the proposed models.