Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. Recently, mobile data-based research reached important conclusions about various aspects of human mobility patterns. But how accurately do these conclusions reflect the reality? To evaluate the difference between reality and approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest interpolations) taking into consideration mobility parameters. Moreover, we evaluate the error between real and estimated load using the proposed interpolation methods. From extensive evaluations based on real cellular network activity data of the state of Massachusetts, we show that, with respect to human trajectories, the linear interpolation offers the best estimation for sedentary people while the cubic one for commuters. Another important experimental finding is that trajectory estimation methods show different error regimes whether used within or outside the "territory" of the user defined by the radius of gyration. Regarding the load estimation error, we show that by using linear and cubic interpolation methods, we can find the positions of the most crowded regions ("hotspots") with a median error lower than 7%.

In networking and computing, resource allocation is typically addressed using classical resource allocation protocols as the proportional rule, the max-min fair allocation, or solutions inspired by cooperative game theory. In this paper, we argue that, under awareness about the available resource and other users demands, a cooperative setting has to be considered in order to revisit and adapt the concept of fairness. Such a complete information sharing setting is expected to happen in 5G environments, where resource sharing among tenants (slices) needs to be made acceptable by users and applications, which therefore need to be better informed about the system status via ad-hoc (northbound) interfaces than in legacy environments. We identify in the individual satisfaction rate the key aspect of the challenge of defining a new notion of fairness in systems with complete information sharing and, consequently, a more appropriate resource allocation algorithm. We generalize the concept of user satisfaction considering the set of admissible solutions for bankruptcy games and we adapt to it the fairness indices. Accordingly, we propose a new allocation rule we call Mood Value: for each user, it equalizes our novel game-theoretic definition of user satisfaction with respect to a distribution of the resource. We test the mood value and a new fairness index through extensive simulations about the cellular frequency scheduling use-case, showing how they better support the fairness analysis. We complete the paper with further analysis on the behavior of the mood value in the presence of multiple competing providers and with cheating users.

Call Detail Records (CDR) are an important source of information in the study of diverse aspects of human mobility. The accuracy of mobility information granted by CDR strongly depends on the radio access infrastructure deployment and the frequency of interactions between mobile users and the network. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR are often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data. In this paper, we precisely address this subject. First, we evaluate the spatial error in CDR, caused by approximating user positions with cell tower locations. Second, we assess the impact of the limited spatial and temporal granularity of CDR on the estimation of standard mobility metrics. Third, we propose novel and effective techniques to reduce temporal sparsity in CDR by leveraging regularity in human movement patterns. Tests with real-world datasets show that our solutions can reduce temporal sparsity in CDR by recovering 75% of daytime hours, while retaining a spatial accuracy within 1 km for 95% of the completed data.

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Cloud RAN (C-RAN) is a very promising architecture for future mobile network deployment, where the cloudcentric approach is useful in improving total processing load. In this context, radio and baseband network functions processing pose interesting problems that we try to expose and solve in this paper. A novel architecture for C-RAN and a first modeling of the system are proposed. Furthermore, we study the impact of many radio parameters on the processing time. Moreover, a mathematical and a deep learning model are proposed and evaluated for processing time prediction. Results show the feasibility of the proposed approaches.

International audienceNowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. In recent years, mobile data-based research reaches important conclusions about various aspects of human mobility patterns and trajectories. But how accurately do these conclusions reflect the reality? In order to evaluate the difference between the reality and the approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest and spline interpolations) while taking into account some mobility parameters. From extensive evaluations based on real cellular network activity data of the Boston metropolitan area, we show that the linear interpolation offers the best estimation for sedentary people and the cubic one for commuters. Moreover, the nearest interpolation appears as the best one for " ordinary people " doing regular stops and standard displacements. Another important experimental finding described in this paper is that trajectory estimation methods show different error regimes whether used within or outside the " territory " of the user defined by the radius of gyration. Index Terms—Mobility patterns, interpolation methods, tra-jectory estimation, radius of gyration. I. INTRODUCTION Human mobility and behavior pattern analysis has long been a prominent research topic for social scientists, urban planners, geographers and telecommunication researchers, but the perti-nency of its results has thus far been limited by the availability of quality data and suitable data mining techniques. Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. In recent years, mobile data-based research reaches important conclusions about various aspects of human characteristics, such as human mobility and calling patterns [1] [2], social networks [3] [4], content consumption cartography [5], urban and transport planning [6] and network design [7]. Nevertheless, in such user displacement sampling data, a high uncertainty is related to users movements, since available samples strongly depend on the user-network interaction frequency. For instance, we cannot determine the user positions between the calls with an acceptable accuracy. Some modeling techniques have been proposed in the literature to predict user movement between two places. Authors in [9] and [10] infer the top-k routes traveling a given location sequence within a specified travel time from uncertain ckeck-in data. These works permit to identify the most popular travel routes in a city but it does not allow to construct the time-senstive routes

Wireless mesh networks (WMNs) are emerging as a key solution to provide broadband and mobile wireless connectivity in a flexible and cost-effective way. In suburban areas, a common deployment model relies on orthogonal frequency division multiple access (OFDMA) communications between mesh routers (MRs), with one MR installed at each user premises. In this paper, we investigate a possible user cooperation path to implement strategic resource allocation in OFDMA WMNs, under the assumption that users want to control their interconnections. In this case, a novel strategic situation appears: How much an MR can demand, how much it can obtain, and how this shall depend on the interference with its neighbors. Strategic interference management and resource allocation mechanisms are needed to avoid performance degradation during congestion cases between MRs. In this paper, we model the problem as a bankruptcy game taking into account the interference between MRs. We identify possible solutions from cooperative game theory, namely the Shapley value and the nucleolus, and show through extensive simulations of realistic scenarios that they outperform two state-of-the-art OFDMA allocation schemes, namely, centralized-dynamic frequency planning, and frequency-ALOHA. In particular, the nucleolus solution offers best performance overall in terms of throughput and fairness, at a lower time complexity.