Integrated Multi-Network Modeling Environment for Spectrum Management

Abstract

We describe a first principles based integrated modeling environment to study urban socio-communication networks which represent not just the physical cellular communication network, but also urban populations carrying digital devices interacting with the cellular network. The modeling environment is designed specifically to understand spectrum demand and dynamic cellular network traffic. One of its key features is its ability to support individual-based models at highly resolved spatial and temporal scales.We have instantiated the modeling environment by developing detailed models of population mobility, device ownership, calling patterns and call network. By composing these models using an appropriate in-built workflow, we obtain an integrated model that represents a dynamic socio-communication network for an entire urban region. In contrast with earlier papers that typically use proprietary data, these models use open source and commercial data sets.The dynamic model represents for a normative day, every individual in an entire region, with detailed demographics, a minute-by-minute schedule of each person's activities, the locations where these activities take place, and calling behavior of every individual. As an illustration of the applicability of the modeling environment, we have developed such a dynamic model for Portland, Oregon comprising of approximately 1.6 million individuals. We highlight the unique features of the models and the modeling environment by describing three realistic case studies.Index Terms-Socially-coupled networks, synthetic information, behavioral and social modeling, cellular and wireless networks, dynamic spectrum.

Synthesis of a high resolution social contact network for Delhi with application to pandemic planning

“…This includes structural properties of the time varying network [9, 13], visitation of individuals to locations [7], temporal variation of location occupancy [9], homophily properties in subnetworks [68], structural motifs [16, 65] etc. Note that the structural properties were not a part of the model input.Finally, we performed structural validation [65, 69] through a comparison of mobility results with recently published mobility data and laws [7], epidemic dynamics, and interventions over structured networks [18]. …”

“…This includes: ( i ) location assignment models based on generalization of gravity models [35, 39, 62–64], ( ii ) homophily-based assignment of activity templates to individuals [35, 64, 65], ( iii ) within and across host disease progression models [49, 66, 67], ( iv ) models of social distancing and compliance [18, 35]. …”

“…This includes structural properties of the time varying network [9, 13], visitation of individuals to locations [7], temporal variation of location occupancy [9], homophily properties in subnetworks [68], structural motifs [16, 65] etc. Note that the structural properties were not a part of the model input.…”

“…Finally, we performed structural validation [65, 69] through a comparison of mobility results with recently published mobility data and laws [7], epidemic dynamics, and interventions over structured networks [18]. …”

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