Adaptive Power Load Balancing in Cellular Networks

Load balancing in mobile cellular networks is an important mechanism that enables distribution of demand across neighboring cells, which is critical for better resource utilization and user satisfaction. Current approaches for load balancing are reactive, redistributing users only when the offered load approaches the cell capacity. This approach can lead to deteriorated network performance and user experience. In order to better cater to users, mobile networks need to be proactive and provision resources based on expected demand. To this end we propose a load balancing mechanism that allows for proactive network configuration based on prediction of traffic load. Our approach makes use of power control mechanisms to reconfigure the coverage of a mobile base station and thus control the amount of users and offered load at that base station. We apply our method on a real-world cellular network in Senegal and show that it enables better distribution of load in Orange Telecom’s network in Senegal

Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic.

The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels