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Concerns have been raised regarding the economical viability for each operator to have a full regional 5G coverage. A possible solution is to have traffic offloaded to competitors. In this work we present a new scheme for optimal offloading in a stochastic environment. This is more in line with the conditions 5G base stations will face with changing link and traffic conditions. The problem is formulated as a Stackelberg game, and the players' utility functions are derived though queuing models. Numerical results illustrate that our scheme provides a global optimal resource allocation up to a threshold. The threshold is a function of the traffic load and the number of offloading candidates. Beyond the threshold players still have incentives to participate, but the market equilibrium is not globally optimal.

Energy usage in LTE base stations are driven by spectral efficiency and traffic. To predict the energy usage these parameters must be forecasted. In this work we analyse hourly measurements collected from more than 12000 base station cells spread across more than 3700 base stations over the course of one month. We show that the two parameters are very weakly correlated, and therefore we investigated them separately. Further, we evaluated the possible gains for advanced prediction methods using a large scale search for individually fitted time series (SARIMA models) for each base station. In total, we examined and evaluated approximately 31000 time series models from an identified group of 4 million potential models. We found that the spectral efficiency measurements can be represented fairly well with time series models, with only an average 6.5% relative error. The time series models have to be individually adapted for each base station as the unsupervised clustering showed that each cluster's members have a wide variety of best fitted models. However, for traffic the time series models have relative high prediction errors, and we believe there is a potential for new methods to improve the forecasts.