For the GDBP, reasonable amount of temporal traffic fluctuations through out the day will create a margin for energy saving.
In this section, we propose two dynamic PBS switching off algorithms for energy saving, which make use of the spatial and temporal traffic load fluctuation in Hetnets.
Future research work would include performance evaluation with spatial and temporal traffic distributions which depend on the day of the week and the time of the day.
Besides adaptations to the temporal traffic variations, the BS can adapt capacity to the spatial traffic variations through dynamic scaling of transmitted power according to capacity demand.
We then take account of the short-term traffic variation in order to accommodate prediction uncertainty incurred by temporal traffic changes and prediction errors.
However, we were able to identify significant spatial, temporal, traffic, roadway and accident characteristics that influenced ice-accident frequency and severity.
To solve this problem, temporal traffic offloading, which smooths traffic by moving peak traffic to off-peak time, has been proposed.
The techniques into the category of CLA allow adapting, customising and extending the infrastructure of the RAN in function of spatial and temporal traffic distributions, aiming to reduce the network energy consumption.
However, if the range of variation including temporal traffic changes within the next control interval is not appropriately decided, the route cannot accommodate the shorter-term variation and congestion still occurs.
With the provided temporal traffic flow profiles and other inputs, the attribute matrixes can be automatically updated and the dynamic charging load of the specific CS could be simulated, based on the proposed traffic-power simulation framework which includes all above modules, i.e., evolution rules for the CA model.
