As the number of mobile devices grows along with demand for faster connections and larger data volumes, wireless networks can easily exceed capacity, resulting in severe network slowdowns and outages. While engineers have developed various sophisticated signal processing methods to accommodate sudden changes in network loads, it has been challenging to evaluate and compare the performance of different approaches in realistic network environments. The reason for this difficulty is that network outages due to capacity saturation can be such rare events that producing simulations to identify outages can be very computationally intensive and take considerable time.
Raul Tempone and colleagues from the KAUST Computer, Electrical, and Mathematical Science and Engineering Division have now applied an importance sampling technique that can simulate rare events for the problem of wireless outage capacity.
“The outage capacity is one of the most important performance metrics of wireless communication systems,” explained Tempone. “It measures the percentage of time that the communication system undergoes an outage, which is typically in the order of one second per million or more. There are no efficient analytical solutions to this problem, and to simulate this situation using conventional simulation methods might take more than a billion simulation runs.”
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