Backhaul Routing and Base Station Sleep Mode Engagement in Energy Harvesting Cellular Networks

Abstract

Future dense mobile networks will imply much higher costs both in access and backhaul. This paper analyzes the effect on wireless mesh backhaul routing performance when energy saving policies are present at the radio access network (RAN). We consider an heterogeneous two-tier network where small cells (SC) with energy harvesting capabilities extend the capacity of the macro base stations (MBS), and can autonomously switch on-off in order to increase the energy efficiency of the network based on a Q-learning (QL) algorithm. Instead of calculating new routes for each SCs activation pattern, we propose to agnostically adapt to the RAN traffic demands using a non-route-based backpressure routing policy for the wireless mesh backhaul to even the network resource usage amongst SCs. We used the ns-3 simulator to integrate the different mobile network segments: RAN, wireless mesh backhaul, and evolved packet core (EPC). Simulation results show an achieved reduction of the %37% of the RAN energy consumption while satisfying traffic demands with an improvement of up to a factor of 10 of delay performance in the backhaul during peak hours.