EnRoute: A DQN based Energy Efficient Routing for URLLC in Next Generation Networks

Abstract

5G networks and beyond (B5G) will support a wide range of services which demand large data rates, massive connection densities, high reliability and low latency. Catering to such needs will require a dense deployment of gNodeBs (gNBs), leading to enormous network energy consumption, thereby contributing to the global carbon emissions. Conventional network energy saving techniques, such as gNB sleep scheduling, significantly hamper the latency performance of delay-sensitive data. To this end, we propose EnRoute for energy efficient route selection of delay-sensitive traffic in B5G networks. We formulate an integer programming problem to optimally select a routing path for delay sensitive data such that its target delay is met while maximizing the energy efficiency of gNBs. Since the problem is NP-complete, we resort to DQN-based learning framework for designing EnRoute. Our simulation results confirm an energy efficient route selection and the key performance indices meeting the required QoS targets. We further compare our approach with two benchmark schemes, one that minimizes the latency, and other that maximizes the energy efficiency of gNBs (instead of tackling them together) for the route selection, and show that our latency performance matches the former (99.7%), with a marginal deficit in energy efficiency with respect to latter (10%).