LAUSR

Energy-efficient communications have become one fundamental aspect for today's cutting-edge wireless technologies due to its valuable impact on the environment. In this paper, we augment our earlier study for the user equipment's (UE) energy efficiency (EE) in the long-term evolution (LTE) downlink by looking at real-time heterogeneous traffic QoS requirements. In particular, we utilize the previously proposed cloud radio access network (C-RAN) and ray tracing (RT)-based scheduling model to optimize both of the EE and the packet delay jitter for real-time applications with fixed packet delay budget subject to other traffic types requirements. Using the utility-based scheduling approach, we formulate the resource allocation problem as a weighted sum binary integer programming (BIP) problem. Due to the inherent complexity of the problem formulation which hinders finding its solution directly, four heuristic algorithms are proposed to solve the optimization problem. Numerical simulations are conducted on three different traffic types each belonging to one of the popular QoS classes; best-effort class, rate, and delay-constrained classes. The obtained results demonstrate a substantial improvement in the system's performance achieved by our proposed schemes compared to other existing schemes.