QoS-Aware Energy-Efficient Downlink Predictive Scheduler for OFDMA-Based Cellular Devices

We propose a predictive energy-efficient scheduling scheme that optimizes the user equipment (UE)'s bits/joule metric subject to quality-of-service (QoS) constraints in downlink orthogonal frequency-division multiple access (OFDMA) systems. This is achieved by minimizing the number of wake-up transmission time intervals (TTIs), where the UE receiver circuit is on, in a longer time horizon than studied before. The proposed predictive scheduler is supported by a ray-tracing (RT) engine that increases the scheduler's knowledge by long-term information about the users' propagation characteristics. A convex multiobjective binary-integer-programming formulation for the problem is presented to optimize both the UE's energy efficiency (EE) and QoS. The multiobjective formulation is then used for benchmarking of a low-complexity and computationally efficient heuristic scheduler. The results show that the proposed schedulers have significantly improved the UE's EE and the overall capacity of the system, compared with a recently published EE scheduling scheme while maintaining target QoS.