MSc. Ramsha Narmeen

User applications based on the deep neural networks (DNNs), such as object or anomaly detection, image recognition, or language processing, running on computation- and energy-constrained user equipment (UE) can be partially or fully processed in the edge computing servers to reduce a processing time and save an energy in the UE. To further reduce the processing time and the UE's energy consumption, DNN with multiple exit points can be incorporated. In this article, we address the problem of the decision on whether the computation should be offloaded from the UE to the edge computing server or processed locally by the UE and we solve this problem jointly and "on-the-fly" together with DNN exit selection. Since the formulated problem is very complex, we exploit the deep deterministic policy gradient for the exit selection and the offloading decisions (labeled DDPG-EOD) for the DNN-based applications. To this end, we first convert the problem into the Markov decision process, and then, we employ an end-to-end learning via DDPG with the actor-critic architecture. Second, we use a knowledge distillation-based technique to efficiently select the DNN's exit to minimize the delay and energy consumption. Simulation results show that the proposal is highly scalable, converges very quickly, and surpasses the best performing state-of-the-art approach by up to 120% and 100% in terms of the overall DNN processing delay and the energy consumption, respectively.

Unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for numerous applications involving aerial networks. However, the UAV-RIS concept faces challenges related to the deployment of the UAV-RIS, especially in cases, where UAV-RIS is combined with emerging technologies, such as beamforming, sensitive to propagation channel variation. In this article, we first overview various use-cases of UAV-RIS beam-forming considering practical scenarios. Aiming to improve the performance of communication channels, we propose a machine learning-based beamforming policy for UAV-RIS by employing prioritized experience replay (PER) based deep Q-Network (DQN). Compared to traditional approaches, the proposed PER DQN-based beamforming for UAV-RIS communication provides significant enhancements in performance. Finally, we highlight some potential directions for future research.