Matteo Bordin

Next-generation wireless systems, already widely deployed, are expected to become even more prevalent in the future, representing challenges in both environmental and economic terms. This paper focuses on improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems through the near-real-time dynamic activation and deactivation of Base Station (BS) Radio Frequency (RF) frontends using Deep Reinforcement Learning (DRL) algorithms, i.e., Proximal Policy Optimization (PPO) and Deep Q-Network (DQN). These algorithms run on the RAN Intelligent Controllers (RICs), part of the Open RAN architecture, and are designed to make optimal network-level decisions based on historical data without compromising stability and performance. We leverage a rich set of Key Performance Measurements (KPMs), serving as state for the DRL, to create a comprehensive representation of the RAN, alongside a set of actions that correspond to some control exercised on the RF frontend. We extend ns-O-RAN, an open-source, realistic simulator for 5G and Open RAN built on ns-3, to conduct an extensive data collection campaign. This enables us to train the agents offline with over 300,000 data points and subsequently evaluate the performance of the trained models. Results show that DRL agents improve energy efficiency by adapting to network conditions while minimally impacting the user experience. Additionally, we explore the trade-off between throughput and energy consumption offered by different DRL agent designs.

Provided herein are methods and systems for beyond line of sight control of autonomous vehicles including a wireless network having an Open RAN infrastructure in communication with a core network and a MEC infrastructure, a MEC orchestrator deployed in the Open RAN infrastructure and including MEC computing nodes and a ground control station, a catalog in communication with the orchestrator and including MEC function apps for operating the MEC infrastructure and/or the core network, and Open RAN apps for operating the Open RAN infrastructure, wherein the orchestrator is configured to process data from the MEC infrastructure, the core network, the open RAN infrastructure, or combinations thereof, and instantiate the MEC function apps in the MEC infrastructure and/or core network, instantiate the Open RAN apps in the Open RAN infrastructure, or combinations thereof to manage a plurality of vehicle functions and/or wireless network functions responsive to data from the vehicles.

Wireless Sensor Networks (WSNs) are pivotal in various applications, including precision agriculture, ecological surveillance, and the Internet of Things (IoT). However, energy limitations of battery-powered nodes are a critical challenge, necessitating optimization of energy efficiency for maximal network lifetime. Existing strategies like duty cycling and Wake-up Radio (WuR) technology have been employed to mitigate energy consumption and latency, but they present challenges in scenarios with sparse deployments and short communication ranges. This paper introduces and evaluates the performance of Unmanned Aerial Vehicle (UAV)-assisted mobile data collection for WuR-enabled WSNs through physical and simulated experiments. We propose two one-hop UAV-based data collection strategies: a naïve strategy, which follows a predetermined fixed path, and an adaptive strategy, which optimizes the collection route based on recorded metadata. Our evaluation includes multiple experiment categories, measuring collection reliability, collection cycle duration, successful data collection time (latency), and node awake time to infer network lifetime. Results indicate that the adaptive strategy outperforms the naïve strategy across all metrics. Furthermore, WuR-based scenarios demonstrate lower latency and considerably lower node awake time compared to duty cycle-based scenarios, leading to several orders of magnitude longer network lifetime. Remarkably, our results suggest that the use of WuR technology alone achieves unprecedented network lifetimes, regardless of whether data collection paths are optimized. This underscores the significance of WuR as the technology of choice for all energy critical WSN applications.