Abstract: The present disclosure discusses network energy savings (NES) machine learning (ML) models that predict NES parameters used to adjust control parameters of respective network nodes in a wireless network, wherein the NES parameters can be used by the respective network nodes to adjust their control parameters, such that the wireless network realizes or achieves NES as a whole. The wireless network is represented as a graph with heterogeneous vertices that represent corresponding network nodes and edges that represent connections between the network nodes. The NES ML model comprises a graph neural network (GNN) and a fully connected neural network (FCNN). The GNN may be a graph convolutional neural network or a graph attention network. The FCNN may be a multi-layer perceptron, a deep neural network, and/or some other type of neural network. Other embodiments may be described and/or claimed.

Abstract: The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained graph neural network model, the input data being indicative of a graph representation of a plurality of wireless communication devices, wherein the trained graph neural network model is configured to provide output data being indicative of a scheduled user set including the plurality of wireless communication devices; and instruct user scheduling of the plurality of wireless communication devices based on the output data of the trained graph neural network model.

Abstract: A computing node to implement an RL management entity in an NG wireless network includes a NIC and processing circuitry coupled to the NIC. The processing circuitry is configured to generate a plurality of network measurements for a corresponding plurality of network functions. The functions are configured as a plurality of ML models forming a multi-level hierarchy. Control signaling from an ML model of the plurality is decoded, the ML model being at a predetermined level (e.g., a lowest level) in the hierarchy. The control signaling is responsive to a corresponding network measurement and at least second control signaling from a second ML model at a level that is higher than the predetermined level. A plurality of reward functions is generated for training the ML models, based on the control signaling from the MLO model at the predetermined level in the multi-level hierarchy.

Abstract: The present disclosure provides connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm/model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and/or claimed.

Abstract: An imaging system that includes a camera mounted on an aerial platform, for example a balloon, allows a user to increase the longevity of the camera's battery by remote control. A user may capture imagery at a time scale of interest and desired power consumption by adjusting parameters for image capture by the camera. A user may adjust a time to capture an image, a time to capture a video, or a number of cycles per time period to capture one or more images as the aerial platform moves in a region of interest to change power consumption for imaging. The system also provides imaging alignment to account for unwanted movement of the aerial platform when moved in the region of interest. Additionally, a mounting device is provided that is simple and inexpensive, and that allows a camera to remain positioned in a desired position relative to the ground.

Abstract: An imaging system that includes a camera mounted on an aerial platform, for example a balloon, allows a user to increase the longevity of the camera's battery by remote control. A user may capture imagery at a time scale of interest and desired power consumption by adjusting parameters for image capture by the camera. A user may adjust a time to capture an image, a time to capture a video, or a number of cycles per time period to capture one or more images as the aerial platform moves in a region of interest to change power consumption for imaging. The system also provides imaging alignment to account for unwanted movement of the aerial platform when moved in the region of interest. Additionally, a mounting device is provided that is simple and inexpensive, and that allows a camera to remain positioned in a desired position relative to the ground.

Abstract: An imaging system that includes a camera mourned on an aerial platform, for example a balloon, allows a user to increase the longevity of the camera's battery by remote control. A user may capture imagery at a time scale of interest and desired power consumption by adjusting parameters for image capture by the camera. A user may adjust a time to capture an image, a time to capture a video, or a number of cycles per time period to capture one or more images as the aerial platform moves in a region of interest to change power consumption for imaging. The system also provides imaging alignment to account for unwanted movement of the aerial platform when moved in the region of interest. Additionally, a mounting device is provided that is simple and inexpensive, and that allows a camera to remain positioned in a desired position relative to the ground.

Abstract: Various methods and devices for positioning autonomous agents including verifying a reported agent location using physical attributes of the received signal; improving agent formation for iterative localization; selecting agents for distributed task sharing; intelligent beacon-placement for group localization; relative heading and orientation determination utilizing time of flight; and secure Instrument Landing System (ILS) implementation for unmanned agents.

Abstract: An imaging system that includes a camera mourned on an aerial platform, for example a balloon, allows a user to increase the longevity of the camera's battery by remote control. A user may capture imagery at a time scale of interest and desired power consumption by adjusting parameters for image capture by the camera. A user may adjust a time to capture an image, a time to capture a video, or a number of cycles per time period to capture one or more images as the aerial platform moves in a region of interest to change power consumption for imaging. The system also provides imaging alignment to account for unwanted movement of the aerial platform when moved in the region of interest. Additionally, a mounting device is provided that is simple and inexpensive, and that allows a camera to remain positioned in a desired position relative to the ground.

Abstract: A system comprises an aerial imaging platform configured to rise to a height above ground. An apparatus allows an entity to move the aerial platform in a desired direction. The aerial platform includes a camera positioned to capture images of the ground. The camera includes a position sensor. A user/entity may move the aerial platform over a region to be imaged. The system includes a device that may be carried by the user/entity. The device receives information about a region to be imaged and a field of vision of the camera, determines a first path, and provides information on the first path to the user/entity. As the user/entity moves the aerial platform along the first path, the device receives data from the camera position sensor and determines a second path. The user/entity may then move the aerial platform along the second path to capture unimaged areas of the region.

Abstract: A system and method are disclosed that may provide an accurate estimate of the angle of arrival (AoA) of a wireless signal received by a device. The received wireless signal may include a plurality of signal components associated with a number of different arrival paths. The device may generate a weighted signal, including a plurality of weighted signal components, by multiplying the plurality of signal components of the received wireless signal with a set of weighting values. The device may identify one or more of the weighted signal components associated with a first arrival path to the device, determine phase information of the one or more identified weighted signal components, and then determine the angle of arrival based, at least in part, on the determined phase information.

Abstract: A system and method are disclosed that may provide an accurate estimate of the angle of arrival (AoA) of a wireless signal received by a device. The received wireless signal may include a plurality of signal components associated with a number of different arrival paths. The device may generate a weighted signal, including a plurality of weighted signal components, by multiplying the plurality of signal components of the received wireless signal with a set of weighting values. The device may identify one or more of the weighted signal components associated with a first arrival path to the device, determine phase information of the one or more identified weighted signal components, and then determine the angle of arrival based, at least in part, on the determined phase information.