Abstract: Described herein are systems, methods and devices for implementing a temperature-aware, multi-antenna scheduler that cools mmWave devices by preventing heat buildup via switching or distributing a data stream to other redundant antennas, allowing for dissipation of heat as well as providing reliable connectivity.

Abstract: The disclosed system and methodology enable coexistence of networking and sensing on next-generation millimeter-wave (mmWave) picocells for traffic monitoring and pedestrian safety at intersections in all weather conditions. Existing wireless signal-based object detection systems suffer from limited resolution, and their outputs may not provide sufficient discriminatory information in complex scenes, such as traffic intersections. The disclosed system uses 5G picocells, which operate at mmWave frequency bands and provide higher data rates and higher sensing resolution than traditional wireless technology. It is difficult to run sensing applications and data transfer simultaneously on mmWave devices due to potential interference. MmWave devices are vulnerable to weak reflectivity and specularity challenges which may result in loss of information about objects and pedestrians.

Abstract: Methodology and corresponding apparatus pertain to human sleep posture monitoring, using a wireless signal-based monitoring system leveraging millimeter-wave technology. A software-only human sleep posture monitoring solution based on millimeter-wave (mmWave) wireless-based solutions enables fine-grained posture monitoring under no light without being privacy-invasive. In zero visibility, body joint information and changes can be extracted directly from mmWave imaging using improved capabilities for extracting human sleep posture data from millimeter-wave wireless systems. A single-person sleep posture monitoring system leverages signal processing and deep learning models to enable fine-grained monitoring continuously and non-intrusively with commodity (i.e., generally available) mmWave devices. The system directly predicts joint locations from reflected mmWave signals by learning the hidden association between them from thousands of data samples.

Abstract: Methodology and corresponding apparatus pertains to sleep disruption monitoring, including use of a wireless signal-based monitoring system leveraging millimeter-wave technology. A software-only sleep disruption monitoring solution can be based on millimeter-wave (mmWave) wireless-based solutions which leverage cross-correlation between successive mmWave reflected signals and a Hidden Markov Model (HMM) to identify respective sleep (rest) and disruptions (toss-turn) periods. A toss-turn detector module can identify sudden movements during sleep from mmWave wireless signals and classify the sleeping period into the two states: Rest or toss-turn. Whenever mmWave transceivers (such as included in 5G-and-beyond devices) are implemented as access points, in mass privacy non-invasive sleep disruption monitoring can be provided for consumers at home.

Abstract: Described herein are systems, methods and devices for implementing a temperature-aware, multi-antenna scheduler that cools mmWave devices by preventing heat buildup via switching or distributing a data stream to other redundant antennas, allowing for dissipation of heat as well as providing reliable connectivity.

Abstract: An antenna assembly includes an antenna and a heatsink. The antenna may be configured to support radio communications and generate heat, and may include a forward antenna surface configured to transmit or receive communications signals and a rear antenna surface that is affixed to a substrate. The heatsink structure may be positioned to be within a forward electromagnetic field that is emitted from the forward antenna surface and away from the rear antenna surface. The heatsink structure may be configured to perform a convection operation between the antenna and a fluid to perform thermal dissipation of the heat from the antenna.

Abstract: Described herein are systems, methods and devices for implementing a temperature-aware, multi-antenna scheduler that cools mmWave devices by preventing heat buildup via switching or distributing a data stream to other redundant antennas, allowing for dissipation of heat as well as providing reliable connectivity.

Abstract: System and methodology are disclosed for approximating traditional SAR imaging on mobile mmWave devices. The presently disclosed technology enables human-perceptible and machine-readable shape generation and classification of hidden objects on mobile mmWave devices. The resulting system and corresponding methodology are capable of imaging through obstructions, like clothing, and under low visibility conditions. To this end, the presently disclosed technology incorporates a machine-learning model to recover the high-spatial frequencies in the object to reconstruct an accurate 2D shape and predict its 3D features and category. The technology is disclosed in particular for security applications, but the broader model disclosed is adaptable to different applications, even with limited training samples.

Abstract: An integrated system and associated methodology allow performing at-home spirometry tests using smart devices which leverage the built-in millimeter-wave (mmWave) technology. Implementations leverage deep learning with some embodiments including a combination of mmWave signal processing and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory Network) architecture. Smartphone devices are transformed into reliable at-home spirometers by having a user hold a device in front of their mouth, inhale their full lung volume, and forcibly exhale until the entire volume is expelled, as in typical spirometry tests. Airflow on the device surface creates tiny vibrations which directly affect the phase of the reflected mmWave signal from nearby objects. Stronger airflow yields larger vibration and higher phase change. The technology analyzes tiny vibrations created by airflow on the device surface and combines wireless signal processing with deep learning.

Abstract: Integrated methodologies and associated devices are provided for performing at-home spirometry tests using improved analysis based on deep learning technology. A deep residual decoder network is established for patient lung function monitoring. Curve learning is conducted with such decoder network based on a patient database of lung function measures. An existing spirometry device may be used for outputting at least one key spirometry indicator and obtaining airflow data from a patient with incident airflow of a sample exhalation resulting in at least one key spirometry indicator from the spirometry device for the patient user. The decoder network processes such spirometry indicator for the patient user to produce flow-volume graphing to extend the capability of the spirometry device for finer-grained, long-term lung function monitoring.

Abstract: A reconfigurable antenna apparatus may include an antenna assembly and control circuitry. The antenna assembly may include an antenna patch array having a plurality of antenna patches and a switch array having plurality of switches. Each switch of the plurality switches may be electronically controllable to transition between states including a conducting state and a non-conducting state. Each switch may be electrically connected between two of the antenna patches of the antenna array. The control circuitry may be configured to control the states of the switches of the switch array to operate the antenna patch array in a first communications mode at a first wavelength, and control the states of the switches of the switch array to operate the antenna patch array in a second communications mode at a second wavelength.

Abstract: Described herein are systems, methods and devices for implementing a temperature-aware, multi-antenna scheduler that cools mmWave devices by preventing heat buildup via switching or distributing a data stream to other redundant antennas, allowing for dissipation of heat as well as providing reliable connectivity.

Abstract: An antenna assembly includes an antenna and a heatsink. The antenna may be configured to support radio communications and generate heat, and may include a forward antenna surface configured to transmit or receive communications signals and a rear antenna surface that is affixed to a substrate. The heatsink structure may be positioned to be within a forward electromagnetic field that is emitted from the forward antenna surface and away from the rear antenna surface. The heatsink structure may be configured to perform a convection operation between the antenna and a fluid to perform thermal dissipation of the heat from the antenna.

Abstract: A reconfigurable antenna apparatus may include an antenna assembly and control circuitry. The antenna assembly may include an antenna patch array having a plurality of antenna patches and a switch array having plurality of switches. Each switch of the plurality switches may be electronically controllable to transition between states including a conducting state and a non-conducting state. Each switch may be electrically connected between two of the antenna patches of the antenna array. The control circuitry may be configured to control the states of the switches of the switch array to operate the antenna patch array in a first communications mode at a first wavelength, and control the states of the switches of the switch array to operate the antenna patch array in a second communications mode at a second wavelength.

Abstract: In some examples, a system can include a processing resource and a memory resource. The memory resource can store machine readable instructions to cause the processing resource to: (1) collect feedback from a physical (PHY)-layer of a client at a plurality of access points, before the client is associated with any one of the plurality of access points and (2) select an access point using a client channel correlation value calculated from the PHY-layer feedback, wherein the selected access point serves the client and a client group concurrently using precoding.

Abstract: In some examples, a computing device may comprise a processing resource and a memory resource storing machine-readable instructions to cause the processing resource to proactively identify a line-of-sight (LOS) blockage of a network signal transmitted on a millimeter-wave link, and reroute network traffic from the millimeter-wave link to a wireless local area network (WLAN) transmission channel in response to identifying the LOS blockage of the network signal.

Abstract: An example communications device includes communications circuitry and control circuitry. The communications circuitry may wirelessly communicate with client devices. The control circuitry may determine signal-to-interference-plus-noise ratios (SINRs) for the client devices based on compressed client-side channel state information received from the client devices. The control circuitry may assign the client devices to multi-user-multiple-input-multiple-output (MU-MIMO) groups based on the SINRs.

Abstract: Example system may comprise a network controller including a processing resource and instructions executable by the processing resource to: receive, from a first access point in a network, properties of a first millimeter Wave (mmWave) signal path between the first access point and a computing device associated with the first access point; predict, utilizing the properties of the first mmWave signal path, properties of a second mmWave signal path between a second access point in the network and the computing device; predict, utilizing the predicted properties of the second mmWave path, a beam strength for the second access point; and assign a rank to the second access point according to the beam strength.

Abstract: An example system may comprise a millimeter wave (mmWave) transmitting device including instructions executable to: identify a plurality of mmWave signal transmission paths from the mmWave transmitting device to a receiving device; collect a channel measurement for each beam of a first portion of beams available at the mmWave transmitting device at each signal transmission path of the plurality of mmWave signal transmission paths; determine a plurality of properties of each signal transmission path of the plurality of mmWave signal transmission paths utilizing the corresponding channel measurements; predict, based on the plurality of properties, a signal metric for a second portion of the beams; and select, based on the predicted signal metric, a beam from the beams to be utilized to transmit a signal to the receiving device.

Abstract: Example system may comprise a network controller including a processing resource and instructions executable by the processing resource to: receive, from a first access point in a network, properties of a first millimeter Wave (mmWave) signal path between the first access point and a computing device associated with the first access point; predict, utilizing the properties of the first mmWave signal path, properties of a second mmWave signal path between a second access point in the network and the computing device; predict, utilizing the predicted properties of the second mmWave path, a beam strength for the second access point; and assign a rank to the second access point according to the beam strength.