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: Systems and techniques for information centric network (ICN) interworking are described herein. For example, a request may be received at a convergence layer of a node. Here, the request originates from an application on the node. A network protocol, from several available to the node, may be determined to transmit the request. The node then transmits the request via the selected network protocol.

Abstract: Systems and methods of beamforming and improving mmWave communications for drones are described. Multiple RF chains are used to adapt the main beam to track changes without the use of pilot signals. To reduce interference, interfering signal power is eliminated by optimizing a non-Gaussian measure to extract the interferers. The AoA of signals from a target drone on neighbouring drones and location of the neighbouring drones and base stations are used to independently corroborate the location reported by the target drone. The base station provides additional synchronization signals below 6 GHz and restricts the search/measurement space in the vertical direction. The inherent sparse structure above 6 GHz is exploited by applying different beamformers on a sounding signal and estimating the AoA and impulse response. Variations of fully digital and hybrid beamforming architectures for multi-cell DL sync and CRS measurement are described.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: Systems, methods, computer program products, and apparatuses to determine, by a neural network based on training data related to wireless signals exchanged by a device and a plurality of wireless access points in an environment, a respective distance between the device and each wireless access point, receive location data related to a respective location of each wireless access point of the plurality of wireless access points, determine a geometric cost of the neural network based on a geometric cost function, the respective distances, and the received location data, and train a plurality of values of the neural network based on a backpropagation and the determined geometric cost.

Abstract: A communication circuit arrangement includes a signal path circuit to estimate, using a first kernel dimension filter and a first delay tap dimension filter, a first interference signal produced by a first amplifier. The signal path circuit further estimates, using a second kernel dimension filter and a second delay tap dimension filter, a second interference signal produced by a second amplifier. A cancellation circuit of the communication circuit arrangement may subtract a combination of the first interference signal and the second interference signal from a received signal to obtain a filtered signal, and one or more filter adaptation circuits may alternate between a kernel update phase and a delay update phase to update the first kernel dimension filter and the second kernel dimension filter during the kernel update phase, and update the first delay tap dimension filter and the second delay tap dimension filter during the delay update phase.

Abstract: A wireless communication device includes a processor configured to select an offload processing task for performance by an edge computing device; cause a baseband modem to establish a direct wireless connection between the wireless communication device and the edge computing device; cause the baseband modem to send first data to the edge computing device via the direct wireless connection; and receive second data from the edge computing device, wherein the second data comprise a result of the offload processing task performed on the first data. The edge computing device includes a processor configured to receive, from a user device, offloaded data to be processed according to an offload processing task; execute the offload processing task on the offloaded data; and cause the radio via the interface to wirelessly send a result of the executed offload processing task via a direct wireless connection with the user device.

Abstract: A communication circuit arrangement includes a first kernel dimension filter circuit configured to apply a first kernel dimension filter to a first input signal to estimate a first kernel dimension interference signal from a first amplifier, a second kernel dimension filter circuit configured to apply a second kernel dimension filter to a second input signal to estimate a second kernel dimension interference signal from a second amplifier, a joint delay tap dimension filter configured to apply a joint delay tap dimension filter to a combination of the first kernel dimension interference signal and the second kernel dimension interference signal to obtain an estimated joint interference signal, and a cancelation circuit configured to remove the estimated joint interference signal from a received signal to obtain a clean signal.

Abstract: Systems and methods of using machine-learning to improve communications across different networks are described. A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model. A neural network is trained to avoid interference using rewards associated with reduced interference or retransmission levels in each network or improved throughput at the CIRN node. A machine-learning scheduling algorithm determines a relay mode of the CIRN node for source and destination node transmissions. The scheduling algorithm is based on the probability of successful transmission between the source and destination nodes multiplied by a collaboration score for successful transmission and the probability of unsuccessful transmission of the particular packet multiplied by a collaboration score for unsuccessful transmission.

Abstract: An apparatus and a method for performing positioning using a Global Navigation Satellite System (GNSS) with a state machine based localization engine are provided. When the apparatus receives GNSS signals, the apparatus provides the localization engine to process the GNSS signals, and determines, based on a GNSS status and a position-velocity-time (PVT) status, a state of the localization engine. Specifically, the state of the localization engine is switchable between at least 3 states, including a dead reckoning state, a tightly coupling state, and a loosely coupling state. Once the state is determined, the localization engine may determine a local accuracy status based on the state of the localization engine. Thus, a downstream module on the apparatus may use the local accuracy status to perform a corresponding downstream action.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: Systems and techniques for machine generation of content names in an information centric network (ICN) are described herein. For example, a node may obtain content. An inference engine may be invoked to produce a name for the content. Once the content is named, the node may respond to an interest packet that includes the name of the content. The response is a data packet that includes the content.

Abstract: A Global Navigation Satellite System (GNSS) receiver for performing GNSS outlier detection and rejection is provided. When the GNSS receiver receives GNSS signals from satellites in the GNSS, the GNSS receiver processes the GNSS signals to perform positioning. Then, the GNSS receiver sequentially performs a Doppler-pseudorange comparison, a Random Sampling Consensus (RANSAC) check for selected subsets of the satellites, and a history-based check for the satellites to determine a status of each satellites as an outlier or an inlier. Specifically, in the RANSAC check, the subsets of the satellites are selected using results of the Doppler-pseudorange comparison as inputs to filter the satellites, thus reducing the number of subsets needed for computation in the RANSAC check. The status of the satellites are recorded for the history-based check, which further exploits the correlations of outliers across time.

Abstract: Systems, methods, computer program products, and apparatuses to determine, by a neural network based on training data related to wireless signals exchanged by a device and a plurality of wireless access points in an environment, a respective distance between the device and each wireless access point, receive location data related to a respective location of each wireless access point of the plurality of wireless access points, determine a geometric cost of the neural network based on a geometric cost function, the respective distances, and the received location data, and train a plurality of values of the neural network based on a backpropagation and the determined geometric cost.

Abstract: An apparatus and a method for providing a global localization output are provided. When the apparatus receives navigation signals, the apparatus processes the signals to determine, based on a fixed earth-centered, earth-fixed (ECEF) reference pose of a reference point in an ECEF coordinate, a new ECEF pose, and to convert the fixed ECEF reference pose to an east-north-up (ENU) reference pose in an ENU coordinate. When the apparatus determines that a jump occurs in the new ECEF pose based on a pose change between the new ECEF pose and a previous ECEF pose, the apparatus calculates a reference shift of the ENU reference pose based on the pose change to absorb the jump in the ENU coordinate, and updates the ENU reference pose based on the reference shift. Thus, a new ENU local pose may be obtained using the ENU reference pose.

Abstract: A Global Navigation Satellite System (GNSS) receiver for performing GNSS outlier detection and rejection is provided. When the GNSS receiver receives GNSS signals from satellites in the GNSS, the GNSS receiver processes the GNSS signals to perform positioning. Then, the GNSS receiver sequentially performs a Doppler-pseudorange comparison, a Random Sampling Consensus (RANSAC) check for selected subsets of the satellites, and a history-based check for the satellites to determine a status of each satellites as an outlier or an inlier. Specifically, in the RANSAC check, the subsets of the satellites are selected using results of the Doppler-pseudorange comparison as inputs to filter the satellites, thus reducing the number of subsets needed for computation in the RANSAC check. The status of the satellites are recorded for the history-based check, which further exploits the correlations of outliers across time.

Abstract: An apparatus and a method for providing a global localization output are provided. When the apparatus receives navigation signals, the apparatus processes the signals to determine, based on a fixed earth-centered, earth-fixed (ECEF) reference pose of a reference point in an ECEF coordinate, a new ECEF pose, and to convert the fixed ECEF reference pose to an east-north-up (ENU) reference pose in an ENU coordinate. When the apparatus determines that a jump occurs in the new ECEF pose based on a pose change between the new ECEF pose and a previous ECEF pose, the apparatus calculates a reference shift of the ENU reference pose based on the pose change to absorb the jump in the ENU coordinate, and updates the ENU reference pose based on the reference shift. Thus, a new ENU local pose may be obtained using the ENU reference pose.

Abstract: An apparatus and a method for performing positioning using a Global Navigation Satellite System (GNSS) with a state machine based localization engine are provided. When the apparatus receives GNSS signals, the apparatus provides the localization engine to process the GNSS signals, and determines, based on a GNSS status and a position-velocity-time (PVT) status, a state of the localization engine. Specifically, the state of the localization engine is switchable between at least 3 states, including a dead reckoning state, a tightly coupling state, and a loosely coupling state. Once the state is determined, the localization engine may determine a local accuracy status based on the state of the localization engine. Thus, a downstream module on the apparatus may use the local accuracy status to perform a corresponding downstream action.

Abstract: Systems, methods, computer program products, and apparatuses to determine, by a neural network based on training data related to wireless signals exchanged by a device and a plurality of wireless access points in an environment, a respective distance between the device and each wireless access point, receive location data related to a respective location of each wireless access point of the plurality of wireless access points, determine a geometric cost of the neural network based on a geometric cost function, the respective distances, and the received location data, and train a plurality of values of the neural network based on a backpropagation and the determined geometric cost.