Abstract: According to an example, a signal space based navigation apparatus may receive traces of wireless signal observations and corresponding pedestrian deadreckoning (PDR) traces between different ones of the wireless signal observations from a plurality of user devices. A plurality of wireless clusters may be generated based on the traces of wireless signal observations. A PDR displacement vector may be generated between two or more of the wireless clusters based on the two or more of the wireless clusters and a subset of the PDR traces corresponding to the two or more of the wireless clusters. A sensing map may be generated based on the plurality of wireless clusters and a plurality of PDR displacement vectors including the PDR displacement vector.

Abstract: A first set of subcarriers having signal-to-noise ratio (SNR) values that exceed a target SNR value is identified at a transmitter. A second set of subcarriers having SNR values below the target SNR value is identified. Power is iteratively reallocated from the first and second set of subcarriers to a third set of subcarriers having SNR values below the target value but closer to the target SNR value than the second set of subcarriers.

Abstract: A set of antennas is selected from a plurality of antennas for transmitting data streams on a plurality of subcarriers based on channel state information of a communications channel between the plurality of antennas and a plurality of receive antennas at a client device, a number of the data streams to be transmitted, and a channel coherence time of the communications channel. A size of the set is equal to or greater than the number of data streams.

Abstract: A method for power management of a mobile device. The method includes evaluating content of a plurality of applications received at a mobile device operated by a user and determining latency information for each of the plurality of applications. The method further includes dynamically determining a priority of the plurality of applications based on the latency information for each application, and dynamically adjusting the mobile device between at least two wireless power modes based on the priority of the plurality of applications.

Abstract: A signal transmitted from a mobile device is received at a device. A change in angle-of-arrival (AoA) of the signal is computed as the mobile device moves from a first location to a second location. A first distance and a second distance of the mobile from the device corresponding to the first and second locations, respectively are computed. A displacement of the mobile device from the first location to the second location is computed based on mobility information provided by a sensor of the mobile device. A change in an angle of direct path (ANDP) of the signal is computed based on the first distance, the second distance, and the displacement. The ANDP is determined based on a comparison of the change in AoA to the change in ANDP.

Abstract: A first energy of a signal received from a mobile device is computed at a device based on information available at a physical (PHY) layer of the mobile device. A second energy of the received signal is computed. A path-loss exponent of the received signal is computed based on a line-of-sight (LoS) factor (lfactor) of the signal. A distance of the mobile device to the device is computed based on the first energy, the second energy, and the path-loss exponent of the received signal.

Abstract: A signal transmitted from a mobile device is received at an antenna array of a device. Motion information of the mobile device is received from a sensor of the mobile device. A change in the AoA of the signal is computed when the mobile device moves from a first position to a second position. The location of the mobile device relative to the antenna array is determined based on the change in AoA of the signal and the motion information of the mobile device.

Abstract: Systems and methods for unsupervised indoor localization are provided. Sensor data obtained from a device carried by a user can be used to simultaneously estimate the indoor location of a user and identify landmarks within the indoor environment based on their signatures. The landmarks can be used to reset the location estimate of the user, and the location estimate of the user can be used to improve the learned location of the landmarks. This recursive process leads to excellent accuracy in indoor localization. Systems and methods for estimating the heading direction of a user are also provided. Sensor data obtained from the user can be used to analyze the forces acting on the user in order to give an accurate heading direction estimate.