High-accuracy locating systems and methods are used for determining successful caregiver rounding, monitoring whether housekeepers have properly cleaned patient beds, or determining whether patients have ambulated sufficient distances during recovery. Patient beds having at least two locating tags are used for establishing patient care zones around the patient beds. Locating anchors and equipment tags are moved around a patient room to determine optimum locating anchor placement within the patient room based on signal quality values. A locating tag on a patient bed switches roles to operate as a locating anchor in response to the patient bed becoming stationary. A locating tag has a digital compass which is used to determine a field of good ranging relative to a front of a caregiver wearing the locating tag.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learning estimation networks in a communications system. One of the methods includes: processing first information with ground truth information to generate a first RF signal by altering the first information by channel impairment having at least one channel effect, using a receiver to process the first RF signal to generate second information, training a machine-learning estimation network based on a network architecture, the second information, and the ground truth information, receiving by the receiver a second RF signal transmitted through a communication channel including the at least one channel effect, inferring by the trained estimation network the receiver to estimate an offset of the second RF signal caused by the at least one channel effect, and correcting the offset of the RF signal with the estimated offset to obtain a recovered RF signal.

A processor-implemented method is presented. The method includes receiving an input sequence comprising a group of channel dynamics observations for a wireless communication channel. Each channel dynamics observation may correspond to a timing of a group of timings. The method also includes determining, via a recurrent neural network (RNN), a residual at each of the group of timings based on the group of channel dynamics observations. The method further includes updating Kalman filter (KF) parameters based on the residual and estimating, via the KF, a channel state based on the updated KF parameters.

Example embodiments of the present disclosure relate to compensating reference signals to train a neural network. According to embodiments of the present disclosure, a solution for compensating reference signals to train a neural network is proposed. A second device transmits information to a first device. The information is used to trigger generation and transmission of compensated training reference signals. The information indicates a set of receiving ports at the first device. The first device compensates the training reference signals based on downlink channel information and transmits the compensated signals to the second device via one or more transmitting ports. In this way, CSI can be obtained more accurately. Further, a processing model at the second device can be improved.

Example embodiments of the present disclosure relate to compensating reference signals to train a neural network. According to embodiments of the present disclosure, a solution for compensating reference signals to train a neural network is proposed. A second device transmits information to a first device. The information is used to trigger generation and transmission of compensated training reference signals. The information indicates a set of receiving ports at the first device. The first device compensates the training reference signals based on downlink channel information and transmits the compensated signals to the second device via one or more transmitting ports. In this way, CSI can be obtained more accurately. Further, a processing model at the second device can be improved.

A large-scale radio frequency signal collection and processing system comprising a plurality of sensor systems mounted on a plurality of collection platforms that integrates a plurality of overlapping datasets with differing characteristics (e.g., different resolutions, different view angles, different heights, different time periods, unrelated types of data) to generate an enriched dataset or datasets using a variety of processing techniques (e.g., statistical analysis, signal processing, image processing) that allows for more comprehensive analysis of the radio frequency signal landscape than would be possible using any of the datasets individually, or in combination but without such integration.

A system for an automated ML-based design of a wireless network. The system includes a processor of a design server node connected to at least one local, edge, or cloud server node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire aerial 3-D mapping data of a target area from an unmanned aircraft system (UAS) flying over the target area; acquire surface 3-D mapping data from a ground robotic crawler; parse the 3-D mapping data to derive an at least one feature vector; provide the at least one feature vector to a machine learning (ML) module residing on the at least one local, edge, or cloud server node for generating a predictive model of a wireless network for some or all of the target area; receive outputs of the predictive model; and generate a wireless network design for the some or all of the target area based on the predictive outputs.

A method, system, and apparatus for emulating impairments in a communication system.

Certain aspects of the present disclosure provide techniques for providing a probabilistic feedback parameter. One example method for wireless communication may be performed by a first wireless node. The method generally includes generating a feedback message indicating a probabilistic estimate comprising a plurality of feedback parameter values and a plurality of value probabilities, wherein each value probability is associated with one feedback parameter value of the plurality of feedback parameter values, and transmitting the feedback message to a second wireless node.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitter node may predict a future state associated with a wireless channel at a future time instance using a machine learning model, wherein the future state is predicted based at least in part on one or more of weights associated with the machine learning model, a current state associated with the wireless channel, or one or more previous states associated with the wireless channel. The transmitter node may select one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel. The transmitter node may perform the transmission using the one or more parameters. Numerous other aspects are described.