A wireless network can generate candidate signal configurations for physical transmissions to or from a user equipment (UE) in a radio environment. The generation of candidate signal configurations can be performed using a first neural network that is associated with the UE. These signal configurations can then be evaluated using a second neural network that is associated with the radio environment. The second neural network can be trained using measurements from previous physical transmissions in the radio environment. The trained second neural network generates a reward value that is associated with the candidate signal configurations. The first neural network is then trained using the reward values from the second neural network to produce improved candidate signal configurations. When a signal configuration that produces a suitable reward value is generated, this signal configuration can be used for the physical transmission in the radio environment.

A wireless network can generate candidate signal configurations for physical transmissions to or from a user equipment (UE) in a radio environment. The generation of candidate signal configurations can be performed using a first neural network that is associated with the UE. These signal configurations can then be evaluated using a second neural network that is associated with the radio environment. The second neural network can be trained using measurements from previous physical transmissions in the radio environment. The trained second neural network generates a reward value that is associated with the candidate signal configurations. The first neural network is then trained using the reward values from the second neural network to produce improved candidate signal configurations. When a signal configuration that produces a suitable reward value is generated, this signal configuration can be used for the physical transmission in the radio environment.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform a measurement operation to attain multiple measurements to report to a base station. The measurements may correspond to a first number of bits if reported. The UE may compress the measurements using an encoder neural network (NN) to obtain an encoder output indicating the measurements. This encoder output may include a second number of bits that is less than the first number of bits. The UE may report the encoder output to the base station in this compressed form. At the base station, the encoder output may be decompressed according to a decoder NN. Once the base station decompresses the encoder output, the UE and base station may communicate according to the measurements determined from the decompression. In some cases, the base station may perform load redistribution based on the measurements.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform a measurement operation to attain multiple measurements to report to a base station. The measurements may correspond to a first number of bits if reported. The UE may compress the measurements using an encoder neural network (NN) to obtain an encoder output indicating the measurements. This encoder output may include a second number of bits that is less than the first number of bits. The UE may report the encoder output to the base station in this compressed form. At the base station, the encoder output may be decompressed according to a decoder NN. Once the base station decompresses the encoder output, the UE and base station may communicate according to the measurements determined from the decompression. In some cases, the base station may perform load redistribution based on the measurements.

Systems and methods for confirming the presence of a person or asset for a given purpose, and recording this information in a distributed ledger. The distributed ledger records and confirms presence indicia in connection with a transaction said facilitates remote and/or automated signatures. The systems and methods detect the presence of one or more humans and/or computing devices at a specific location at the time of a transaction, and contemporaneously recording information concerning the transaction in a distributed ledger. Presence can be determined using network presence sensing (NPS), other types of sensors, or the combination of NPS with other sensors.

Systems and methods for confirming the presence of a person or asset for a given purpose, and recording this information in a distributed ledger. The distributed ledger records and confirms presence indicia in connection with a transaction said facilitates remote and/or automated signatures. The systems and methods detect the presence of one or more humans and/or computing devices at a specific location at the time of a transaction, and contemporaneously recording information concerning the transaction in a distributed ledger. Presence can be determined using network presence sensing (NPS), other types of sensors, or the combination of NPS with other sensors.

Implementations relate to selection of a physics-specific model for determination of characteristics of radio frequency signal propagation. In some implementations, a method includes receiving a plurality of first propagation characteristics of a radio frequency (RF) signal, determining a feature vector based on the first propagation characteristics, inputting the feature vector to a machine-learning meta-model, and executing the machine learning meta-model to select a particular physics-specific model from multiple physics-specific models, where each of the physics-specific models is for a different RF signal propagation environment. The feature vector is input to the particular physics-specific model, and the particular physics-specific model is executed to output an estimate of one or more second propagation characteristics of the RF signal based on the feature vector.

Embodiments include methods, performed by a user equipment (UE), for uplink (UL) transmission in a wireless network. Such methods include receiving a configuration associated with an UL transmission to a network node in the wireless network. Such methods include, for each of a plurality of combinations of the UE's available antennas and transmitters, determining metrics based on the UE performing the UL transmission using the particular combination. The metrics include a quality metric associated with reception of the UL transmission by the network node, and a UE energy consumption metric. Such methods include selecting one of the plurality of combinations of the available antennas and transmitters based on the respective quality metrics and the respective UE power consumption metrics, and performing the UL transmission according to the configuration and using the selected combination of available antennas and transmitters. Other embodiments include UEs configured to perform such methods.

A terminal according to an aspect of the present disclosure includes a receiving section that receives a pathloss reference signal, and a control section that calculates a pathloss for transmission power control on the basis of layer 1 (L1)-reference signal received power (RSRP) for the pathloss reference signal when the pathloss reference signal is updated by a media access control-control element (MAC CE), and a measurement condition is met. According to an aspect of the present disclosure, the pathloss can be appropriately calculated.

The present disclosure relates to a method for operating a terminal and a base station in a wireless communication system and an apparatus for supporting the same. In an embodiment of the present disclosure, a method for operating a terminal in a wireless communication system may include: transmitting a first message including information related to learning; receiving a second message including configuration information for learning; transmitting an uplink reference signal; and transmitting channel information related to a downlink channel measured based on a downlink reference signal.