Abstract: A discovery measurement timing configuration (DMTC) window reservation signal is disclosed for new radio (NR) shared spectrum (NR-SS) networks. The DMTC window is defined providing a reserved location for transmission of essential control signaling and potentially high priority traffic that may be transmitted in a prioritized manner. Access within the DMTC window may be provided with a clear channel assessment (CCA) exempt transmission (CET) option or a non-CET option. When the CET option is used, the reservation duration and period may be conveyed through the channel reservation signal. User equipments (UEs) that detect the reservation signal may re-transmit with added offset to inform neighboring base station that may not be within range of the serving base station. Base station that receive either the transmitted or re-transmitted reservation signal will refrain from communications that may interfere with the reception at the UEs.

Abstract: Disclosed are techniques for determining a position of a user equipment (UE). A differential round-trip-time (RTT) based positioning procedure is proposed to determine the UE position. In this technique, the UE position is determined based on the differences of the RTTs between the UE and a plurality of base stations. The differential RTT based positioning procedure has much looser inter-gNodeB timing synchronization requirements than the OTDOA technique and also has much looser group delay requirements than traditional RTT procedures.

Abstract: Methods, systems, and devices for wireless communication are described. After receiving a transmission, a user equipment (UE) may store status information for the transmission and send an acknowledgment message that includes a signature. When a base station receives an acknowledgment message with a signature, it may transmit a subsequent downlink grant that includes the same signature. The signature included in a downlink grant may enable the UE to retrieve a stored status and decode the next data transmission based on the retrieved status. That is, the base station may use the signature in the grant to indicate which acknowledgment the grant is based on.

Abstract: A method of wireless communication by a user equipment (UE) receives a machine learning model from a base station. The UE reports, to the base station, a machine learning processing capability. The UE also transmits, to the base station, gradient updates or weight updates to the machine learning model. A base station transmits a machine learning model to a number of UEs. The base station receives, from each of the number of UEs, a machine learning processing capability report. The base station groups a number of UEs in accordance with the machine learning processing capability reports, to receive gradient updates to the machine learning model.

Abstract: Disclosed are techniques for determining a position of a user equipment (UE). A differential round-trip-time (RTT) based positioning procedure is proposed to determine the UE position. In this technique, the UE position is determined based on the differences of the RTTs between the UE and a plurality of base stations. The differential RTT based positioning procedure has much looser inter-gNodeB timing synchronization requirements than the OTDOA technique and also has much looser group delay requirements than traditional RTT procedures.

Abstract: In an aspect, a radar controller determines a radar slot format that configures transmission of a reference radar signal on a first symbol over a first link from a first base station to a second base station followed by at least one target radar signal on at least one second symbol over at least one second link from the first base station to the second base station, and transmits an indication of the radar slot format to the first base station and the second base station. The first base station transmits, and the second base station receives, the reference radar signal and the at least one target radar signal in accordance with the radar slot format.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may receive a reporting configuration that indicates one or more reporting conditions, wherein the reporting configuration further indicates that, based at least in part on the one or more reporting conditions being satisfied, the client device is to report an update associated with a machine learning component. The client device may transmit the update associated with the machine learning component to the server device based at least in part on whether the one or more reporting conditions are satisfied. Numerous other aspects are provided.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may determine a feedback associated with a machine learning component based at least in part on applying the machine learning component. Accordingly, the client device may transmit a quantized value based at least in part on the feedback. The quantized value is determined based at least in part on distances between the feedback and a non-uniform set of quantized digits. Numerous other aspects are provided.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may determine a feedback associated with a machine learning component based at least in part on applying the machine learning component. Accordingly, the client device may transmit a quantized value based at least in part on the feedback. The quantized value is determined using randomization with probabilities based at least in part on respective distances between one or more values of the feedback and a plurality of quantized digits. Numerous other aspects are provided.

Abstract: A method of wireless communication, by a user equipment (UE) includes receiving, from a base station, a jointly trained artificial neural network. The method also includes calculating a value representing at least one of (1) a gradient estimate for a weight of the jointly trained artificial neural network, or (2) the weight of the jointly trained artificial neural network. The method further includes expanding the value into a numerical system with base N into a plurality of digits. The method also includes determining a number and/or a location of the plurality of digits to transmit based on a deterministic task assignment rule received from the base station or a probabilistic task assignment rule. The method further includes transmitting the determined number and/or the determined location of the plurality of digits to the base station.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may receive, using at least one lower layer of a wireless communication protocol stack, a machine learning component from a server device. The client device may transmit, to the server device and using the at least one lower layer, an update associated with the machine learning component, wherein transmitting the update comprises transmitting a plurality of transport blocks. Numerous other aspects are described.

Abstract: A method of wireless communication performed by a first transmission device incudes receiving a first set of observations from a first receiving device based on transmissions from the first transmission device to the first receiving device at a previous time slot of a fixed contention-based spectrum sharing system. The first transmission device shares a spectrum with a second transmission device. The method also includes measuring an energy level from an ongoing data transmission of the second transmission device at a current time slot. The method further includes generating, at an artificial neural network of the first transmission device, at least one of a transmission determination, a set of transmission parameters, or a combination thereof, based on a second set of observations, the energy level, and a counter.

Abstract: A base station may precode a first reference signal using a first precoder (P1) and at least one second reference signal using at least one second precoder (P2). At least two UEs may measure the first reference signal and the at least one second reference signal, and transmit channel state feedback based on the measuring (m11 and m12 for a first UE, and m21 and m22 for a second UE). The channel state feedback may comprise channel state information compressed by at least one neural network. The base station may derive a higher-level precoder (W) for multiple-input multiple-output (MIMO) downlink transmission to the at least two UEs.

Abstract: Methods, systems, and devices for wireless communication are described. A user equipment (UE) may transmit a precoded reference signal to prevent interference with an incoming signal from a base station. The UE may determine a precoder for the reference signal based at least in part on a whitening matrix. In some examples, the UE may determine the precoder based at least in part on the whitening matrix and an effective channel between the base station and the UE. The precoded reference signal may be used by devices in other wireless communications systems to avoid interfering with a signal being received by the UE. In some examples, the UE may alternatively or additionally transmit an indication of the number of additional layers of the link between the UE and the base station that may be nulled out for a second link.

Abstract: A medium reservation framework is disclosed for coexistence of coordinated and uncoordinated wireless networks in licensed spectrum and shared spectrum. The proposed medium reservation framework organically takes into account operating regimes with different numbers of Tx/Rx antennas per node, provides flexibility in trading off medium contention aggressiveness and power saving, leverages the inherent synchronization nature of NR, and covers both coordinated and uncoordinated operation scenarios. Various aspects of the medium reservation framework may be centered on one or more combinations of some basic building blocks, such as an operation grid, synchronization signals, and reservation messages, such as a reservation request signal (RRQ) and one or more a reservation response signals (RRS).

Abstract: Methods, systems, and devices for wireless communications are described. A configuration for a reference signal used to determine a non-linear behavior of transmission components at a transmitting device may be determined. The configuration for the reference signal may be determined based on signaling transmitted by the transmitting device, signaling transmitted by a device that receives the reference signal, or both. Additionally, or alternatively, the configuration for the reference signal may be determined based on a configuration of other signals transmitted by the transmitting device prior to or concurrently with the transmission of the reference signal. The determined configuration may be used to generate and transmit the reference signal or to determine a configuration of a received reference signal. In both cases, a non-linear response of transmission components at the transmitting device may be determined based on the reference signal.

Abstract: A method of wireless communication performed by a first transmission device includes determining a set of spectrum sharing parameters based on sensing performed during a sensing period of a current time slot in a fixed contention based spectrum sharing system. The first transmission device shares a spectrum with a second transmission device. The method also includes determining, at a first artificial neural network of the first transmission device, a transmission device action, and/or a transmission parameter in response to receiving the set of spectrum sharing parameters. The method further includes transmitting, from the first transmission device, to a first receiving device during a data transmission phase of the current time slot based on the transmission device action and/or the transmission parameter.

Abstract: A method of training an artificial neural network (ANN), receives, from a base station, signal information for a radio frequency signal between the base station and a user equipment (UE). The artificial neural network is trained to determine a location of the UE and to map the environment based on the received signal information and in the absence of labeled data.

Abstract: A communications transceiver system that employs self-interference mitigation techniques can detect static objects by using techniques that introduce angular or Doppler diversity. This can include moving Tx and Rx antennas and/or performing beam sweeping. When processing RF sensing data from reflected RF signals, self-interference mitigation techniques can be used and compensation can be made for the movement and/or beam sweeping to allow for both self-interference mitigation and detection of static objects.

Abstract: Techniques are provide for neural network based positioning of a mobile device. An example method for determining a line of sight delay, an angle of arrival, or an angle of departure value includes receiving reference signal information, determining one or more windowed channel impulse responses based on the reference signal information and one or more window functions, processing the one or more windowed channel impulse responses with a neural network, and determining an output of the neural network.