Abstract:
A wireless user equipment (UE) may receive a downlink transmission from a base station in a first subframe of a first subframe configuration, and determine that a neighboring base station is operating according to a second subframe configuration. The UE may modify interference operations for the downlink transmission in the first subframe based on the determination to account for the neighboring base station operating according to the different subframe configuration. Modifying interference operations may include, for example, skipping interference operations, applying different interference operations to a subframe or a portion of a subframe, or a combination thereof. Modifying interference operations may be based on one or more characteristics of the neighboring base station communications.
Abstract:
Methods and apparatus for selecting samples for secondary synchronization signal (SSS) detection are described. Several alternatives are provided for efficient cell identifier detection. In a first alternative, multiple bursts of a signal received from a cell are sampled with non-uniform spacing between sampling intervals to determine a sequence for cell identification. In a second alternative, samples of a first and a second signal received from a stronger cell are cancelled, and a sequence for detecting a weaker cell is determined by reducing effects of the samples of a third signal received from the weaker cell which do not overlap with the primary synchronization signal (PSS) or SSS of the stronger cell. In a third alternative, a sequence for detecting a weaker cell is determined by reducing effects of any sampled bursts that correspond to a high transmission power portion of a signal from a stronger cell.
Abstract:
Obtaining a timing reference in wireless communication is facilitated when desiring to communicate with a weak serving base station (such as an evolved NodeB) in the presence of a stronger interfering base station. The user equipment (UE) may track a stronger interfering base station's timing, or the UE may track a timing that is derived by a composite power delay profile (PDP) from multiple base stations. The composite PDP may be constructed by adjusting individual base station PDPs according to a weighting scheme. The timing obtained in such a manner may be used for estimation of the channel of the interfering base station and cancelling interfering signals from the base station. It may also be used to estimate the channel of the serving base station after adding a backoff. The UE may track a stronger interfering base station's frequency, or the UE may track a composite frequency.
Abstract:
Methods and apparatus for partitioning resources for enhanced inter-cell interference coordination (eICIC) are provided. Certain aspects involve broadcasting a message indicating time-domain resource partitioning information (RPI), where a user equipment (UE) may be operating in idle mode. With the RPI, the UE may be able to identify protected resources with reduced/eliminated interference from neighboring cells. The RPI in this broadcasted message may be encoded as a bitmap as an alternative or in addition to enumeration of the U/N/X subframes. Other aspects entail transmitting a dedicated or unicast message indicating the time-domain RPI, where a UE may be operating in connected mode. With the RPI, the UE may be able to determine channel state information (CSI), make radio resource management (RRM) measurements, or perform radio link monitoring (RLM), based on one or more signals from a serving base station during the protected time-domain resources.
Abstract:
Monostatic radar with progressive length transmission may be used with half-duplex systems or with full-duplex systems to reduce self-interference. The system transmits a first signal for a first duration and receives a first reflection of the first signal from a first object during a second duration. The system transmits a second signal for a third duration longer than the first duration and receives a second reflection of the second signal from a second object during a fourth duration. The system calculates a position of the first object and the second object based on the first reflection and the second reflection. The first signal, first duration, and second duration are configured to detect reflections from objects within a first distance of the system. The second signal, third duration, and fourth duration are configured to detect reflections from objects between the first distance and a second distance from the system.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a base station may transmit, to a user equipment (UE), a federated learning configuration that indicates one or more parameters of a federated learning procedure associated with a machine learning component. The base station may receive a local update associated with the machine learning component from the UE based at least in part on the federated learning configuration. Numerous other aspects are provided.
Abstract:
A user equipment (UE) may receive a configuration message including a plurality of configurations to at least one of measure or report a set of positioning signals. The UE may select a configuration from the plurality of configurations. The UE may receive the set of positioning signals. The UE may measure the set of positioning signals and transmit a first report message including a first report of the measured set of positioning signals based on the selected configuration. The UE may measure the set of positioning signals based on the selected configuration and transmit a second report message including a second report of the measured set of positioning signals. The UE may measure the set of positioning signals based on the selected configuration and transmit a third report message including a third report of the measured set of positioning signals based on the selected configuration.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a signal. The UE may determine, based at least in part on a machine learning component, a predicted communication metric and a confidence indication, the machine learning component comprising a machine learning model, and wherein determining the predicted communication metric and the confidence indication comprises: receiving, by the machine learning model, an input that comprises an input metric and an error measurement corresponding to the input metric; and providing, by the machine learning model, and based at least in part on a machine learning function and the input, the predicted communication metric and the confidence indication. The UE may perform a wireless communication task based at least in part on the predicted communication metric and the confidence indication. Numerous other aspects are described.
Abstract:
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may establish communications between the UE and a serving cell. The serving cell may be different than each non-serving cell of a set of non-serving cells for the UE. The UE may use machine learning models to determine outputs associated with mobility measurement reporting for the UE. The outputs may indicate whether to perform measurements associated with the set of non-serving cells, whether to report the measurements associated with the set of non-serving cells, one or more non-serving cells within the set of non-serving cells for performing or reporting the measurements, or one or more reference signals of the one or more non-serving cells for performing the measurements, or any combination thereof. The UE may perform the mobility measurement reporting based on the outputs.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit capability information that indicates support for one or more model combinations of machine learning (ML) models, wherein the capability information further indicates one or more performance parameters of an ML model of the ML models with respect to a model combination of the one or more model combinations that includes the ML model. The UE may receive one or more indications to use one or more of the ML models based at least in part on the capability information. Numerous other aspects are described.