Abstract: Methods, systems, and devices for wireless communication are described. A network node may determine a first time location associated with a peak in data traffic for multiple devices in communication with a communications network including the network node. The network node may determine a second time location for the peak in the data traffic for a subset of the devices based on a threshold for an overall peak in data traffic for the multiple devices. The network node may transmit a signal that indicates the second time location for the peak in the data traffic for the subset of devices. The network node may communicate the data with the subset of devices based on the signal indicating the second time location.

Abstract: A positioning reference signal measurement method includes: receiving, at a user equipment from a network entity, an OFDM PRS (orthogonal frequency division multiplexing positioning reference signal) comprising a first set of first OFDM symbols that are consecutive and include a first center symbol and at least one pair of first side symbols disposed symmetrically about the first center symbol and having identical resource element sounding patterns; combining the first side symbols in each of the at least one pair of first side symbols to produce at least one first combined symbol; and determining a measurement of the OFDM PRS based on the at least one first combined symbol and the first center symbol.

Abstract: Methods, systems, and devices for wireless communication are described. A user equipment (UE) may select a non-Discrete Fourier Transform (non-DFT) codebook of a set of non-DFT codebooks associated with a channel state feedback message. The UE may determine a set of singular vectors associated with the non-DFT codebook based on a first machine learning model, where the set of singular vectors corresponds to a subspace associated with the non-DFT codebook. The UE may compress the non-DFT codebook, the set of singular vectors, or both, based on a second machine learning model. The UE may transmit the channel state feedback message including the compressed non-DFT codebook, the compressed set of singular vectors, or both.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive signaling indicating a transition from a first type of channel state information (CSI) processing to a second type of CSI processing at the UE, wherein at least one of the first type of CSI processing or the second type of CSI processing is a machine-learning-based CSI processing type. The UE may transition from the first type of CSI processing to the second type of CSI processing in accordance with the signaling and a timeline for the transition. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. A communication device, otherwise known as a user equipment (UE) may transmit capability information including an indication of a discontinuous monitoring delay period. The UE may receive a message including an indication to adjust monitoring of a physical downlink control channel (PDCCH), and monitor for the PDCCH during the discontinuous monitoring delay period. The UE may adjust monitoring for the PDCCH following the discontinuous monitoring delay period during a temporal period based on the received indication to adjust monitoring of the PDCCH. In some examples, at least one of physical downlink shared channel (PDSCH) reception, downlink reference signal monitoring, uplink reference signal transmissions, and channel reporting is enabled during the temporal period while monitoring of the PDCCH is adjusted.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit a UE capability reporting message including information associated with identifying a set of UE conditions associated with a first set of functionalities, wherein the first set of functionalities corresponds to a set of model features. The UE may receive, based at least in part on transmitting the UE capability reporting message, control signaling identifying a second set of functionalities that is a subset of the first set of functionalities. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. A communication device, otherwise known as a user equipment (UE) may receive a downlink control information (DCI) message including an indication to enable discontinuous monitoring of a physical downlink control channel (PDCCH). The UE may enable the discontinuous monitoring of the PDCCH based on the received DCI message, and disable monitoring of the PDCCH during a temporal period in which at least one of physical downlink shared channel (PDSCH) reception, downlink reference signal monitoring, uplink reference signal transmissions, and channel reporting is enabled. The UE may thereby experience power saving by disabling monitoring of the PDCCH while still performing other wireless operations during the temporal period. The UE may, as a result, also include features for improvements to wireless communications and, in some examples, may promote high reliability and low latency wireless communications, among other benefits.

Abstract: Methods, systems, and devices for wireless communication are described. A wireless device may transmit feedback, such as hybrid automatic repeat request (HARQ) feedback for groups of code blocks rather than for an entire transport block or individual code blocks. The wireless device may transmit an acknowledgement (ACK) or negative-acknowledgement (NACK) to provide feedback for each code block group of a set of code block groups. An ACK may indicate that code blocks in a code block group were successfully decoded, and a NACK may indicate that at least one code block in a code block group was not successfully decoded. Wireless devices may support several techniques for grouping code blocks for feedback reporting to allow for efficient retransmissions and limited overhead. Different grouping schemes may be employed depending on system constraints, device capability, link conditions, or the like.

Abstract: Disclosed are techniques for addressing relation round trip time (RTT) positioning and timing advance (TA) command with user equipment (UE) receive-transmit (Rx-Tx) measurement reporting in wireless network such as in new radio (NR).

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive one or more channel state information (CSI) reference signals (CSI-RSs). The UE may transmit a compressed CSI report via layer 2 signaling, the compressed CSI report being compressed using a machine learning model. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive one or more channel state information (CSI)-reference signals (RSs). The UE may transmit a compressed CSI report that comprises an indication of a plurality of channel metrics, an indication of a channel metric of the plurality of channel metrics comprising a compressed output of a machine learning model having an input as the channel metric, wherein at least one of the plurality of channel metrics is based at least in part on expected orthogonalization of the compressed CSI report at a network node. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described in which a base station may configure a user equipment (UE) with a low latency mode indication that indicates one or more different retransmission timelines or repetition techniques relative to communications that are not associated with the low latency mode indication. The base station may provide the low latency mode indication in downlink control information (DCI) that may include a resource grant for uplink resources for an uplink communication, such as for a UE transmission of pose information of an extended reality (XR) application. The low latency mode indication may signal that no retransmissions of the uplink communication are to be transmitted, that a different retransmission timeline is enabled for low latency mode communications, that retransmissions may happen based on a particular timeline that is determined according to one or more timers, or any combinations thereof.

Abstract: Disclosed are techniques for communication. In an aspect, a network node transmits, to a network entity, a plurality of beam representations for a corresponding plurality of beams, wherein each beam representation of the plurality of beam representations comprises an antenna configuration associated with a beam of the plurality of beams or a mapping of beam angle and beam gain associated with the beam, the antenna configuration including at least a number of antenna elements and an antenna element spacing, and transmits, to the network entity, a first mapping of one or more positioning reference signal (PRS) resources or PRS resource sets to the plurality of beam representations, wherein each of the one or more PRS resources or PRS resource sets is associated with a single beam representation of the plurality of beam representations.

Abstract: In an aspect, a UE obtains information (e.g., UE-specific information, etc.) associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedure, obtains positioning measurement data associated with a location of the UE, and determines a positioning estimate for the UE based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria.

Abstract: In an aspect, a UE obtains information (e.g., UE-specific information) associated with a set of triggering criteria (e.g., from a server, a serving network, e.g., in conjunction with or separate from a set of neural network functions) for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures. The UE obtains positioning measurement data associated with a location of the UE, and processes the positioning measurement data into a respective set of positioning measurement features based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria.

Abstract: Sounding reference signals (SRS) transmitted by a UE, e.g., for positioning or channel estimation, may be configured for one or both of frequency tone level cyclic shift and symbol level code, which, for example, enables multiplexing a greater number of UEs. The frequency tone level cyclic shift is produced by jointly processing a number of symbols with an extended cyclic shift structure. The SRS may be configured using a symbol group level that indicates the number of symbols associated with a cyclic shift structure, and an outer code that indicates a multiplier applied to the SRS at the symbol level, which increases the multiplexing opportunities. The symbol level code may further indicate an extended cyclic shift indicating a linear increase in phase rotation across tones in symbols associated with the symbol group level.

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 receive signaling from a network entity via a cell. The signaling may indicate a state of resource utilization associated with at least the cell. The UE may modify a compression scheme in accordance with the state of resource utilization. The compression scheme may be used at the UE for compressing channel state information (CSI) associated with a layer used for wireless communication between the UE and the network entity via the cell.

Abstract: A configuration for a UE to utilize grant periodicity values that are not multiples or fractions of a slot. The apparatus receives, from a base station, a configuration for a periodicity for a configured grant, the configured grant being for communicating with the base station based on the corresponding periodicity. The apparatus communicates with the base station based on the configured periodicity in association with the configured grant, the communicating being one of receiving the plurality of PDSCHs associated with the configured grant based on the corresponding configured periodicity or transmitting the plurality of PUSCHs associated with the configured grant based on the corresponding configured periodicity.

Abstract: Certain aspects of the present disclosure provide techniques for exchanging information between user equipments (UEs) and network entities regarding which models the UEs and network entities support. A method that may be performed by a UE includes: obtaining an indication of one or more machine learning (ML) based network-side models applicable at a network entity; and transmitting signaling indicating one or more of the ML-based network-side models supported by the UE.