Abstract:
A method, an apparatus, and a computer program product for wireless communication are provided. The apparatus begins to transmit a data packet and control information. Upon receiving an Ack of early decoding of the data packet prior to transmission of the entire data packet, the apparatus ceases transmission of the data packet, yet continues to transmit at least a portion of the control information. Transmission of the portion of the control information that is only needed to decode the data packet is ceased. Transmission of the residual portion of the control information ceases once its use ends. A receiving apparatus begins to receive the data packet and control information. After early decoding the packet, the apparatus transmits an Ack of early decoding and powers down a decoding module. Upon receiving a second Ack, the apparatus ceases to monitor the control information.
Abstract:
Aspects described herein generally relate to communicating between a user equipment (UE) and a cell using frequency division duplexing (FDD) to separate an uplink frequency band and a downlink frequency band with the cell. An indicator can be transmitted from the cell and received by the UE to implement time division duplexing (TDD) on the uplink frequency band. Based at least in part on the indicator, communicating between the UE and the cell can include separating the uplink frequency band into a plurality of downlink subframes for receiving downlink communications from the cell and a plurality of uplink subframes for transmitting uplink communications to the cell.
Abstract:
A method of data-aided timing recovery for Ethernet systems is disclosed. A first device negotiates a pseudorandom number sequence with a second device and receives a data signal from the second device. The first device samples the received data signal to recover a first training sequence. The first device also generates a second training sequence based on the pseudorandom number sequence. The second training sequence is then synchronized with the first training sequence. The synchronized second training sequence is used to align a receive clock signal of the first device with the data signal received from the second device.
Abstract:
A method of data-aided timing recovery for Ethernet systems is disclosed. A first device negotiates a pseudorandom number sequence with a second device and receives a data signal from the second device. The first device samples the received data signal to recover a first training sequence. The first device also generates a second training sequence based on the pseudorandom number sequence. The second training sequence is then synchronized with the first training sequence. The synchronized second training sequence is used to align a receive clock signal of the first device with the data signal received from the second device.
Abstract:
A method of initializing a receiver is performed during an initialization mode. Timing offset values for a timing recovery circuit are repeatedly selected. For each selected timing offset value, timing recovery is performed using the timing offset value and groups of weights for a decision feedback equalizer are repeatedly selected. Each selected group of weights is used to perform blind decision feedback equalization. For each selected group of weights, a metric indicating data reception quality is computed. A timing offset value and a group of weights are chosen based on the computed metrics.
Abstract:
Methods and apparatus of wireless communication at a user equipment comprise compressing an uplink data packet. The methods and apparatus further comprise transmitting the uplink data packet on an uplink dedicated transport channel (DCH) to a network entity a plurality of times within a time duration allowed for transmission of the uncompressed uplink data packet. Moreover, the methods and apparatus comprise receiving a downlink acknowledgement message from the network entity corresponding to the uplink data packet. Additionally, the methods and apparatus comprise terminating transmission of the uplink DCH by transmitting bits of zero power for a remainder of the time duration on the DCH based on receiving the downlink acknowledgement message.
Abstract:
Certain aspects of the present disclosure are generally directed to apparatus and techniques for event state detection. One example method generally includes receiving a plurality of sensor signals at a computing device, determining, at the computing device, probabilities of sub-event states based on the plurality of sensor signals using an artificial neural network for each of a plurality of time intervals, and detecting, at the computing device, the event state based on the probabilities of the sub-event states via a state sequence model.
Abstract:
Methods, systems, and devices for wireless communications are described. In some systems, devices use machine learning (ML) models to support wireless communications. For example, a user equipment (UE) may download ML model information from a network to determine an ML model. The network may additionally configure a status reporting procedure, a fallback procedure, or both for the ML model. In some examples, based on a configuration, the UE may transmit a status report to a base station according to a reporting periodicity, a UE-based trigger, a network-based trigger, or some combination thereof. Additionally or alternatively, the UE may determine to fallback from operating using the ML model to operating in a second mode based on a fallback trigger. In some examples, to restore operating using a downloaded ML model, the UE may download an updated ML model or receive iterative updates to a previously downloaded ML model.
Abstract:
Embodiments include devices and methods for processing an image captured by an image sensor of an unmanned autonomous vehicle (UAV). A processor of the UAV may determine a body coordinate matrix of the UAV. The processor may determine an estimated rotation of the image sensor of the UAV. The processor may determine an estimated rotation of the UAV. The processor may transform an image captured by the image sensor based on the body coordinate matrix, the estimated rotation of the image sensor, and the estimated rotation of the UAV.
Abstract:
The various embodiments include methods and apparatuses for cancelling nonlinear interference during concurrent communication of dual-technology wireless communication devices. Nonlinear interference may be estimated using a multilayer perceptron neural network by augmenting aggressor signal(s) by weight factors, executing a linear combination of the augmented aggressor signals, and executing a nonlinear sigmoid function for the combined aggressor signals at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. Multiple hidden layers may repeat the process for the hidden layer output signals. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal. The weight factors may be trained based on a determination of an error of the estimated nonlinear interference.