Abstract: A processor-implemented method includes sampling, according to a priority sampling policy, a set of node priorities from a computation graph. Each node priority of the set of node priorities may be associated with a respective node on the computation graph. Additionally, each node may represent an operation of a task performed by an artificial neural network. The method also includes converting, via a list scheduling function, the node priorities to a schedule that associates each node of the computation graph with a processor of a group of processors of a device associated with the artificial neural network, the schedule associated with a makespan. The method further includes performing the task in accordance with the schedule.

Abstract: A processor-implemented method includes generating, by a scheduling model, a group of schedules from a computation graph associated with a task, each node on the computation graph being associated with an operation of an artificial neural network, each schedule of the group of schedules associating each node of the computation graph with a processor of a group of processors of a hardware device. The processor-implemented method also includes testing one or more schedules of the group of schedules on the hardware device or a model of the hardware device. The processor-implemented method further includes selecting a schedule of the one or more schedules based on testing the one or more schedules, the selected schedule satisfying a selection condition.

Abstract: A processor-implemented method for generating a schedule for executing operations of a compute graph includes receiving a graph including multiples nodes connected by edges. Each of the multiple nodes represents an operation to be executed. A set of sequences for executing the nodes is determined based on one or more precedence constraints. One or more sequences are selected from the set of sequences based on a memory constraint associated with a device for executing the nodes. A schedule for executing the nodes on the device is generated based on the selected one or more sequences.

Abstract: A processor-implemented method for generating a topological order using an artificial neural network (ANN) includes receiving a set of tasks to be performed. The tasks are represented in a graph including multiple nodes connected by edges. Each node corresponds to a task in the set of tasks. A scheduling priority is assigned to each node in the graph. A next node of potential next nodes is selected according to a probability of each of the potential next nodes based on the assigned scheduling priorities and a topology of the graph. A topological order of the tasks is generated by repeating the selection of the next node.

Abstract: A method performs XNOR-equivalent operations by adjusting column thresholds of a compute-in-memory array of an artificial neural network. The method includes adjusting an activation threshold generated for each column of the compute-in-memory array based on a function of a weight value and an activation value. The method also includes calculating a conversion bias current reference based on an input value from an input vector to the compute-in-memory array, the compute-in-memory array being programmed with a set of weights. The adjusted activation threshold and the conversion bias current reference are used as a threshold for determining the output values of the compute-in-memory array.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: A method performs XNOR-equivalent operations by adjusting column thresholds of a compute-in-memory array of an artificial neural network. The method includes adjusting an activation threshold generated for each column of the compute-in-memory array based on a function of a weight value and an activation value. The method also includes calculating a conversion bias current reference based on an input value from an input vector to the compute-in-memory array, the compute-in-memory array being programmed with a set of weights. The adjusted activation threshold and the conversion bias current reference are used as a threshold for determining the output values of the compute-in-memory array.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: An apparatus and method for transmitting an indicator of channel quality while minimizing the use of a broadcast channel is described. A metric of forward link geometry of observed transmission signals is determined. An indicator of channel quality value is determined as a function of the observed transmission signals. An access sequence is selected, randomly, from one group of a plurality of groups of access sequences, wherein each of the plurality of groups of access sequences correspond to different ranges of channel quality values.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: The disclosure relates to drone user equipment (UE) indications that may be conveyed to a wireless network. In particular, a UE that has flight capabilities (i.e., capabilities to operate as an unmanned aircraft system) and optional further capabilities to report a current height level may indicate such capabilities to the wireless network. As such, the wireless network may differentiate the drone UE from other UEs that only operate on the ground. Furthermore, the optional current height level may enable the wireless network to differentiate among drone UEs operating at different heights and/or from other UEs that are operating on the ground. The wireless network may further use the information indicating the flight capabilities either alone or in combination with the optional height information to configure power control parameters, manage interference, provide mobility management functions, generate neighbor lists, control beamforming, or implement a radio resource configuration or management procedure.

Abstract: Systems and methodologies are described that facilitate dynamically supplementing or decrementing resource assignments to mobile devices in a wireless network environment without requiring transmission of replacement assignments. Supplemental assignments can be generated based on information related to mobile device need and resource availability. Moreover, resource assignments can be persisted for a mobile device.

Abstract: The disclosed embodiments provide for methods and systems for selecting sectors for handoff in a communication system. According to one aspect, the method includes monitoring an indicia of transmit power on a plurality of RL control channels directed to a plurality of sectors, and selecting one of the sectors as a candidate for a RL handoff. The disclosed embodiments also provide for methods and systems for indicating a selected serving sector for handoff in a communication system. According to one aspect, the method includes sending a first signal to a first sector to indicate the first sector as a serving sector for a forward-link handoff, and sending a second signal to a second sector to indicate the second sector as a serving sector for a reverse-link handoff.

Abstract: Techniques are provided to support fast frequency hopping with a code division multiplexed (CDM) pilot in a multi-carrier communication system (e.g., an OFDMA system). Each transmitter (e.g., each terminal) in the system transmits a wideband pilot on all subbands to allow a receiver (e.g., a base station) to estimate the entire channel response at the same time. The wideband pilot for each transmitter may be generated using direct sequence spread spectrum processing and based on a pseudo-random number (PN) code assigned to that transmitter. This allows the receiver to individually identify and recover multiple wideband pilots transmitted concurrently by multiple transmitters. For a time division multiplexed (TDM)/CDM pilot transmission scheme, each transmitter transmits the wideband pilot in bursts. For a continuous CDM pilot transmission scheme, each transmitter continuously transmits the wideband pilot, albeit at a low transmit power level.

Abstract: Systems and methodologies are described that facilitate using both synchronous and asynchronous HARQ assignment schemes. According to various aspects, the system and methods are described for determining if a synchronous HARQ or an asynchronous HARQ assignment scheme should be used for providing a resource assignment and indicating whether the resource assignment is provided using the synchronous HARQ or an asynchronous HARQ assignment scheme.

Abstract: Systems and methodologies are described that facilitate dynamically supplementing or decrementing resource assignments to mobile devices in a wireless network environment without requiring transmission of replacement assignments. Supplemental assignments can be generated based on information related to mobile device need and resource availability. Moreover, resource assignments can be persisted for a mobile device.