Abstract:
Certain aspects relate to methods and apparatus for wireless communications, comprising determining to use a reduced paging cycle to page a user equipment (UE) of a first type that supports the reduced paging cycle, the reduced paging cycle having a shorter period relative to a paging cycle used with UEs of a second type which do not support the reduced paging cycle, and paging the UE of the first type in accordance with the reduced paging cycle. Certain aspects relate to methods and apparatus for conveying system information by a base station, comprising broadcasting a first system information common to each cell of a group of cells in an area and broadcasting a second system information that can vary between cells in the group of cells, wherein the second system information is broadcast more frequently than the first system information.
Abstract:
A method for managing synapse plasticity in a neural network includes converting a first set of synapses from a plastic synapse type to a fixed synapse type. The method may also include converting a second set of synapses from the fixed synapse type to the plastic synapse type.
Abstract:
Certain aspects of the present disclosure support techniques for time synchronization of spiking neuron models that utilize multiple nodes. According to certain aspects, a neural model (e.g., of an artificial nervous system) may be implemented using a plurality of processing nodes, each processing node implementing a neuron model and communicating via the exchange of spike packets carrying information regarding spike information for artificial neurons. A mechanism may be provided for maintaining relative spike-timing between the processing nodes. In some cases, a mechanism may also be provided to alleviate deadlock conditions between the multiple nodes.
Abstract:
Certain aspects of the present disclosure provide methods and apparatus for creating tags (static or dynamic) for input/output classes of a neural network model using supervised learning. The method includes augmenting a neural network model with a plurality of neurons and training the augmented network using spike timing dependent plasticity (STDP) to determine one or more tags.
Abstract:
An access point is identified based on a plurality of pilot signatures. Here, in addition to transmitting a pilot signal that is encoded (e.g., spread/scrambled) using a particular pilot signature, an access point transmits a message that includes at least one indication of at least one other pilot signature. For example, an access point may use one PN offset to generate a pilot signal and transmit a message that identifies at least one other PN offset. An access terminal that receives the pilot signal and the message may then generate a pilot report that identifies all of these pilot signatures. Upon receiving a handover message including this pilot-related information, a target network entity with knowledge of the pilot signatures assigned to that access point may then accurately identify the access point as a target for handover of the access terminal.