Qualcomm

About: Qualcomm is a company organization based out in Farnborough, United Kingdom. It is known for research contribution in the topics: Wireless & Signal. The organization has 19408 authors who have published 38405 publications receiving 804693 citations. The organization is also known as: Qualcomm Incorporated & Qualcomm, Inc..

Method and system for changing forward traffic channel power allocation during soft handoff

Abstract: A system and method for adjusting forward traffic channel power allocation in a communications system, wherein signal qualities of pilot channels respectively transmitted by multiple base stations in an active set of a mobile station, are measured by the mobile station, compared to a signal quality standard, and the comparison results reported to a system controller, thereby to indicate which of the pilots at the mobile station surpass the standard. The system controller then adjusts the forward channel power allocation based on the comparison results.

Pre-fetching information based on gesture and/or location

Abstract: Example methods, apparatuses, and articles of manufacture are disclosed that may be utilized to pre-fetch and/or obtain information for use with mobile devices based, at least in part, on a gesture of a user and/or location of a mobile device. By way of example, a method may include detecting an arrival of a mobile device at a location; and pre-fetching, in response to the detection of the arrival, information in connection with executing one or more applications and/or functions on the mobile device. In certain implementations, a method may include processing signals received from at least one sensor at a mobile device; inferring, in response to the processing the signals, a likelihood of a user executing one or more applications and/or functions on the mobile device; and pre-fetching information in connection with the executing applications and/or functions on the mobile device based, at least in part, on the likelihood.

Method and apparatus for performing soft switch of virtual sim service contracts

Abstract: A system and method for providing updated rules governing the switching of enabled provisioning data supporting a wireless service contract. A mobile device may be initially programmed with a profile data table and priority list index data table to automatically enable provisioning data supporting one of the plurality of service providers stored in a VSIM internal memory unit to conduct a wireless communication when certain operational parameter values are satisfied. The profile data table and priority list index data table may be automatically updated in response to a variety of triggers. The profile data table and priority list index data table may be stored remotely. Operational parameters regarding each call request are collected and transmitted to a remote service contract selection server. The selection of an optimal service provider account may be made remotely in the service contract selection server and transmitted back to the mobile device.

Vehicular Communications Using DSRC: Challenges, Enhancements, and Evolution

Abstract: Dedicated Short-Range Communications (DSRC) has been designed to support vehicular communications. In the U.S., DSRC operates in the 5.9 GHz licensed spectrum band. Its physical (PHY) and medium access control (MAC) layers, defined in the IEEE 802.11p standard, are based on the IEEE 802.11 family of Wi-Fi standards. Vehicular communication environments differ significantly from the sparse and low-velocity nomadic use cases of a typical Wi-Fi deployment. Thus, there are many challenges to adapt Wi-Fi technologies to support the unique requirements of vehicular communications such as achieving high and reliable performance in highly mobile, often densely populated, and frequently non-line-of-sight environments. The automotive and the communications industries, academia, and governments around the world have been devoting tremendous efforts to address these challenges, and significant achievements have been made. Remaining challenges can be addressed by the future versions of DSRC. In this paper, we investigate the current technologies used by DSRC to support vehicle safety communications, analyze existing and possible DSRC performance enhancements that can be realized in the near term, and provide a few initial thoughts on the DSRC evolution path.

SnaPEA: predictive early activation for reducing computation in deep convolutional neural networks

Abstract: Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a single input. To reduce these computations, this paper offers a solution that leverages a combination of runtime information and the algorithmic structure of CNNs. Specifically, in numerous modern CNNs, the outputs of compute-heavy convolution operations are fed to activation units that output zero if their input is negative. By exploiting this unique algorithmic property, we propose a predictive early activation technique, dubbed SnaPEA. This technique cuts the computation of convolution operations short if it determines that the output will be negative. SnaPEA can operate in two distinct modes, exact and predictive. In the exact mode, with no loss in classification accuracy, SnaPEA statically re-orders the weights based on their signs and periodically performs a single-bit sign check on the partial sum. Once the partial sum drops below zero, the rest of computations can simply be ignored, since the output value will be zero in any case. In the predictive mode, which trades the classification accuracy for larger savings, SnaPEA speculatively cuts the computation short even earlier than the exact mode. To control the accuracy, we develop a multi-variable optimization algorithm that thresholds the degree of speculation. As such, the proposed algorithm exposes a knob to gracefully navigate the trade-offs between the classification accuracy and computation reduction. Compared to a state-of-the-art CNN accelerator, SnaPEA in the exact mode, yields, on average, 28% speedup and 16% energy reduction in various modern CNNs without affecting their classification accuracy. With 3% loss in classification accuracy, on average, 67.8% of the convolutional layers can operate in the predictive mode. The average speedup and energy saving of these layers are 2.02x and 1.89x, respectively. The benefits grow to a maximum of 3.59x speedup and 3.14x energy reduction. Compared to static pruning approaches, which are complimentary to the dynamic approach of SnaPEA, our proposed technique offers up to 63% speedup and 49% energy reduction across the convolution layers with no loss in classification accuracy.