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..

Interference management and performance analysis of UMTS/HSPA+ femtocells

Abstract: Femtocells are low-power cellular base stations that operate in licensed spectrum. They are typically deployed indoors to improve coverage and provide excellent user experience, including high data rates. Cellular operators benefit from reduced infrastructure and operational expenses for capacity upgrades and coverage improvements. Femtocells also bring unique challenges, such as unplanned deployment, user installation, restricted access, and interoperability with existing handsets and network infrastructure. Although femtocells may cause some interference to other users in the network, with the use of proper interference management techniques, this can be well controlled. We present interference management techniques for both downlink and uplink of femtocells operating based on 3GPP Release 7 standards (also known as HSPA+). Femtocell carrier selection and femtocell DL Tx power self-calibration are proposed as key interference management methods for downlink. For uplink interference management, adaptive attenuation at the femtocell and limiting the Tx power of the femtocell users are proposed. Different interference models and their analysis are presented. In addition, coverage performance and capacity results are presented to quantify the benefits of femtocells. We demonstrate that in addition to coverage enhancements, significant capacity improvements are achieved on both downlink and uplink when femtocells are deployed in 3G UMTS/HSPA+ networks.

Data-Free Quantization Through Weight Equalization and Bias Correction

Abstract: We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on common computer vision architectures and tasks. 8-bit fixed-point quantization is essential for efficient inference on modern deep learning hardware. However, quantizing models to run in 8-bit is a non-trivial task, frequently leading to either significant performance reduction or engineering time spent on training a network to be amenable to quantization. Our approach relies on equalizing the weight ranges in the network by making use of a scale-equivariance property of activation functions. In addition the method corrects biases in the error that are introduced during quantization. This improves quantization accuracy performance, and can be applied to many common computer vision architectures with a straight forward API call. For common architectures, such as the MobileNet family, we achieve state-of-the-art quantized model performance. We further show that the method also extends to other computer vision architectures and tasks such as semantic segmentation and object detection.

An anti-jamming stochastic game for cognitive radio networks

Abstract: Various spectrum management schemes have been proposed in recent years to improve the spectrum utilization in cognitive radio networks. However, few of them have considered the existence of cognitive attackers who can adapt their attacking strategy to the time-varying spectrum environment and the secondary users' strategy. In this paper, we investigate the security mechanism when secondary users are facing the jamming attack, and propose a stochastic game framework for anti-jamming defense. At each stage of the game, secondary users observe the spectrum availability, the channel quality, and the attackers' strategy from the status of jammed channels. According to this observation, they will decide how many channels they should reserve for transmitting control and data messages and how to switch between the different channels. Using the minimax-Q learning, secondary users can gradually learn the optimal policy, which maximizes the expected sum of discounted payoffs defined as the spectrum-efficient throughput. The proposed stationary policy in the anti-jamming game is shown to achieve much better performance than the policy obtained from myopic learning, which only maximizes each stage's payoff, and a random defense strategy, since it successfully accommodates the environment dynamics and the strategic behavior of the cognitive attackers.

Rate Region of the Quadratic Gaussian Two-Encoder Source-Coding Problem

Abstract: We determine the rate region of the quadratic Gaussian two-encoder source-coding problem. This rate region is achieved by a simple architecture that separates the analog and digital aspects of the compression. Furthermore, this architecture requires higher rates to send a Gaussian source than it does to send any other source with the same covariance. Our techniques can also be used to determine the sum-rate of some generalizations of this classical problem. Our approach involves coupling the problem to a quadratic Gaussian ldquoCEO problem.rdquo

Adaptive Observation of Behavioral Features on a Mobile Device

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.