78,881 research outputs found
Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications
This paper compares three candidate large-scale propagation path loss models
for use over the entire microwave and millimeter-wave (mmWave) radio spectrum:
the alpha-beta-gamma (ABG) model, the close-in (CI) free space reference
distance model, and the CI model with a frequency-weighted path loss exponent
(CIF). Each of these models have been recently studied for use in standards
bodies such as 3GPP, and for use in the design of fifth generation (5G)
wireless systems in urban macrocell, urban microcell, and indoor office and
shopping mall scenarios. Here we compare the accuracy and sensitivity of these
models using measured data from 30 propagation measurement datasets from 2 GHz
to 73 GHz over distances ranging from 4 m to 1238 m. A series of sensitivity
analyses of the three models show that the physically-based two-parameter CI
model and three-parameter CIF model offer computational simplicity, have very
similar goodness of fit (i.e., the shadow fading standard deviation), exhibit
more stable model parameter behavior across frequencies and distances, and
yield smaller prediction error in sensitivity testing across distances and
frequencies, when compared to the four-parameter ABG model. Results show the CI
model with a 1 m close-in reference distance is suitable for outdoor
environments, while the CIF model is more appropriate for indoor modeling. The
CI and CIF models are easily implemented in existing 3GPP models by making a
very subtle modification -- by replacing a floating non-physically based
constant with a frequency-dependent constant that represents free space path
loss in the first meter of propagation.Comment: Open access available at:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=743465
A Broad Learning Approach for Context-Aware Mobile Application Recommendation
With the rapid development of mobile apps, the availability of a large number
of mobile apps in application stores brings challenge to locate appropriate
apps for users. Providing accurate mobile app recommendation for users becomes
an imperative task. Conventional approaches mainly focus on learning users'
preferences and app features to predict the user-app ratings. However, most of
them did not consider the interactions among the context information of apps.
To address this issue, we propose a broad learning approach for
\textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor
\textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to
effectively integrate user's preference, app category information and
multi-view features to facilitate the performance of app rating prediction. The
multidimensional structure is employed to capture the hidden relationships
between multiple app categories with multi-view features. We develop an
efficient factorization method which applies Tucker decomposition to learn the
full-order interactions within multiple categories and features. Furthermore,
we employ a group norm regularization to learn the group-wise
feature importance of each view with respect to each app category. Experiments
on two real-world mobile app datasets demonstrate the effectiveness of the
proposed method
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Applying machine learning to predict future adherence to physical activity programs.
BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011
Channel Acquisition for Massive MIMO-OFDM with Adjustable Phase Shift Pilots
We propose adjustable phase shift pilots (APSPs) for channel acquisition in
wideband massive multiple-input multiple-output (MIMO) systems employing
orthogonal frequency division multiplexing (OFDM) to reduce the pilot overhead.
Based on a physically motivated channel model, we first establish a
relationship between channel space-frequency correlations and the channel power
angle-delay spectrum in the massive antenna array regime, which reveals the
channel sparsity in massive MIMO-OFDM. With this channel model, we then
investigate channel acquisition, including channel estimation and channel
prediction, for massive MIMO-OFDM with APSPs. We show that channel acquisition
performance in terms of sum mean square error can be minimized if the user
terminals' channel power distributions in the angle-delay domain can be made
non-overlapping with proper phase shift scheduling. A simplified pilot phase
shift scheduling algorithm is developed based on this optimal channel
acquisition condition. The performance of APSPs is investigated for both one
symbol and multiple symbol data models. Simulations demonstrate that the
proposed APSP approach can provide substantial performance gains in terms of
achievable spectral efficiency over the conventional phase shift orthogonal
pilot approach in typical mobility scenarios.Comment: 15 pages, 4 figures, accepted for publication in the IEEE
Transactions on Signal Processin
Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data
A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance
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