Search results for: Kelley Trombly-Freytag

2 Toward an Architecture of a Component-Based System Supporting Separation of Non- Functional Concerns

Authors: Jerzy Nogiec, Kelley Trombly-Freytag, Shangping Ren

Abstract:

The promises of component-based technology can only be fully realized when the system contains in its design a necessary level of separation of concerns. The authors propose to focus on the concerns that emerge throughout the life cycle of the system and use them as an architectural foundation for the design of a component-based framework. The proposed model comprises a set of superimposed views of the system describing its functional and non-functional concerns. This approach is illustrated by the design of a specific framework for data analysis and data acquisition and supplemented with experiences from using the systems developed with this framework at the Fermi National Accelerator Laboratory.

Keywords: Distributed system, component-based technology, separation of concerns, software development, supervisory and control, QoS

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1283

1 SNR Classification Using Multiple CNNs

Authors: Thinh Ngo, Paul Rad, Brian Kelley

Abstract:

Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: Classification, classifier fusion, CNN, Deep Learning, prediction, SNR.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 650