Centralized two-stage modulation classification by using networked sensors

Automatic Modulation Classification Based on SNR Estimation via Two-Stage Convolutional Neural Networks

2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) ◽

10.1109/icsp51882.2021.9408847 ◽

2021 ◽

Author(s):

Yu Mao ◽

Meng-Luan Zhu ◽

Ting Sun ◽

Yang-Yang Dong ◽

Chun-Xi Dong

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Modulation Classification ◽

Two Stage ◽

Snr Estimation ◽

Automatic Modulation Classification

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Distributed Hybrid Two-Stage Multi-Sensor Fusion for Cooperative Modulation Classification in Large-Scale Wireless Sensor Networks

Sensors ◽

10.3390/s19194339 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4339 ◽

Cited By ~ 2

Author(s):

Markovic ◽

Sokolovic ◽

Dukic

Keyword(s):

Large Scale ◽

Partial Information ◽

Complete Information ◽

Decision Fusion ◽

Fusion Process ◽

Modulation Classification ◽

Two Stage ◽

Multiple Sensors ◽

Cluster Data ◽

Large Scale Networks

Recent studies showed that the performance of the modulation classification (MC) is considerably improved by using multiple sensors deployed in a cooperative manner. Such cooperative MC solutions are based on the centralized fusion of independent features or decisions made at sensors. Essentially, the cooperative MC employs multiple uncorrelated observations of the unknown signal to gather more complete information, compared to the single sensor reception, which is used in the fusion process to refine the MC decision. However, the non-cooperative nature of MC inherently induces large loss in cooperative MC performance due to the unreliable measure of quality for the MC results obtained at individual sensors (which causes the partial information loss while performing centralized fusion). In this paper, the distributed two-stage fusion concept for the cooperative MC using multiple sensors is proposed. It is shown that the proposed distributed fusion, which combines feature (cumulant) fusion and decision fusion, facilitate preservation of information during the fusion process and thus considerably improve the MC performance. The clustered architecture is employed, with the influence of mismatched references restricted to the intra-cluster data fusion in the first stage. The adopted distributed concept represents a flexible and scalable solution that is suitable for implementation of large-scale networks.

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New results on a two-stage novel modulation classification technique for cognitive radio applications

2011 - MILCOM 2011 Military Communications Conference ◽

10.1109/milcom.2011.6127673 ◽

2011 ◽

Cited By ~ 2

Author(s):

Okhtay Azarmanesh ◽

Sven G. Bilen

Keyword(s):

Cognitive Radio ◽

Modulation Classification ◽

Two Stage ◽

Classification Technique

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Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

1989 ◽

Vol 47 ◽

pp. 368-369

Author(s):

Sengshiu Chung ◽

Peggy Cebe

Keyword(s):

Single Crystals ◽

High Performance ◽

Dilute Solution ◽

Two Stage ◽

Annealing Behavior ◽

High Performance Polymers ◽

Seeding Method ◽

Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

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