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An Algorithm for Automatic Recogniton of Digital QAM Modulations

Abstract

This paper proposes a new Matlab-developed algorithm for automatic recognition of digital modulations using the constellation of states. Using this technique the automatic distinction between four digital modulation schemes (8-QAM, 16-QAM, 32-QAM and 64-QAM) was made. It has been seen that the efficiency of the algorithm is influenced by the type of modulation, the value of the signal-to-noise ratio and the number of samples. In the case of an AWGN noise channel the simulation results indicated that the value of SNR (signal-to-noise ratio) has a small influence on the recognition rate for lower-order QAM (8-QAM and 16-QAM). The length of the signal may change essentially the recognition rate of this algorithm especially for modulations with a high number of bits per symbol. Consequently, for the 64-QAM modulation in a case of 25dB signal-to-noise ratio the recognition rate is doubled if the sample rate is incresed from 5400 to 80640.

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Data-Aided Carrier Recovery with QPSK Modulation

Abstract

Digital communication has proven to be the most efficient method of data transmission especially where long distances are involved. This led to the invention of more sophisticated methods of communication ranging from mobile handset communication to more advanced satellite communication. The speeds of passing information have been improving over the years and real time video communication has been made possible with digital devices. Various methods of digital data transmission are employed based on the information to be transmitted. This paper focuses on carrier recovery in digital communication systems, especially those based on Quadrature Phase Shift Keying (QPSK) modulation and demodulation scheme. The design being implemented is that of coherent demodulation for QPSK scheme using SIMULINK design tool. Performance of QPSK is also investigated to make a comparison and the suitability of the scheme to use in digital data transmission applications.

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M-Ary Phase Modulation for Digital Watermarking

. Trappe W., Wu M., Wang Z. J. and Liu K. J. R. (2003). Anticollusion fingerprinting for multimedia, IEEE Transactions on Signal Processing , 51 (4): 1069-1087. Wilson S. G. (1996). Digital Modulation and Coding , Prentice Hall, New York. Zeng W. and Liu B. (1999). A statistical watermark detection technique without using original images for resolving rightful ownerships of digital images, IEEE Transactions on Image Processing , 8 (11): 1534-1548.

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HF Noise Measurement in the Western Part of Romania

classification of digital modulations in presence of HF noise , EURASIP J. Adv. Signal Process., (2012) 2012:238. [8] M. Bröms, Some results from measurements of interference in Sweden , Fifth Int. Conf. of HFRadio Systems and Techniques, pp. 337-342, 1991. [9] W.N. Furman, J.W. Nieto, W.M. Batts, Wideband HF channel availability – Measurement techniques and results , 14th Ionospheric Effects Symposium, Alexandria VA, USA, pp. 1-7, 2015. [10] A. De Sabata, C. Balint, Structure of Signal Received by Passive Ionospheric Sounding in the HF Band at the

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A New Approach to Performance analysis of Point-To-Point Radio Links at Frequencies above 70 GHz

for Propagation Modeling", 2003. BONATI, A. P. : Essential Knowledge of Rain Structure For Radio Application Based on Available Data and Models, Radio Africa 99 (Oct 1999). XIONG, F. : Digital Modulation Techniques, Artech House, 2000. ETSI TS 102 524 V1.1.1 (2006-07), Radio Equipment and Antennas for Use in Point-to-Point MillimeterWave Applications in Fixed Services Frequency Bands 71 GHz to 76 GHz and 81 GHz to 86 GHz, 2007. LIN, S.—COSTELO, D. : Error

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Low Power Consumption Digital Clock Recovery Circuit Based on Threshold Crossing

-Complexity Non-Data-Aided Timing Recovery for PAM-based M-ary CPM Receivers, Radioengineering 21 No. 3 (Sep 2012). [9] LOTZE, J.—FAHMY, S. A.—NOGUERA, J.—DOYLE, L. E. : A Model-Based Approach to Cognitive Radio Design, IEEE J. Sel. Areas Commun. 29 No. 2 (Feb 2011). [10] XIONG, F. : Digital Modulation Techniques, Second Edition, Artech House, 2006. [11] CORDESSES, L. : A Direct Digital Synthesis: A Tool for Periodic Wave Generation (Part I)a, IEEE Signal Processing Magazine 21 No. 4 (July 2004), 50–54. [12] CESSNA, R. J.—DONALD, L. M. : Phase

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A Model of OFDM based Maritime VHF Communication System for Data Exchange

, Geneva, 2012. 34. F. Xiong, Digital Modulation Technique: Second edition , Artech House Inc., Norwood, 2006.

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Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

: Decentralized compressive data storage in wireless sensor networks. Ad Hoc Networks, 37, 475-485. [10] Zong, F., Eurydice, M.N., Galvosas, P. (2016). Fast reconstruction of highly undersampled MR images using one and two dimensional principal component analysis. Magnetic Resonance Imaging, 34, 227-238. [11] Sun, Z., Wang, S., Chen, X. (2016). Feature-based digital modulation recognition using compressive sampling. Mobile Information Systems, 10, 9754162. [12] Maceková, Ľ., Žiga, M. (2014). The wireless sensor network concept for

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