Modulation Classification in Multipath Environments Using Deterministic Particle Filtering Export

@proceedings{citeulike:5455072, abstract = {We address the challenge of modulation classification in an unknown dispersive environment. A bank of deterministic particle filters (DPF) is used to jointly estimate the communication channel and data sequence for each possible modulation scheme, and the best path metrics from each DPF form a feature vector. Maximum likelihood modulation classification is performed and makes use of the statistics of the feature vector under different modulation schemes. Simulation results show that the algorithm can successfully classify the observed modulation schemes with as few as 50 symbol observations, making the algorithm practical under time-varying conditions, as well. Additionally, since the communication channel and the transmitted sequence are estimated in the modulation classification process, the proposed algorithm is a natural choice for joint channel estimation, data detection, and modulation classification.}, author = {Roufarshbaf, H. and Nelson, J. K.}, booktitle = {Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th}, citeulike-article-id = {5455072}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/DSP.2009.4785937}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4785937}, doi = {10.1109/DSP.2009.4785937}, journal = {Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th}, keywords = {communication-signals, modulation-classification, particle-filter, signal-classification}, pages = {292--297}, posted-at = {2009-08-17 22:59:11}, priority = {2}, title = {Modulation Classification in Multipath Environments Using Deterministic Particle Filtering}, url = {http://dx.doi.org/10.1109/DSP.2009.4785937}, year = {2009} }

TY - CONF ID - citeulike:5455072 L3 - citeulike-article-id:5455072 N2 - We address the challenge of modulation classification in an unknown dispersive environment. A bank of deterministic particle filters (DPF) is used to jointly estimate the communication channel and data sequence for each possible modulation scheme, and the best path metrics from each DPF form a feature vector. Maximum likelihood modulation classification is performed and makes use of the statistics of the feature vector under different modulation schemes. Simulation results show that the algorithm can successfully classify the observed modulation schemes with as few as 50 symbol observations, making the algorithm practical under time-varying conditions, as well. Additionally, since the communication channel and the transmitted sequence are estimated in the modulation classification process, the proposed algorithm is a natural choice for joint channel estimation, data detection, and modulation classification. JF - Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th EP - 297 TI - Modulation Classification in Multipath Environments Using Deterministic Particle Filtering T2 - Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th SP - 292 KW - communication-signals KW - modulation-classification KW - particle-filter KW - signal-classification AU - Roufarshbaf, H AU - Nelson, JK PY - 2009/// UR - http://dx.doi.org/10.1109/DSP.2009.4785937 ER -