A Computationally Lightest and Robust Neural Network Receiver for Ultra Wideband Time Hopping Communication Systems

Author(s):  
Mohamed Ershadh ◽  
Meenakshi M
2008 ◽  
Vol 2 (8) ◽  
pp. 1051 ◽  
Author(s):  
A.R. Alizad ◽  
B. Alipanahi ◽  
M. Shiva ◽  
S.H. Jamali ◽  
S. Nader-Esfahani

2015 ◽  
Vol 743 ◽  
pp. 545-550 ◽  
Author(s):  
Gang Liu ◽  
Gui Xin Xuan ◽  
Xia Zhang

In this paper, we analyze correlation property of time-hopping (TH) codes for time-hopping spread spectrum ultra wideband (THSS-UWB) communication systems. Several different definitions of TH periodic correlation function are compared and discussed. Based on the comparison, the relation between TH codes and frequency-hopping (FH) codes is obtained. Also, the averages of TH periodic correlation function values are investigated, and the relations between averages and four parameters of TH codes are introduced. Based on the result, low bound of maximal TH correlation function values is further given, where the expression of low bound of TH codes relates codes period, the number of time slots, TH codes family size and maximal TH correlation function values.


2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.


SPIN ◽  
2012 ◽  
Vol 02 (03) ◽  
pp. 1240004 ◽  
Author(s):  
NIAN X. SUN ◽  
GOPALAN SRINIVASAN

Multiferroic materials and devices have attracted intensified recent interests due to the demonstrated strong magnetoelectric (ME) coupling in new multiferroic materials and devices with unique functionalities and superior performance characteristics. Strong ME coupling has been demonstrated in a variety of multiferroic heterostructures, including bulk magnetic on ferro/piezoelectric multiferroic heterostructures, magnetic film on ferro/piezoelectric slab multiferroic heterostructures, thin film multiferroic heterostructures, etc. Different multiferroic devices have been demonstrated, which include magnetic sensors, energy harvesters, and voltage tunable multiferroic RF/microwave devices which are compact, lightweight, and power efficient. In this progress report, we cover the most recent progress on multiferroic heterostructures and devices with a focus on voltage tunable multiferroic heterostructures and devices with strong converse ME coupling. Recent progress on magnetic-field tunable RF/microwave devices are also covered, including novel non-reciprocal tunable bandpass filters with ultra wideband isolation, compact, low loss and high power handling phase shifters, etc. These novel tunable multiferroic heterostructures and devices and tunable magnetic devices provide great opportunities for next generation reconfigurable RF/microwave communication systems and radars, Spintronics, magnetic field sensing, etc.


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