Design and Implementation of Modulation Recognition Algorithm Based on Monitoring Receiver

2013 ◽  
Vol 411-414 ◽  
pp. 898-902
Author(s):  
Peng Zhou ◽  
Qi An ◽  
Wei Xia ◽  
Zi Shu He
Keyword(s):  
Digital Signal Processor ◽  
Signal To Noise Ratio ◽  
Digital Signal ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
Signal To Noise ◽  
Communication Signals ◽  
Design And Implementation ◽  
Modulation Type ◽  
The Right

In order to recognize the modulation type of common communication signals, an automatic recognition algorithm based on decision theory is designed and introduced. Combined with engineering realization, an adjustment is made to the algorithm. Then, a recognition scheme is proposed and realized on Digital Signal Processor (DSP), which is the key module in monitoring receiver. When the signal-to-noise ratio is not less than 12 dB, the experimental results show that the right recognition rates of eight common communication signals are above 90%. The algorithm proposed can result in a good case, and the smaller calculated complexity compared with its counterparts makes it could better reach the real-time requirement of engineering realization.

scholarly journals Software implementation of LTE-advanced using Matlab Simulink

2017 ◽  
Vol 6 (4) ◽  
pp. 116 ◽  
Author(s):  
Wessam Mostafa ◽  
Eman Mohamed ◽  
Abdelhalim Zekry
Keyword(s):  
Digital Signal Processor ◽  
Channel Coding ◽  
Signal To Noise Ratio ◽  
Digital Signal ◽  
International Telecommunication Union ◽  
Matlab Simulink ◽  
International Telecommunication ◽  
Coding Schemes ◽  
Simulink Model ◽  
Lte Advanced

Long Term Evolution Advanced (LTE-A) is the evolution of the LTE that developed by 3rd Generation Partnership Project (3GPP).LTE-A exceeded International Telecommunication Union (ITU) requirements for 4th Generation (4G) known as International Mobile Telecommunications (IMT-Advanced). It is formally introduced in October 2009. This paper presents a study and an implementation of the LTE-A downlink physical layer based on 3GPP release 10 standards using Matlab simulink. In addition, it provides the LTE-A performance in terms of Bit Error Rate (BER) against Signal to Noise Ratio (SNR) for different modulation and channel coding schemes. Moreover, different scenarios of Carrier Aggregation (CA) are modeled and implemented. The Simulink model developed for the LTE-A transceiver can be translated into digital signal processor DSP code or VHDL on FPGA code.

Digital Modulation Recognition in Different Environments

2014 ◽  
Vol 1049-1050 ◽  
pp. 2084-2087 ◽  
Author(s):  
Rong Li
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Automatic Recognition ◽  
Modulation Recognition ◽  
Signal To Noise ◽  
Digital Modulation ◽  
Multipath Interference ◽  
Modulation Signal ◽  
Simulation Results ◽  
Recognition Success

For the using of multi-modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

scholarly journals Spatial Transformer Network-Based Automatic Modulation Recognition of Blind Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuxin Huang
Keyword(s):  
Time Scale ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Spatial Transformation ◽  
Modulation Recognition ◽  
Training Time ◽  
Communication Signals ◽  
Frequency Offsets ◽  
Time Required ◽  
High Level

Modulation recognition of communication signals plays an important role in both civil and military uses. Neural network-based modulation recognition methods can extract high-level abstract features which can be adopted for classification of modulation types. Compared with traditional recognition methods based on manually defined features, they have the advantage of higher recognition rate. However, in actual modulation recognition scenarios, due to inaccurate estimation of receiving parameters and other reasons, the input signal samples for modulation recognition may have large phase, frequency offsets, and time scale changes. Existing deep learning-based modulation recognition methods have not considered the influences brought by the above issues, thus resulting in a decreased recognition rate. A modulation recognition method based on the spatial transformation network is proposed in this paper. In the proposed network, some prior models for synchronization in communication are introduced, and the priori models are realized through the spatial transformation subnetwork, so as to reduce the influence of phase, frequency offsets, and time scale differences. Experiments on simulated datasets prove that compared with the traditional CNN, ResNet, and the CLDNN, the recognition rate of the proposed method has increased by 8.0%, 5.8%, and 4.6%, respectively, when the signal-to-noise ratio is greater than 0. Moreover, the proposed network is also easier to train. The training time required for convergence has reduced by 4.5% and 80.7% compared to the ResNet and CLDNN, respectively.

Applying Smart Security Model to Protect Client Services From the Threats of the Optical Network

2021 ◽  
pp. 80-113
Author(s):  
Kamel H. Rahouma ◽  
Ayman A. Ali
Keyword(s):  
Signal To Noise Ratio ◽  
Optical Network ◽  
Optical Signal ◽  
Software Defined Network ◽  
Security Model ◽  
Signal To Noise ◽  
New Model ◽  
Intelligent Software ◽  
Optical Transport Network ◽  
The Right

The chapter discusses the security of the client signals over the optical network from any wiretapping or loosing. The physical layer of the optical transport network (OTN) is the weakest layer in the network; anyone can access the optical cables from any location and states his attack. A security layer is proposed to be added in the mapping of OTN frames. The detection of any intrusion is done by monitoring the variations in the optical signal to noise ratio (OSNR) by using intelligent software defined network. The signal cryptographic is done at the source and the destination only. The chapter shows how the multi-failure restorations in the multi-domains could be done. A new model is introduced by slicing the multi-domains to three layers to fit the needs of 5G. The results show that the multi-failure restoration improved from 25% to 100%, the revenue from some OTN domains increased by 50%, the switching time enhanced by 50%, the latency reduced from 27 msec to 742 usec, and it will take many years to figure out the right keys to perform the decryption process.

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