scholarly journals Blind Identification of Convolutional Encoder Parameters

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shaojing Su ◽  
Jing Zhou ◽  
Zhiping Huang ◽  
Chunwu Liu ◽  
Yimeng Zhang
Keyword(s):  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Convolutional Codes ◽  
Soft Decision ◽  
Decision Systems ◽  
Correlation Attack ◽  
Intelligent Communication ◽  
Convolutional Encoder ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper gives a solution to the blind parameter identification of a convolutional encoder. The problem can be addressed in the context of the noncooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary convolutional codes, while the coding parameters are unknown. Some previous literatures have significant contributions for the recognition of convolutional encoder parameters in hard-decision situations. However, soft-decision systems are applied more and more as the improvement of signal processing techniques. In this paper we propose a method to utilize the soft information to improve the recognition performances in soft-decision communication systems. Besides, we propose a new recognition method based on correlation attack to meet low signal-to-noise ratio situations. Finally we give the simulation results to show the efficiency of the proposed methods.

scholarly journals Multi-Domain Communication Systems and Networks: A Tensor-Based Approach

Network ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 50-74
Author(s):  
Divyanshu Pandey ◽  
Adithya Venugopal ◽  
Harry Leib
Keyword(s):  
Communication Systems ◽  
Physical Layer ◽  
Mathematical Framework ◽  
Trade Off ◽  
Information Theoretic ◽  
Time Frequency ◽  
Tensor Formulation ◽  
Processing Techniques ◽  
Multiple Domains ◽  
Signal Processing Techniques

Most modern communication systems, such as those intended for deployment in IoT applications or 5G and beyond networks, utilize multiple domains for transmission and reception at the physical layer. Depending on the application, these domains can include space, time, frequency, users, code sequences, and transmission media, to name a few. As such, the design criteria of future communication systems must be cognizant of the opportunities and the challenges that exist in exploiting the multi-domain nature of the signals and systems involved for information transmission. Focussing on the Physical Layer, this paper presents a novel mathematical framework using tensors, to represent, design, and analyze multi-domain systems. Various domains can be integrated into the transceiver design scheme using tensors. Tools from multi-linear algebra can be used to develop simultaneous signal processing techniques across all the domains. In particular, we present tensor partial response signaling (TPRS) which allows the introduction of controlled interference within elements of a domain and also across domains. We develop the TPRS system using the tensor contracted convolution to generate a multi-domain signal with desired spectral and cross-spectral properties across domains. In addition, by studying the information theoretic properties of the multi-domain tensor channel, we present the trade-off between different domains that can be harnessed using this framework. Numerical examples for capacity and mean square error are presented to highlight the domain trade-off revealed by the tensor formulation. Furthermore, an application of the tensor framework to MIMO Generalized Frequency Division Multiplexing (GFDM) is also presented.

scholarly journals Filtering Techniques for Chaotic Signal Processing

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 450 ◽  
Author(s):  
Denis Butusov ◽  
Timur Karimov ◽  
Alexander Voznesenskiy ◽  
Dmitry Kaplun ◽  
Valery Andreev ◽  
...  
Keyword(s):  
Signal Processing ◽  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Chaotic Signal ◽  
Denoising Method ◽  
Practical Applications ◽  
Iir Filter ◽  
Chaotic Signals ◽  
Processing Techniques

The vulnerability of chaotic communication systems to noise in transmission channel is a serious obstacle for practical applications. Traditional signal processing techniques provide only limited possibilities for efficient filtering broadband chaotic signals. In this paper, we provide a comparative study of several denoising and filtering approaches: a recursive IIR filter, a median filter, a wavelet-based denoising method, a method based on empirical modes decomposition, and, finally, propose the new filtering algorithm based on the cascade of driven chaotic oscillators. Experimental results show that all the considered methods make it possible to increase the permissible signal-to-noise ratio to provide the possibility of message recognition, while the new proposed method showed the best performance and reliability.

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Gert Van Dijck ◽  
Marc M. Van Hulle
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Cell Types ◽  
Signal Quality ◽  
Quality Signals ◽  
Processing Techniques ◽  
Electrode Selection ◽  
Signal Processing Techniques ◽  
Processing Steps ◽  
Selection Of

AbstractRecently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the “usefulness” of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing “good quality signals” or “signals of interest.” This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on “signal quality.” The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist’s appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature “electronic depth control” in contrast to the mechanical repositioning of the electrode shafts in search of “good quality signals” or “signals of interest.”

scholarly journals Discrimination of Parkinsonian Tremor From Essential Tremor by Voting Between Different EMG Signal Processing Techniques

2014 ◽  
Vol 10 (2) ◽  
pp. 11
Author(s):  
A Hossen ◽  
Z Al-Hakim ◽  
M Muthuraman ◽  
J Raethjen ◽  
G Deuschl ◽  
...  
Keyword(s):  
Signal Processing ◽  
Essential Tremor ◽  
Test Group ◽  
The Body ◽  
Soft Decision ◽  
Test Set ◽  
Parkinsonian Tremor ◽  
Emg Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

 Parkinson's disease (PD) and essential tremor (ET) are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor”. PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG) signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC) of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively. 

scholarly journals An Overview of Signal Processing Techniques for Terahertz Communications

2020 ◽  
Author(s):  
Hadi Sarieddeen ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Signal Processing ◽  
Channel Coding ◽  
Communication Systems ◽  
Channel Model ◽  
Mimo Systems ◽  
Future Generation ◽  
Data Detection ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

scholarly journals Genetic Algorithms for Soft-Decision Decoding of Linear Block Codes

1994 ◽  
Vol 2 (2) ◽  
pp. 145-164 ◽  
Author(s):  
Harpal Maini ◽  
Kishan Mehrotra ◽  
Chilukuri Mohan ◽  
Sanjay Ranka
Keyword(s):  
Genetic Algorithms ◽  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Search Space ◽  
Block Codes ◽  
Specific Information ◽  
Soft Decision ◽  
Linear Block Codes ◽  
Soft Decision Decoding ◽  
Linear Block

Soft-decision decoding is an NP-hard problem of great interest to developers of communication systems. We show that this problem is equivalent to the problem of optimizing Walsh polynomials. We present genetic algorithms for soft-decision decoding of binary linear block codes and compare the performance with various other decoding algorithms including the currently developed A* algorithm. Simulation results show that our algorithms achieve bit-error-probabilities as low as 0.00183 for a [104,52] code with a low signal-to-noise ratio of 2.5 dB, exploring only 22,400 codewords, whereas the search space contains 4.5 × 10l5 codewords. We define a new crossover operator that exploits domain-specific information and compare it with uniform and two-point crossover.

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