scholarly journals Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation

2019 ◽  
Vol 9 (15) ◽  
pp. 3022 ◽  
Author(s):  
AbdelMoniem ◽  
Gasser ◽  
El-Mahallawy ◽  
Fakhr ◽  
Soliman
Keyword(s):  
Neural Network ◽  
Channel Estimation ◽  
Multiple Access ◽  
Short Term Memory ◽  
Power Coefficient ◽  
Modulation Scheme ◽  
Channel Fading ◽  
Adaptive Coding ◽  
Time Frequency ◽  
Shift Keying

Non-orthogonal multiple access (NOMA) is the technique proposed for multiple access in the fifth generation (5G) cellular network. In NOMA, different users are allocated different power levels and are served using the same time/frequency resource blocks (RBs). The main challenges in existing NOMA systems are the limited channel feedback and the difficulty of merging it with advanced adaptive coding and modulation schemes. Unlike formerly proposed solutions, in this paper, we propose an effective channel estimation (CE) algorithm based on the long-short term memory (LSTM) neural network. The LSTM has the advantage of adapting dynamically to the behavior of the fluctuating channel state. On average, the use of LSTM results in a 10% lower outage probability and a 37% increase in the user sum rate as well as a maximal reduction in the bit error rate (BER) of 50% in comparison to the conventional NOMA system. Furthermore, we propose a novel power coefficient allocation algorithm based on binomial distribution and Pascal’s triangle. This algorithm is used to divide power among N users according to each user’s channel condition. In addition, we introduce adaptive code rates and rotated constellations with cyclic Q-delay in the quadri-phase shift keying (QPSK) and quadrature amplitude modulation (QAM) modulators. This modified modulation scheme overcomes channel fading effects and helps to restore the transmitted sequences with fewer errors. In addition to the initial LSTM stage, the added adaptive coding and modulation stages result in a 73% improvement in the BER in comparison to the conventional NOMA system.

scholarly journals Uplink Non-Orthogonal Multiple Access with Channel Estimation Errors for Internet of Things Applications

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 912
Author(s):  
Minjoong Rim ◽  
Chung Kang
Keyword(s):  
Internet Of Things ◽  
Channel Estimation ◽  
Communication Systems ◽  
Multiple Access ◽  
Base Station ◽  
Shared Resources ◽  
Estimation Errors ◽  
Channel Estimation Errors ◽  
Pilot Symbols ◽  
Shift Keying

One of the key requirements for next generation wireless or cellular communication systems is to efficiently support a large number of connections for Internet of Things (IoT) applications, and uplink non-orthogonal multiple access (NOMA) schemes can be used for this purpose. In uplink NOMA systems, pilot symbols, as well as data symbols can be superimposed onto shared resources. The error rate performance can be severely degraded due to channel estimation errors, especially when the number of superimposed packets is large. In this paper, we discuss uplink NOMA schemes with channel estimation errors, assuming that quadrature phase shift keying (QPSK) modulation is used. When pilot signals are superimposed onto the shared resources and a large number of devices perform random accesses concurrently to a single resource of the base station, the channels might not be accurately estimated even in high SNR environments. In this paper, we propose an uplink NOMA scheme, which can alleviate the performance degradation due to channel estimation errors.

Blind channel estimation of space-time Frequency-Shift Keying

2006 ◽  
Vol 23 (2) ◽  
pp. 277-281
Author(s):  
Yuanyuan Gao ◽  
Xiaoxin Yi ◽  
Zuping Qian ◽  
Xianbing Hu
Keyword(s):  
Channel Estimation ◽  
Frequency Shift ◽  
Space Time ◽  
Blind Channel Estimation ◽  
Frequency Shift Keying ◽  
Time Frequency ◽  
Blind Channel ◽  
Shift Keying

Evaluation of Machine Learning-Driven Automatic Modulation Classifiers Under Various Signal Models

2020 ◽  
Author(s):  
Pejman Ghasemzadeh ◽  
Subharthi Banerjee ◽  
Michael Hempel ◽  
Hamid Sharif ◽  
Tarek Omar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Communication Systems ◽  
Short Term Memory ◽  
Cognitive Radios ◽  
Wireless Channel ◽  
Support Vector ◽  
Modulation Scheme ◽  
Modulation Classification ◽  
Non Linear

Abstract Automatic Modulation Classification (AMC) is becoming an essential component in receiver designs for next-generation communication systems, such as Cognitive Radios (CR). AMC enables receivers to classify an intercepted signal’s modulation scheme without any prior information about the signal. This is becoming increasingly vital due to the combination of congested frequency bands and geographically disparate frequency licensing for the railroad industry across North America. Thus, a radio technology is needed that allows train systems to adapt automatically and intelligently to changing locations and corresponding RF environment fluctuations. Three AMC approaches have been proposed in the scientific literature. The performance of these approaches depends especially on the particular environment where the classifiers are employed. In this work, the authors present a performance evaluation of the Feature-based AMC approach, as this is the most promising approach for railroads in real-time AMC operations under various different wireless channel environments. This study is done as the first one for railroads application where it considers different environments models including Non-Gaussian Class A noise, Multipath fast fading, and their combination. The evaluation is conducted for signals using a series of QAM modulation schemes. The authors selected the signal’s Cumulant statistical features for the feature extraction stage in this study, coupled with three different machine learning classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN) and Recurrent Neural Network (RNN) utilizing long-short term memory (LSTM), in order to maintain control over the classifiers’ accuracy and computational complexity, especially for the non-linear cases. Our results indicate that when the signal model noise shows higher non-linear behavior, the RNN classifier on average achieves higher classification accuracy than the other classifiers.

scholarly journals Blind separation of PCMA signals based on neural network

2018 ◽  
Vol 173 ◽  
pp. 03045 ◽  
Author(s):  
WEI Chi ◽  
PENG Hua ◽  
QIU Zeyu
Keyword(s):  
Neural Network ◽  
Frequency Spectrum ◽  
Multiple Access ◽  
Single Channel ◽  
Blind Separation ◽  
Output Data ◽  
Time Frequency ◽  
Characteristic Data ◽  
Lower Complexity ◽  
Separation Model

For paired carrier multiple access (PCMA) signals, a new single-channel blind separation on neural network was proposed. Firstly, the sample waveforms (three symbols) which contains different bit information are constructed, secondly, the time-frequency spectrum of each sample under the different influences of the trailing symbols is Intercepted, finally, the characteristic data of the spectrum as the input data, and the two-bit sequence in each sample as the output data to be trained, network trains these data repeatedly to complete the construction of separation model. The receiver carries on window truncation to the time-frequency spectrum of PCMA signal, neural network recognize the characteristic data of these spectrums to realizes the separation of bit sequences. Experimental results show that this algorithm has lower complexity than PSP algorithm, and the accuracy of it is close to PSP algorithm (L=5).

scholarly journals High Order Chirp Rate Shift Keying Modulation Using the Fractional Fourier Transform

2017 ◽  
Vol 2 (4) ◽  
pp. 1 ◽  
Author(s):  
Seema Sud
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Wigner Distribution ◽  
High Order ◽  
Modulation Scheme ◽  
Chirp Rate ◽  
Bandwidth Utilization ◽  
Time Frequency ◽  
Rate Shift ◽  
Shift Keying

In this paper, we discuss an improved demodulation scheme using the Fractional Fourier Transform (FrFT) for a modulation scheme employing chirp rate shift keying (CrSK). CrSK in conjunction with the FrFT enable very high order, e.g. more than 32-ary modulation schemes to be achievable with good bit error rate (BER) performance, even in the absence of coding, thereby overcoming limitations of traditional schemes including phase shift keying (PSK) or QAM (quadrature amplitude modulation). By using an FrFT-based demodulator, we expand our demodulation degrees of freedom from a single (e.g. frequency) axis to an entire time-frequency domain, called the Wigner Distribution (WD). We show how the proposed demodulation scheme using the FrFT improves over past approaches by more than 7 dB, enabling us to achieve close to 4-ary performance with a 32-ary modulation scheme. This enables future systems to operate at 5 bits/s/Hz bandwidth efficiency, enhancing bandwidth utilization for future generation, high data rate, applications, such as internet.

scholarly journals Music Composition and Emotion Recognition Using Big Data Technology and Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yu Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Music Composition ◽  
Feature Recognition ◽  
Short Term Memory ◽  
Learning Ability ◽  
Time Frequency ◽  
Big Data Technology

To implement a mature music composition model for Chinese users, this paper analyzes the music composition and emotion recognition of composition content through big data technology and Neural Network (NN) algorithm. First, through a brief analysis of the current music composition style, a new Music Composition Neural Network (MCNN) structure is proposed, which adjusts the probability distribution of the Long Short-Term Memory (LSTM) generation network by constructing a reasonable Reward function. Meanwhile, the rules of music theory are used to restrict the generation of music style and realize the intelligent generation of specific style music. Afterward, the generated music composition signal is analyzed from the time-frequency domain, frequency domain, nonlinearity, and time domain. Finally, the emotion feature recognition and extraction of music composition content are realized. Experiments show that: when the iteration times of the function increase, the number of weight parameter adjustments and learning ability will increase, and thus the accuracy of the model for music composition can be greatly improved. Meanwhile, when the iteration times increases, the loss function will decrease slowly. Moreover, the music composition generated through the proposed model includes the following four aspects: sadness, joy, loneliness, and relaxation. The research results can promote music composition intellectualization and impacts traditional music composition mode.

scholarly journals Design of a SIMO Deep Learning-Based Chaos Shift Keying (DLCSK) Communication System

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 333
Author(s):  
Majid Mobini ◽  
Georges Kaddoum ◽  
Marijan Herceg
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Fading Channels ◽  
Communication Systems ◽  
Chaotic Maps ◽  
Short Term Memory ◽  
Reference Signal ◽  
Shift Keying ◽  
Input Multiple Output ◽  
Chaos Shift Keying

This paper brings forward a Deep Learning (DL)-based Chaos Shift Keying (DLCSK) demodulation scheme to promote the capabilities of existing chaos-based wireless communication systems. In coherent Chaos Shift Keying (CSK) schemes, we need synchronization of chaotic sequences, which is still practically impossible in a disturbing environment. Moreover, the conventional Differential Chaos Shift Keying (DCSK) scheme has a drawback, that for each bit, half of the bit duration is spent sending non-information bearing reference samples. To deal with this drawback, a Long Short-Term Memory (LSTM)-based receiver is trained offline, using chaotic maps through a finite number of channel realizations, and then used for classifying online modulated signals. We presented that the proposed receiver can learn different chaotic maps and estimate channels implicitly, and then retrieves the transmitted messages without any need for chaos synchronization or reference signal transmissions. Simulation results for both the AWGN and Rayleigh fading channels show a remarkable BER performance improvement compared to the conventional DCSK scheme. The proposed DLCSK system will provide opportunities for a new class of receivers by leveraging the advantages of DL, such as effective serial and parallel connectivity. A Single Input Multiple Output (SIMO) architecture of the DLCSK receiver with excellent reliability is introduced to show its capabilities. The SIMO DLCSK benefits from a DL-based channel estimation approach, which makes this architecture simpler and more efficient for applications where channel estimation is problematic, such as massive MIMO, mmWave, and cloud-based communication systems.

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