Uplink NOMA signal transmission with convolutional neural networks approach

Lin, Chuan; Chang, Qing; Li, Xianxu

doi:10.23919/jsee.2020.000068

Cited by 21 publications

(9 citation statements)

References 9 publications

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“…The reference symbols Y γ,k , if were used in (8), then the expression in (9) would provide an estimated output Xγ . According to (10), the error between the actual reference symbols and the ELM equalized symbols could be written as…”

Section: Residual Compensation-based Elm (Rc-elm) Receiver For Mimo-nomamentioning

confidence: 99%

“…DNN based precoding and decoding approaches are proposed in [9] for MIMO-NOMA system with successive interference cancellation (SIC) scheme. Uplink signal detection for MIMO-NOMA is performed by means of convolutional neural networks (CNN) in [10]. The iterative soft-thresholding technique is the foundation of the adaptive DL-based receiver architecture known as MMNet [11].…”

Section: Introductionmentioning

confidence: 99%

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Residual Compensation-Based Extreme Learning Machine for MIMO-NOMA Receiver

et al. 2023

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Extreme learning machine (ELM) uses a simple machine learning (ML) architecture that allows its implementation for smart IoT network operations. However, some inaccuracy lies between the predicted and the actual data during the ELM training, which can be caused due to the limitation of the modeling representation. This paper thus investigates a residual compensation-based ELM (RC-ELM) for its application in designing a receiver for MIMO-NOMA aided IoT systems. In RC-ELM, the base ELM layers determine the relationship between the transmitted and received data and the additional layers of the RC-ELM attempt to compensate the error introduced during the training mechanism. The analysis of the appropriate number of compensation layers for training error minimization is conducted on the basis of the bit error rate (BER) and the error vector magnitude (EVM) performances of the RC-ELM training. A minimum BER improvement of 5% for user 1 and 18% for user 2 is shown with with the aid of RC-ELM for a two-IoT user instance. The EVM is marginally increased by 0.0008% for user 1 and 1.61% for user 2 in training stage. Besides, the RC-ELM receiver is also compared to the minimum mean square error (MMSE), the classic ELM and the trained multilayer perceptron (MLP) receivers in terms of BER and EVM. Both of the ELM receivers show improved performances with respect to the other receivers.INDEX TERMS IoT, MIMO-NOMA, machine learning, extreme learning machine, RC-ELM, multilayer perceptron.

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Section: Residual Compensation-based Elm (Rc-elm) Receiver For Mimo-nomamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Residual Compensation-Based Extreme Learning Machine for MIMO-NOMA Receiver

et al. 2023

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“…Using DL schemes such as CNNs, the authors of [34] reconstruct uplink users' MIMO channel signals. Data from several users may potentially be decoded instantly using the suggested technique which does not rely on any of the typical processes found in communication signal processing.…”

Section: Related Workmentioning

confidence: 99%

“…The same approach can be used to represent all users' pilot signals at any frame [34], [35], [43], [44], [46], [47]:…”

Section: System Modelmentioning

confidence: 99%

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

Shankar¹,

Chaudhar²,

Mishra³

2022

JTIT

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In this paper, the performance of a deep learning based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Further more, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results.

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“…In downlink NOMA scenarios, all the individual information signals are superimposed on a single signal transmitted by the base station (BS) to the equipment users. For the decoding processes, most research works consider using successive interference cancellation (SIC) techniques in receiving devices to separate signals in the power domain for each user [12][13][14]. Studies in the literature indicate that a key parameter for good NOMA performance is the correct power allocation per user [4,15].…”

Section: Introductionmentioning

confidence: 99%

Bit Error Rate Analysis of NOMA-OFDM in 5G Systems With Non-Linear HPA With Memory

et al. 2021

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The orthogonal frequency division multiplexing (OFDM) and the non-orthogonal multiple access (NOMA) scheme are presented as promising techniques to meet the requirement of fifth-generation (5G) communication systems. Although much attention has recently been devoted to study these techniques, some scenarios have still been less explored. Considering that a fundamental part of any communication system is the use of power amplifiers, this paper presents an analytical evaluation of the bit error rate (BER) of NOMA-OFDM systems in the presence of a high power amplifier (HPA) with memory. Considering that the non-linear distortions generated by the HPA can be modeled using a polynomial model with memory, new theoretical expressions are developed to obtain the BER of the system. Specifically, exact BER expressions for a downlink NOMA-OFDM system with two users are presented and verified by Monte Carlo simulation results. The obtained numerical results demonstrate that the performance degradation of both users is highly dependent on the non-linear distortions, even when the successive interference cancellation (SIC) technique is performed perfectly.INDEX TERMS 5G, bit error rate, high power amplifier, non-linearity, non-orthogonal multiple access, orthogonal frequency division multiplexing.

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Uplink NOMA signal transmission with convolutional neural networks approach

Cited by 21 publications

References 9 publications

Residual Compensation-Based Extreme Learning Machine for MIMO-NOMA Receiver

Residual Compensation-Based Extreme Learning Machine for MIMO-NOMA Receiver

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

Bit Error Rate Analysis of NOMA-OFDM in 5G Systems With Non-Linear HPA With Memory

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