Novel Multilayer Extreme Learning Machine as a Massive MIMO Receiver for Millimeter Wave Communications

Carrera, Diego Fernando; Vargas-Rosales, César; Zabala-Blanco, David; Yungaicela-Naula, Noe M.; Azurdia-Meza, César A.; Marey, Mohamed; Firoozabadi, Ali Dehghan

doi:10.1109/access.2022.3178709

Cited by 6 publications

(5 citation statements)

References 58 publications

(115 reference statements)

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“…To design both the ELM receivers, activation function tanh is used [12], [13]. The input weights W and biases b follow uniform distribution within the interval [-0.01,0.01].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Contrary to the tuning mechanism-based ML algorithms, extreme learning machine (ELM) uses randomly assigned input weights and biases (that connect between the input layer and the hidden layer) and perform supervised learning with the help of training datasets by determining the output weights (connecting the hidden layer and the output layer), which is responsible for determining the desired output. In [12], [13], the ELM-based receiver systems are proposed which do not require any channel information. However, due to the limitation of modeling representation, some training inaccuracy may be introduced during the learning stage as ML approaches, including ELM, might not always reach the global minima with the given ML parameters.…”

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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“…To design both the ELM receivers, activation function tanh is used [12], [13]. The input weights W and biases b follow uniform distribution within the interval [-0.01,0.01].…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…The primary areas of emphasis for 3GPPP encompass several key aspects. These include the investigation of multi-beam formation techniques, the provision of extra-terrestrial coverage below 7 GHz, the enhancement of efficiency through antenna tuning systems in mobile devices, the expansion of spectrum allocation from 24.25 to 52.6 GHz, extending up to 71 GHz, the optimization of bandwidth allocation to support Internet of Things (IoT) devices at 20 MHz/100 MHz in emerging Sub-7 GHz/mmWave frequencies, and the development of mobile MIMO antenna systems [4] . The basic principle of MIMO technology is to use multiple antennas to transmit and receive signals simultaneously, which can increase the data rate and improve the reliability of the communication system.…”

Section: Introductionmentioning

confidence: 99%

Four port tri-circular ring MIMO antenna with wide-band characteristics for future 5G and mmWave applications

Munir,

Nasralla,

Esmail

2024

Heliyon

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“…O RTHOGONAL frequency division multiplexing (OFDM) has emerged as the strategy of choice for current and next-generation wireless standards due to its reliability in frequency-selective multipath circumstances and high spectrum efficiency utilization [1], [2]. It has been considered for use in the settings of cellular networks, including the fifth-generation and beyond systems, with its deployment in long term evolution standards [3]- [5]. It has also been effectively deployed in a wide range of other wireless technologies, including local area networks, and radars, microwave, and satellite systems [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Coded Assisted Transmit and Receive IQ Mismatch Compensation With Channel Estimation for AF Cooperative OFDM Systems

Marey

Mostafa

2023

IEEE Access

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Amplify-and-forward (AF) orthogonal frequency division multiplexing (OFDM) transmissions encounter a significant difficulty in the form of in-phase and quadrature phase (IQ) mismatch. Previous reports on this problem have solely been discussed in the context of uncoded transmissions. In addition, in these precedent studies, IQ equalization must be conducted following the estimation stage for accurate detection of data symbols. This research delves into the issue of the IQ mismatch between transmission and reception in AF-OFDM systems in the context of channel coding. We design a magnificent code-aided approach to predict the overall channel impulse responses (CIRs), which encompass the actual CIRs and IQ mismatch originating at the source, relay, and destination. Instead of using a collection of algorithms, the proposed approach can be utilized to estimate nine parameters simultaneously. Due to the impractical nature of the precise maximum-likelihood (ML) strategy to this situation, we instead utilize an expectation-maximization (EM) process as a low complexity strategy to predict the parameters under consideration. The suggested estimation approach uses an iterative process to improve predictions exploiting the a priori knowledge gained from the soft information supplied by the channel decoder. In addition, we demonstrate how to carry out data detection by making use of the estimated parameters. The simulation results verify the effectiveness of the proposed estimator and detector for usage in real-world settings, with superiority over the conventional ones.

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Novel Multilayer Extreme Learning Machine as a Massive MIMO Receiver for Millimeter Wave Communications

Cited by 6 publications

References 58 publications

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

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

Four port tri-circular ring MIMO antenna with wide-band characteristics for future 5G and mmWave applications

Coded Assisted Transmit and Receive IQ Mismatch Compensation With Channel Estimation for AF Cooperative OFDM Systems

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