A Novel Adaptive Optimized Fast Blind Channel Estimation for Cyclic Prefix Assisted Space–Time Block Coded MIMO-OFDM Systems

Nandi, Shovon; Pathak, Narendra Nath; Nandi, Arnab

doi:10.1007/s11277-020-07629-z

Cited by 29 publications

(10 citation statements)

References 25 publications

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“…The proposed classifier performs effectively in the presence of unknown frequency, timing, and phase offsets without prior knowledge of channel state information. Nandi et al 35 introduced an adaptive optimized, fast blind channel estimate utilizing a cyclic prefix supported by (STBC‐MIMO‐OFDM) system to tackle high complexity and poor convergence, which are important difficulties in multicarrier communications. O'Shea et al 36 established deep learning‐based radio signal classification for radio communications signals by considering a rigorous baseline method using higher order moments and strongly boosted gradient tree classification.…”

Section: Related Workmentioning

confidence: 99%

Blind modulation classification in multiple input and output‐orthogonal frequency division multiplexing using time‐frequency analysis and customized convolutional neural network architecture

PramodKumar

KiranKumar

2023

Trans Emerging Tel Tech

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MIMO with OFDM is a powerful communication technology in both army and civilian environments. Because there is no prior knowledge of carrier state information or signal overlapping in MIMO‐OFDM systems, outdated probability and feature‐based approaches can be applied. However, these methods cannot easily be used to provide the channel characteristics. Blind modulation categorization is a critical stage in deploying 5G networks. The issue of blind modulation classification for a multiple input and output (MIMO) antenna system using orthogonal frequency division multiplexing (OFDM) is investigated in this research. The independent component analysis (ICA) method has been used to separate the MIMO modulated signal, and the smoothed pseudo Wigner‐Ville distribution (SPWVD) with a Hamming window of 0.2 s has been used to analyze the time‐frequency characteristics of time‐domain modulated signals to get around the challenge of blind attenuation categorization in MIMO‐OFDM arrangements. The recovered time‐frequency characteristics are then transformed into red‐green‐blue (RGB) spectrogram pictures and a perfect‐tweaked customized CNN in which CNN (convolutional neural network) is integrated with LSTM (long short term memory) is used to categorize modulation kinds based on the illustrations of RGB spectrograms. Finally, a module for merging decisions based on majority voting was employed to combine the categorization findings of all receiving antennas. As a result, a customized CNN‐based blind modulation technique outperforms existing works to find the exact modulation type in the MIMO‐OFDM communication.

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Section: Related Workmentioning

confidence: 99%

Blind modulation classification in multiple input and output‐orthogonal frequency division multiplexing using time‐frequency analysis and customized convolutional neural network architecture

PramodKumar

KiranKumar

2023

Trans Emerging Tel Tech

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“…Shovon Nandi [17] presented a Novel Adaptive Blind Channel Estimation for Cyclic Prefix Coded MIMO-OFDM System. This method was implemented to support multiple users at the same time over the same frequency band.…”

Section: Chittettimentioning

confidence: 99%

Blind Channel Estimation Using Enhanced Independent Component Analysis for MIMO-OFDM System

2022

IJIES

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Blind Source Separation (BSS) is a process of separating a set of source signals from mixed-signal without the help of information of source signals. In some noisy acoustic surroundings, instinctive class detection is completely dependent on vocalization which remains a stimulating task. To identify the definite classes easily, the source signals have to be detached from the mixed signals and this separation procedure is considered as a substantial pre-processing phase before the detection procedure takes place. This research mainly focuses on the issues of BSS in bio-acoustic mixed signals. Independent Component Analysis (ICA) is current technique in the area of BSS that can discrete the mixed-signal and also utilizes Negentropy as its objective function. However, this method is penetrating to the separation matrix and it cannot diverge. So, the bootstrap ICA procedures with Fast and Robust Bootstrap (FRB) method is developed which is applicable for all the signals. The quality of separated source signals using Enhanced-ICA and other algorithms are compared and evaluated according to MATLAB toolbox metrics. The results show that Enhanced-ICA with negentropy is used for finding a maximum non-gaussianity which achieves the BER performances of 0.00019 which is better than existing Discrete Wavelet Transform based BSS (DWT-BSS) and Modified Newton with Improved Animal Migration Optimization (MN-IAMO).

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“…STBC is a MIMO technique where multiple copies of a data stream are sent via the multiple transmit antennas and then exploiting the many receive antennas to receive the different versions of the signal which improves the reliability of data transferred [4]. STBC combines all the received versions in an optimal way to extract maximum diversity from all the received signals.…”

Section: A Technical Reviewmentioning

confidence: 99%

Hybrid MIMO: A New Transmission Method For Simultaneously Achieving Spatial Multiplexing and Diversity Gains in MIMO Systems

Lemayian

Hamamreh

2021

RS Open Journal on Innovative Communication Technologies

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This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under project grant No. 119E392. All the codes used can be found at researcherstore.com ABSTRACT Multiple input multiple output (MIMO) technology has evolved over the past few years into a technology with great potential to drive the direction of future wireless communications. MIMO technology has become a solid reality when massive MIMO systems (MIMO with large number of antennas and transceivers) were commercially deployed in several countries across the world in the recent past. Moreover, MIMO has been integrated into state-of-the-art paradigms such as fifth-generation (5G) networks as one of the main enabling technologies. MIMO possesses many attractive and highly desirable properties such as spatial multiplexing, diversity gains, and adaptive beamforming gains that leads to high data rates, enhanced reliability, and other enhancements. Nevertheless, beyond 5G technologies demand wireless communication systems with, among other properties, immensely higher data rates and better reliability simultaneously at the same time. In this work, a new, novel MIMO technique for simultaneously achieving multiplexing and diversity gains as well as completely eliminating any processing at the MIMO receiver, leading to advantages such as low complexity and low power consumption, is proposed. The proposed technique employs the design of interference-canceling matrices, which are calculated from the channels between the transceiver antennas, where the matrices are employed at the base station to help achieve multiplexing and diversity gains simultaneously. The novelty and efficiency of the introduced paradigm is demonstrated via mathematical models and validated by Monte Carlo simulations. Results indicate that the proposed system outperforms conventional MIMO models.INDEX TERMS Multiple input multiple output, MIMO, Massive MIMO, internet of things (IoT), low power, low complexity, spatial multiplexing, diversity gain.

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A Novel Adaptive Optimized Fast Blind Channel Estimation for Cyclic Prefix Assisted Space–Time Block Coded MIMO-OFDM Systems

Cited by 29 publications

References 25 publications

Blind modulation classification in multiple input and output‐orthogonal frequency division multiplexing using time‐frequency analysis and customized convolutional neural network architecture

Blind modulation classification in multiple input and output‐orthogonal frequency division multiplexing using time‐frequency analysis and customized convolutional neural network architecture

Blind Channel Estimation Using Enhanced Independent Component Analysis for MIMO-OFDM System

Hybrid MIMO: A New Transmission Method For Simultaneously Achieving Spatial Multiplexing and Diversity Gains in MIMO Systems

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