Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC

Yang, Ruike; Zhang, Weitao; Lou, Shun-Tian

doi:10.1155/2020/8396930

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…When used in conjunction with centroid reconstruction and initialization methodologies, the proposed MC classifier minimizes the number of parameters to be estimated while also assisting in the initialization of centroids for improved EM algorithm convergence. Yang et al 32 proposed a parallel investigation‐based dynamic blind bandwidth prediction model. The authors used a window with exponential growth to weigh the preceding data, allowing us to use a balanced least squares requirement to generate the cost function.…”

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 Because there is no prior knowledge of carrier state information or signal overlapping in MIMO‐OFDM systems, outdated probability and feature‐based approaches can be applied.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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“…Applications of OTF abound. They include unveiling the topology of evolving networks [70], spatio-temporal prediction or image in-painting [41], multiple-input multiple-output (MIMO) wireless communications [13], [71], brain imaging [72], monitoring heart-related features from wearable sensors for multi-lead electro-cardiography (ECG) [73], anomaly detection in networks and topic modeling [16], structural health monitoring (in an internet of things (IoT) context) [36], online cartography (spectrum map reconstruction in cognitive radio networks) [14], detection of anomalies in the process of 3D printing [74], data traffic monitoring in networks [10], [16], cardiac MRI [10], stream data compression (e.g., in power distribution systems [75] or in video [76]), and online completion [10], [77], [78], among others.…”

Section: Related Workmentioning

confidence: 99%

Online Rank-Revealing Block-Term Tensor Decomposition

Rontogiannis¹,

Kofidis²,

Giampouras³

2021

Preprint

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The so-called block-term decomposition (BTD) tensor model, especially in its rank-(Lr, Lr, 1) version, has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of block components of rank higher than one, a scenario encountered in numerous and diverse applications. Its uniqueness and approximation have thus been thoroughly studied. The challenging problem of estimating the BTD model structure, namely the number of block terms (rank) and their individual (block) ranks, is of crucial importance in practice and has only recently started to attract significant attention. In data-streaming scenarios and/or big data applications, where the tensor dimension in one of its modes grows in time or can only be processed incrementally, it is essential to be able to perform model selection and computation in a recursive (incremental/online) manner. To date there is only one such work in the literature concerning the (general rank-(L, M, N )) BTD model, which proposes an incremental method, however with the BTD rank and block ranks assumed to be a-priori known and time invariant. In this preprint, a novel approach to rank-(Lr, Lr, 1) BTD model selection and tracking is proposed, based on the idea of imposing column sparsity jointly on the factors and estimating the ranks as the numbers of factor columns of nonnegligible magnitude. An online method of the alternating iteratively reweighted least squares (IRLS) type is developed and shown to be computationally efficient and fast converging, also allowing the model ranks to change in time. Its time and memory efficiency are evaluated and favorably compared with those of the batch approach. Simulation results are reported that demonstrate the effectiveness of the proposed scheme in both selecting and tracking the correct BTD model.

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“…Linear prediction approach is used in [19] for noise reduction in speech signals by employing Wiener filter. Other blind estimation works adopt techniques like minimum mean square error in [2], decision-directed maximum a posteriori probability in [20], parallel factor analysis (PARFAC) in [21] and Kalman filter in [22], [23]. Semi-blind channel estimation techniques in [24]- [26] use superimposed pilots on the data subcarriers after encoding the data through a spreading matrix to avoid loss of spectral efficiency.…”

mentioning

confidence: 99%

Pilot based channel estimation improvement in orthogonal frequency-division multiplexing systems using linear predictive coding

Saleh Idan,

Al-Haddad

2024

IJECE

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Pilot based least square (LS) channel estimation is a commonly used channel estimation technique in orthogonal frequency-division multiplexing based systems due to its simplicity. However, LS estimation does not handle the noise effect and hence suffers from performance degradation. Since the channel coefficients are correlated in time and hence show a slower variation than the noise, it is possible to encode the channel using linear predictive coding (LPC) without the noise. In this work, the channel is estimated from the pilots using LS estimation and in a second step the channel’s LS estimated is encoded as LPC coefficients to produce an improved channel estimation. The estimation technique is simulated for space-time block coding (STBC) based orthogonal frequency-division multiplexing (OFDM) system and the bit error rate (BER) curves show improvement of the LPC estimation over the LS estimation of the channel.

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Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC

Cited by 4 publications

References 27 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

Online Rank-Revealing Block-Term Tensor Decomposition

Pilot based channel estimation improvement in orthogonal frequency-division multiplexing systems using linear predictive coding

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