MIMO-OFDM Channel Estimation Using ANFIS

Seyman, Muhammet Nuri; Taşpınar, Necmi

doi:10.5755/j01.eee.120.4.1458

Cited by 14 publications

(12 citation statements)

References 13 publications

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“…The ability to tackle the nonlinear classification issue by combining numerous superposition perceptions is one of the communication-related uses for neural networks. [26][27][28][29][30][31][32] Seyman et al 26 propounded a pilot training-based CE technique using the adaptive neuro-fuzzy inference system (ANFIS) algorithm. Further, the same CE technique had been introduced for multiple-input multiple-output (MIMO) OFDM systems.…”

Section: Literature Surveymentioning

confidence: 99%

A new optimized least‐square sparse channel estimation scheme for underwater acoustic communication

Kumar

2023

Int J Communication

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Summary In underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)‐CE algorithm along with polynomial interpolated black widow optimization (PI‐BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA‐OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL‐TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference‐free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal‐to‐noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.

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Section: Literature Surveymentioning

confidence: 99%

A new optimized least‐square sparse channel estimation scheme for underwater acoustic communication

Kumar

2023

Int J Communication

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“…Although no work has been performed related to channel estimation using the adaptive neuro fuzzy inference system (ANFIS) for OFDM-IDMA system, there are some studies in which the ANFIS and the other heuristic methods are applied to estimate channel frequency responses for single-input single-output (SISO) and multiple-input multiple-output (MIMO) OFDM systems [15][16][17][18][19]. One study was conducted to estimate channel frequency responses in the OFDM-IDMA system by using a radial basis function neural network (RBFNN), which is one of the heuristic methods used for channel estimation [20].…”

Section: Introductionmentioning

confidence: 99%

“…In [15], an estimator based on a backpropagation neural network with three layers was proposed for SISO-OFDM transmission technology and the system performance was evaluated by using bit error rate (BER) and mean square error (MSE) graphs. In [16] and [17], an ANFIS-based channel estimator was offered for MIMO and SISO OFDM, respectively, and a comparison was made with conventional estimation algorithms such as LS and MMSE with regards to performance and complexity. In [18], a multilayered perceptron (MLP)-based estimator trained by the Levenberg-Marquardt algorithm was proposed for the MIMO-OFDM system, and in [19], a RBFNN was utilized to build a channel estimator for the same system.…”

Section: Introductionmentioning

confidence: 99%

Channel estimation using an adaptive neuro fuzzy inference system in the OFDM-IDMA system

Taşpınar¹,

Şimşir²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a channel estimator based on an adaptive neuro fuzzy inference system (ANFIS) is proposed for the purpose of estimating channel frequency responses in orthogonal frequency division multiplexing-interleave division channel estimator based on ANFIS shows better performance than both the LS algorithm and the other considered heuristic methods like MLP-BP, MLP-LM, and RBFNN, whereas the MMSE algorithm still shows the best performance as expected because of exploiting channel statistics and noise information, which makes it very complex to be used in any system. As well as being less complex compared to the MMSE algorithm, the estimator based on ANFIS does not need pilot tones for channel estimation. These properties bring our proposed method to an advantageous position among the other estimation techniques.

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“…Previously, we can see that ST and SF cannnot achieve maximum diversity but after a few years, there are improvement of performance. They can be used in MIMO system with a few changes that need to be considered but the performance is still not as good as STF [6].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of CLS-DFE for MIMO OFDMA System

Sulong¹

2016

JACN

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Orthogonal frequency division multiple access (OFDMA) is a multiuser of digital modulation technique used to increase wireless transmission rate. Signal efficiency is distorted due to Inter symbol interference (ISI) that occurs due to the multipath propagation. Cascaded Least Square (CLS) with Decision Feedback Equalizer (DFE) had been used in this research in order to mitigate ISI in OFDMA system. CLS is combination of two adaptive algorithms which are Least Mean Square (LMS) and Recursive Least Square (RLS). This research also justified that combination of space time frequency block code (STFBC) with multiple input multiple output (MIMO) antenna enable to minimize ISI. The simulation results show the improvement of bit error rate (BER) performance after reducing ISI and also prove that STFBC was able to achieve maximum diversity order in the MIMO OFDMA system. Index Terms-Least mean square (LMS), recursive least square (RLS), cascaded least square (CLS), decision feedback equalizer (DFE). Siti Maisurah Sulong received her B.Eng. (Hon.) degree in communication from Universiti Teknologi MARA (UiTM), Shah Alam in 2014. She currently pursues her M.Sc. degree (electrical) in UiTM. Her research interests include wireless telecommunication, OFDMA for the WiMax, adaptive algorithm, equalizers and diversity scheme. Azlina Idris received her B.Eng. (Hon.) degree from Leeds Metropolitan University and M.Sc. degree from Universiti Teknologi Malaysia. She completed her PhD degree in Universiti Malaya and now holds position as a senior lecturer in UiTM Shah Alam. Besides that, she is also the coordinator for student employability. Her research interests include pair wise error probability in OFDM, diversity scheme, wireless interference and equalizers. Suzi Seroja Sarnin received her B.Eng. (Hon) degree from Universiti Teknologi Malaysia (UTM), Malaysia in 1999 and M.Sc. (microelectronic), degree from Universiti Kebangsaan Malaysia (UKM), Malaysia in 2006 respectively. She currently pursues her PhD degree in UiTM. Her research interests include channel estimation, coding algorithm for CDMA and WCDMA, mobile telecommunication, synchronization algorithm in OFDM for the Wimax technology application.

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MIMO-OFDM Channel Estimation Using ANFIS

Cited by 14 publications

References 13 publications

A new optimized least‐square sparse channel estimation scheme for underwater acoustic communication

A new optimized least‐square sparse channel estimation scheme for underwater acoustic communication

Channel estimation using an adaptive neuro fuzzy inference system in the OFDM-IDMA system

Performance Evaluation of CLS-DFE for MIMO OFDMA System

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