Fast Convergent LMS Adaptive Receiver for MC-CDMA Systems with Space-Time Block Coding

Seo, Bangwon; Ahn, Woogeun; Jeong, Cheol; Kim, Hyung-Myung

doi:10.1109/lcomm.2010.08.100679

Cited by 13 publications

(16 citation statements)

References 8 publications

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“…In proposed FLeABPNN, the learning rate of the backpropagation neural network is updated using fuzzy logic instead of using a constant learning rate. This improves the performance of the MIMO based MC-CDMA system as compared to the conventional LMS [28,29] & GA based suboptimum receiver [16,33] in terms of Convergence rate & Minimum Mean Square Error. Computational complexity is another challenging issue in modern communication [38][39][40][41][42].…”

Section: • Training Based Methodsmentioning

confidence: 99%

“…Then taking the M-point Fast Fourier Transforms (FFT) after converting the data into serial to parallel. Finally, the received signal vector in the frequency domain is written as [16,28,29,33]:…”

Section: System Modelmentioning

confidence: 99%

“…In the Mean Square Error (MSE) based receiver for MC-CDMA system with Alamouti's STBC achieved by using the following problem [16,28,29,33].…”

Section: System Modelmentioning

confidence: 99%

“…The rate of convergence is increased by updating the weight of vectors. The improved MMSE based cost function can be written as [16,28,29,33]:…”

Section: System Modelmentioning

confidence: 99%

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Multi user detection using fuzzy logic empowered adaptive back propagation neural network

Abbas¹,

Khan²,

Ata³

et al. 2019

NNW

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In Wireless communication, Multiple Input and Multiple Output (MIMO) systems have always been quite popular. Multicarrier systems are established along with different techniques of space-time coding to accomplish the demands of these systems. One of the most popular techniques is Multi-Carrier Code Division Multiple Access (MC-CDMA) with Alamouti's Space-Time Block Codes (STBC). This article, proposed the Fuzzy Logic empowered Adaptive Back Propagation Neural Network (FLeABPNN) based Multi User Detection (MUD) system, which is used to determine the receiver weights of MC-CDMA with the scheme of two variations. The proposed FLeABPNN approach takes advantage of a neuro-fuzzy hybrid system which conglomerates the competences of both fuzzy logic and neural networks for multi-user detection. It is observed that due to the fuzzy logic-based learning rate, proposed FLeABPNN based receiver without relationship & with relationship achieved the 3.04 × 10 −06 and 2.05 × 10 −06 Bit Error Rate (BER) respectively. The proposed FLeABPNN based receiver gives fast convergence rate & low BER as compared to other suboptimal published techniques like GA & LMS. It also observed that the Computational Complexity of the proposed FLeABPNN based MC-CDMA receiver is less then LMS based receiver up to 18 users, but higher than GA based receiver.

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Section: • Training Based Methodsmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

See 2 more Smart Citations

Multi user detection using fuzzy logic empowered adaptive back propagation neural network

Abbas¹,

Khan²,

Ata³

et al. 2019

NNW

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show abstract

“…The received signal vector for first two consecutive symbols is The cost function give in (9) and (10) is further improved in [7]. There was a simple relationship drawn between optimal weight vectors , and , .…”

Section: Mmse Based Cost Functionmentioning

confidence: 99%

Island genetic algorithm based MUD for MC-CDMA system

Umair

Khan

Choudry

2015

2015 International Conference on Information and Communication Technologies (ICICT)

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Multi Carrier systems like Multicarrier CodeDivision Multiple Access (MC-CDMA) systems are effective solution for fulfilling high data rate demand of future networks. The Alamouti's space time coding is employed on MC-CDMA system. In this paper, Island Genetic Algorithm (IGA) is applied to MC-CDMA receiver in order to find out the weight vectors. This receiver is studied for Fast Fourier transform (FFT) and Slantlet Transform (SLT) separately. We find out that IGA technique gives attractive BER up to medium SNR in both SLT and FFT based MC-CDMA system. Moreover, the SLT based systems performed quiet well.

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Blind RLS-Based Detection Using GSC with DF for STBC MC-CDMA Systems Under Mismatched Channel Estimation

Jen

2014

Wireless Pers Commun

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Fast Convergent LMS Adaptive Receiver for MC-CDMA Systems with Space-Time Block Coding

Cited by 13 publications

References 8 publications

Multi user detection using fuzzy logic empowered adaptive back propagation neural network

Multi user detection using fuzzy logic empowered adaptive back propagation neural network

Island genetic algorithm based MUD for MC-CDMA system

Blind RLS-Based Detection Using GSC with DF for STBC MC-CDMA Systems Under Mismatched Channel Estimation

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