Multiuser detection with neural network and PIC in CDMA systems for AWGN and Rayleigh fading asynchronous channels

Işik, Yalçın; Taşpınar, Necmi

doi:10.1007/s11277-007-9293-0

Cited by 19 publications

(9 citation statements)

References 21 publications

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“…There are numerous strategies like Neural Network [25][26][27]41], Genetic Algorithm (GA) [31][32][33] Differential Equation (DE), Cooperative Co-Evolutionary (CC) Algorithms [34], Particle Swarm Optimization (PSO) [40], Maximum Likelihood (ML) [5,6], Partial Opposite Mutant Particle Swarm Optimization (POMPSO), Total Opposite Mutant Particle Swarm Optimization (TOMPSO) [35][36][37], Island GA, Differential Equation (DE) and Island DE has been proposed which further enhance the performance of the 5-th generation communication network [20,38,39,41,42].…”

Section: • Training Based Methodsmentioning

confidence: 99%

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%

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

Abbas¹,

Khan²,

Ata³

et al. 2019

NNW

View full text Add to dashboard Cite

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“…Since the marginal distribution of the j th sample of the k th symbol is Poisson distributed according to (15), given the model parameters Θ pois , we have…”

Section: A the Viterbi Detectormentioning

confidence: 99%

“…This is because, given the model parameters as well as the current symbol and the previous M symbols, the samples within the current bit interval are generated independently and distributed according to (15). Note that (16) that can transition to V k+1,u :…”

Section: A the Viterbi Detectormentioning

confidence: 99%

“…Some examples of machine learning tools applied to design problems in communication systems include multiuser detection in code-division multiple-access (CDMA) systems [12]- [15], decoding of linear codes [16], design of new modulation and demodulation schemes [17], [18], detection and channel decoding [19]- [24], and estimating channel model parameters [25], [26]. A recent survey of machine learning techniques applied to communication systems can be found in [27].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Detection of Data Sequences in Communication Systems

Farsad

Goldsmith

2018

IEEE Trans. Signal Process.

320

155

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We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models. Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI). In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in realtime as the signal stream arrives at the receiver. We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems. In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically. We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed. Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration.

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“…Motivated by the recent success of deep learning in speech and image processing, where modeling can be difficult, we consider using deep learning in design and analysis of communication systems [6,7,8]. Some examples of machine learning tools applied to design problems in communication systems include multiuser detection in code-division multiple-access (CDMA) systems [9,10,11,12], decoding of linear codes [13], design of new modulation and demodulation schemes [14], and estimating channel model parameters [15]. Most previous works have used machine learning to improve one component of the communication system based on the knowledge of the underlying channel models.…”

Section: Introductionmentioning

confidence: 99%

Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems

Farsad

Goldsmith

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel. However, in some systems, such as molecular communication systems where chemical signals are used for transfer of information, the underlying channel models are unknown. In these scenarios, a completely new approach to design and analysis is required. In this work, we focus on one important aspect of communication systems, the detection algorithms, and demonstrate that by using tools from deep learning, it is possible to train detectors that perform well without any knowledge of the underlying channel models. We propose a technique we call sliding bidirectional recurrent neural network (SBRNN) for real-time sequence detection. We evaluate this algorithm using experimental data that is collected by a chemical communication platform, where the channel model is unknown and difficult to model analytically. We show that deep learning algorithms perform significantly better than a detector proposed in previous works, and the SBRNN outperforms other techniques considered in this work.

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Multiuser detection with neural network and PIC in CDMA systems for AWGN and Rayleigh fading asynchronous channels

Cited by 19 publications

References 21 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

Neural Network Detection of Data Sequences in Communication Systems

Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems

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