RNN Based MIMO Channel Prediction

Potter, Chris

doi:10.1007/978-3-642-12869-1_8

Cited by 11 publications

(10 citation statements)

References 23 publications

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“…3. Another related work cited in [17] improves the effort reported in [16] by using an on-line approach. A new hybrid PSO-EA-DEPSO algorithm is presented for training a RNN for MIMO channel prediction.…”

Section: Application Of Recurrent Neural Network (Rnn) In Wireless Comentioning

confidence: 91%

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Application of Soft Computing Tools in Wireless Communication—A Review

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

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The proliferation of number of users in a limited wireless spectrum have raised the levels of inter symbol interference (ISI) and have also contributed towards probable degradation of quality of service (QoS). The key challenges faced by upcoming wireless communication systems is to provide high-data-rate wireless access with better QoS. Also, the fast shrinking spectrum for such communication have necessitated the development of methods to increase spectral efficiency. Multiple input multiple output (MIMO) wireless technology is a viable option in such a situation and is likely to be able to meet the demands of these ever-expanding mobile networks. Many researchers have explored this field over a considerable period of time. A sizable portion of the research have been on the application of traditional statistical methods in such areas. Over the years, soft computational tools like artificial neural network (ANN), fuzzy systems and their combinations have received attention in the diverse segments of wireless communication. This is because of the fact that these are learning based systems. These learn from the environment, retain the knowledge and use it subsequently. This paper highlights some of the important application areas in wireless communication which have reported the use of soft computing tools in wireless communication that are in circulation in open literature.

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Section: Application Of Recurrent Neural Network (Rnn) In Wireless Comentioning

confidence: 91%

“…RNNs have also been used for a diverse range of applications in wireless communication. Some of the relevant literature are cited between [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Application Of Recurrent Neural Network (Rnn) In Wireless Comentioning

confidence: 99%

Application of Soft Computing Tools in Wireless Communication—A Review

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

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“…As a burgeoning optimizer, DEPSO has shown its prominent advantage and prosperity, which are witnessed by the diversity of DEPSO variants and their applications [28], [72]- [126]. Besides, DEPSO has been further hybridized with other optimizers, giving birth to more complicated hybrids [127]- [129]. In the past decade, many scholars have made contributions to DEPSO research.…”

Section: Previous Depsosmentioning

confidence: 99%

“…DEPSO-ZX has been successfully applied in multimodal image registration [74], modeling of gene regulatory networks [75], structure optimization of a high-temperature superconducting cable [76], and design of finite-impulse response filters [77]. Besides, DEPSO-ZX was even further hybridized with PSO and ES to form a more complicated hybrid [128], [129]. In [78], Moore and Venayamoorthy proposed a DEPSO (namely DEPSO-MV), which shares the same idea with DEPSO-ZX, but its DE and PSO parents are DE/rand/2/bin and a modified PSO with "Ring" topology, respectively.…”

Section: Previous Depsosmentioning

confidence: 99%

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Xin

Chen

Zhang

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

162

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Abstract-Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, operating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other representative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify different hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploitation regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future.

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“…PSO has been shown to be very effective in optimizing challenging multidimensional, nonlinear and multimodal problems in a variety of fields such as signal processing [20][21][22][23], communication networks [24], biomedical [25,26], control [27,28], robotics [29], power systems [30], electromagnetics [31], image and video analysis [32,33]. It was inspired by the social behavior of animals, specifically the ability of groups of animals to work collectively in finding the desirable positions in a given area.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multipath channel identification by using global optimization in ambiguity function domain

Güldoǧan

Arıkan

2011

Signal Processing

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a b s t r a c tA new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.

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RNN Based MIMO Channel Prediction

Cited by 11 publications

References 23 publications

Application of Soft Computing Tools in Wireless Communication—A Review

Application of Soft Computing Tools in Wireless Communication—A Review

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Multipath channel identification by using global optimization in ambiguity function domain

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