Semi-blind MIMO-OFDM channel estimation based on ICA and Pilot Carriers

Xu, Chunlan; Li, Hong; Chang, Shuai

doi:10.1109/icspcc.2011.6061601

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…Similarly to the PCA, the ICA has been studied in the context of OFDM and MIMO-OFDM systems channel estimation [218][219][220][221]. The ICA application enables blind signal separation, supporting blind equalizers design based on iterative layered space-time equalization [218] or MMSE and layered space-frequency equalization to enhance the system performance [219].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…The proposal combining wavelet transformation and ICA is presented in [220], which is similar to the approach discussed in [217] for PCA. In [221], a semi-blind channel estimation strategy integrates ICA with pilot carriers. The pilots allow obtaining initial channel estimation that serves as the input data to the ICA algorithm.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

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Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Section: Dimensionality Reductionmentioning

confidence: 99%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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“…For 2x2 Alamouti MIMO-OFDM System, channel estimation coefficients in the receiver are obtained as shown in formulas (6)(7)(8)(9).…”

Section: Mimo-ofdm Channelmentioning

confidence: 99%

“…In semiblind channel estimation algorithms both small number of pilot bits and statistical properties of channel are used. In literature there are numerous channel estimation algorithms for example ICA [6,7], singular value decomposition (SVD) [8], subspace methods [9] All algorithms have advantages and disadvantages. But in semi-blind channel estimation, more bandwidth efficiency are obtained than pilot based channel estimation because of less pilot usage.…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation Techniques for MIMO-OFDM Systems over Frequency Selective Fading Channels

Ercan

2016

International Journal of Applied Mathematics, Electronics and Computers

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Wireless channel is very complex because of both frequency and time selectivity. In order to overcome the adverse effects of multipath fading in mobile communication channels, channel estimation must be performed. There are three kinds of channel estimation methods: pilot based, blind and semi-blind. In pilot based channel estimation, some of data symbols are used to estimate channel. In blind channel estimation statistical properties of channel are used. In semi-blind channel estimation information from both data symbols and statistical properties is utilized. In this study, pilot based and semi-blind channel estimation are used to estimate the channels with various frequency selectivity in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. Semi-blind channel estimation is done by using independent component analysis (ICA). Simulation results show that if the frequency selectivity of the channel is high, semi-blind channel estimation technique can be used instead of pilot based channel estimation. Thus with a using a small number of pilot bits, more data can be used in the MIMO systems.

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Semi-blind MIMO-OFDM channel estimation based on ICA and Pilot Carriers

Cited by 2 publications

References 3 publications

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Channel Estimation Techniques for MIMO-OFDM Systems over Frequency Selective Fading Channels

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