Time-Domain Adaptive Decision-Directed Channel Equalizer for RGI-DP-CO-OFDM

Li, Xiang; Zhong, Wen‐De; Alphones, Arokiaswami; Chen, Yu

doi:10.1109/lpt.2013.2292694

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The DDCE methods are addressed for OFDM systems with different approaches. They include joint estimation of carrier frequency offset (CFO) and sampling clock frequency offset [140], sample-spaced and fractionally spaced CIR [142], generalized M estimators for mitigating error propagation [141], LS and least MMSE estimators [143], maximum a posteriori channel estimation [144], hard decision signal-to-noise ratio (SNR)-assisted residual CFO estimation [145], joint CIR and noise variance estimation [146], subspace algorithm [147], time-domain channel equalizer [144], and EM algorithm [81]. Performing soft DDCE based on selecting reliable data tones purified by inter stream interference cancelation is proposed in [139].…”

Section: Decision-directed Channel Estimation Techniquesmentioning

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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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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