Stochastic Optimization Assisted Joint Channel Estimation and Multi-User Detection for OFDM/SDMA

Zhang, Jiankang; Chen, Sheng; Mu, Xiaomin; Hanzo, Lajos

doi:10.1109/vtcfall.2012.6399211

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Evolutionary algorithms are convectional ML methods based on biological evolution mechanisms, aiming at the global minimum while not sticking to local minima. Some evolutionary algorithms are the genetic algorithm (GA), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), differential evolution algorithms (DEA), and colony optimization [194,195]. Among those approaches, GA [196][197][198][199][200][201][202], RWBS [203][204][205], and PSO [206][207][208][209] have been applied to channel estimation in multicarrier systems.…”

Section: Evolutionary Algorithmmentioning

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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