ArticlePeerJ. Computer science2021
Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Enhancing 5G uplink communications through efficient PCC-OFDM with neural network-based channel estimation.Scientific reports · 2026Article
- Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management.PeerJ. Computer science · 2023Article
- Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models.PeerJ. Computer science · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.