Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems
Bibliographic record
Abstract
Abstract So far, various data‐driven approaches have been presented to obtain channel state information (CSI) in millimeter wave multiple‐input‐multiple‐output wireless networks. In almost all previous works, training and testing channels were assumed to have the same distribution, which may not be the case in practice. In this article, we address this challenge by proposing a learning framework that is a combination of a recurrent neural network (RNN) model and a deep neural network (DNN) for estimating CSI in a dynamic wireless communication environment. Furthermore, we use federated learning to train the learning‐based channel estimation model. More specifically, we introduce a two‐stage downlink pilot transmission procedure, where in the initial stage, long frame length downlink pilot signals are used to train the introduced RNN‐DNN model. Following that, users will receive shorter‐frame‐length pilot signals that can be used for CSI estimation. To speed up the training procedure of the proposed network, we first generate a pre‐trained model and then modify it according to the collected data samples. Simulation results demonstrate that, when the channel distribution is unavailable, the proposed approach performs significantly better than the most recent channel estimation algorithms in terms of estimation performance and computational complexity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".