Federated Learning Based Near-Field Channel Estimation for XL-MIMO Communications
Bibliographic record
Abstract
Extremely large-scale massive MIMO (XL-MIMO) is foreseen as a promising technology to achieve ultra-low latency, high data transmission speed, and extremely low error rate in future 6 G networks. The increases in antenna apertures and the use of higher frequencies (millimeter-wave and subTHz) in XL-MIMO significantly extend the Rayleigh distance, thereby enhancing the prevalence of near-field (NF) communications. Unfortunately, existing far-field channel models struggle to accurately capture both line-of-sight (LoS) and non-line-ofsight (NLoS) propagation paths in the presence of NF effects. Moreover, the huge number of antenna elements greatly increases computational demands and complicates channel estimation tasks. In this paper, we propose a federated learning (FL)based near-field channel estimation (NFCE) framework for mixed LoS/NLoS environments. In this framework, we employ a federated deep residual learning (FDRL)-based convolutional neural network (CNN) architecture, where only a subset of local devices participates in the distributed training process. By utilizing the complex convolution and a few residual blocks, this framework reduces the effect of signal noise while minimizing communication overhead and computational complexity. Simulation results demonstrate that our proposed framework significantly improves NFCE performance in terms of normalized mean square error and bit error rate compared to selected benchmark schemes.
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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 | 0.002 |
| 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 | 0.001 |
| 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".