Large Sequence Model for MIMO Equalization in Fully Decoupled Radio Access Network
Bibliographic record
Abstract
Fully decoupled RAN (FD-RAN) aims to improve network performance by decoupling the hardware of base stations (BSs) and enabling flexible cooperation, making it a promising architecture for next-generation wireless networks. With the emergence of artificial intelligence, FD-RAN provides an opportunity to integrate physical-layer signal processing with neural network models. However, conventional deep learning-based multi-input multi-output (MIMO) equalization methods often rely on extensive offline training under fixed channel conditions, resulting in limited generalization to unseen wireless environments. Motivated by the strong generalization ability demonstrated by in-context learning (ICL) in natural language processing, we extend ICL to cooperative MIMO equalization in the FD-RAN framework. In this setup, geographical location information is incorporated as side information to enhance inference accuracy. Lightweight Transformer encoders are deployed at resource-constrained BSs to compress received signals, which are then forwarded to a central unit where a large decoder-only Transformer, adapted from GPT-2, performs equalization. The components are jointly trained to capture channel characteristics effectively. We further evaluate the generalization capability of large models by comparing the proposed ICL-based equalizer against meta-learning baselines. Experimental results show that our method achieves over 29% improvement in normalized mean square error under 8-bit and 12-bit fronthaul constraints compared to an unquantized LMMSE baseline. Moreover, as the pretraining dataset size increases, the ICL-based equalizer consistently outperforms meta-learning approaches, underscoring its scalability and potential for deployment in large-scale, data-driven wireless systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".