Bibliographic record
Abstract
Recently, cell-free massive multiple-input multiple-output (mMIMO), a distributed version of mMIMO, has increasingly been deployed to increase the spectral and energy efficiency of communication systems. In this paper, we investigate the ability of using cell-free mMIMO to reduce the jamming attacks which is a critical issue for communication. We propose a full-stack framework from detecting to suppressing jamming attacks in cell-free mMIMO system. At first, we exploit the unused pilots to design a jammer detector based on the likelihood functions of the measured signals. Then, we propose a jamming suppression method including two tasks: jamming estimation and access point (AP) selection. In the first task, we estimate the phase and amplitude when projecting the received signals onto the unused pilots, and then use them to estimate the jamming signal. In the second task, we employ the neural network-contextual multi-armed bandit (NN-CMAB) for online selection of APs which can provide the multi-user spatial diversity considering the existence of the jammers. Furthermore, we also propose a power control strategy for managing the minimum rate requirement in multi-user settings. Numerical results confirm the advantages of proposed designs over conventional jamming-ignorant-LMMSE strategy in spectral efficiency.
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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".