Joint Channel Estimation and User Activity Detection for mmWave Grant-Free Massive MTC Networks Under Pilot Contamination Attack
Bibliographic record
Abstract
Due to the lack of authentication, millimeter-wave (mmWave) grant-free massive machine-type communication (GFmMTC) networks are vulnerable to the pilot contamination attack (PCA), which will cause serious performance degradation of channel estimation (CE) and active user detection (AUD). However, the existing works towards the PCA detection in the mmWave GFmMTC networks perform the CE, AUD, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, in this paper, we establish a three-dimensional transmission model with time-correlated two-dimensional sparsity for both legitimate users (LUs) and attackers in mmWave GFmMTC networks, where the LU and attacker activity sparsity, the virtual angular channel sparsity, and the temporal correlation of L U activity are jointly considered. Based on the established transmission model, a three-dimensional multiple measurement vector-compressive sensing (MMV-CS) based joint CE and AUD scheme against PCA is proposed. Specifically, we first formulate the joint CE and AUD under PCA as a three-dimensional MMV-CS problem. Then, by utilizing the sparsity of user activity and angular virtual channel, we develop a parallel expectation-maximization vector approximate message passing with MMV (Parallel EM- VAMP-MMV) algorithm to efficiently solve the formulated problem. Simulation results show that the proposed scheme can achieve a substantial performance gain over comparison methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".