Secrecy analysis for forward link multi-user massive MIMO system with MRT precoding
Bibliographic record
Abstract
The secrecy performance of forward link multi-user massive multiple-input multiple-output (MIMO) system is investigated in this paper. The base station (BS) equipped with a large number of transmit antennas sends mutually independent datas to multiple single-antenna legitimate users simultaneously. A passive multi-antenna eavesdropper is placed in the scenario to intercept the signals. We consider the case that the number of legitimate users and eavesdropper antennas increases proportionally with the number of BS antennas. Maximum ratio transmission (MRT) is used as linear precoder at the BS. Lower bounds on the ergodic achievable rate of legitimate users and upper bounds of the eavesdropper are derived. By minimizing the upper bounds on the achievable rates of the eavesdropper while satisfying the rate constraints of legitimate users, we present the corresponding optimal radiated power scaling (RPS) factor. In the limit of an infinite number of BS antennas, we prove that the optimal RPS factor converge to a different constant limit with that of the case in which the number of legitimate users and eavesdropper antennas is finite. We show that when the number of BS antennas grows large, the secrecy performance is mainly determined by the ratios between the number of BS antennas, legitimate users and eavesdropper antennas. A number of simulation results are presented to visualize the analysis.
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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 | 0.000 |
| 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".