Impact of Hardware Impairments on Physical Layer Security of Cell-Free Massive MIMO
Bibliographic record
Abstract
In contemporary discussions on wireless systems, the detrimental effects of hardware impairments (HWIs) are often overlooked, as evident in prior research on Cell-Free Massive Multiple-Input Multiple-Output (CF-MaMIMO). This paper analyzes the impact of HWIs on security performance based on broadcasting artificial noise (AN) as a Physical Layer Security (PLS) technique in CF-MaMIMO systems. For this purpose, we derive the SNR of the legitimate users and the eavesdroppers considering HWIs along with broadcasting AN. Contrary to existing literature, we demonstrate that in certain instances, the AN leads to degradation in the security performance of the system due to HWIs. Our findings reveal that fluctuations in the hardware quality of users, eavesdroppers and access points (APs) have a direct effect on the system’s ability to eliminate the impact of AN. Furthermore, these findings emphasize the significance of considering hardware quality when applying PLS techniques by broadcasting AN to maximize the security performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".