Analysis and Design of Secure Massive MIMO Systems in the Presence of Hardware Impairments
Bibliographic record
Abstract
To keep the hardware costs of future communications systems manageable, the use of low-cost hardware components is desirable. This is particularly true for the emerging massive multiple-input multiple-output (MIMO) systems which equip base stations (BSs) with a large number of antenna elements. However, low-cost transceiver designs will further accentuate the hardware impairments, which are present in any practical communication system. In this paper, we investigate the impact of hardware impairments on the secrecy performance of downlink massive MIMO systems in the presence of a passive multiple-antenna eavesdropper. Thereby, for the BS and the legitimate users, the joint effects of multiplicative phase noise, additive distortion noise, and amplified receiver noise are taken into account, whereas the eavesdropper is assumed to employ ideal hardware. We derive a lower bound for the ergodic secrecy rate of a given user when matched filter data precoding and artificial noise (AN) transmission are employed at the BS. Based on the derived analytical expression, we investigate the impact of the various system parameters on the secrecy rate and optimize both the pilot sets used for uplink training and the AN precoding. Our analytical and simulation results reveal that: 1) the additive distortion noise at the BS may be beneficial for the secrecy performance, especially if the power assigned for AN emission is not sufficient; 2) all other hardware impairments have a negative impact on the secrecy performance; 3) despite their susceptibility to pilot interference in the presence of phase noise, so-called spatially orthogonal pilot sequences are preferable unless the phase noise is very strong; and 4) the proposed generalized null-space AN precoding method can efficiently mitigate the negative effects of phase noise.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".