Multi-User Detection Under Correlated Noise With Dense Large-Scale Antenna Arrays and Low-Resolution ADCs
Bibliographic record
Abstract
We investigate the uplink scenario in massive multiple input multiple output (MIMO) communication systems using dense uniform linear arrays (ULAs) of antenna elements that are tightly packed within a confined space and equipped with low-resolution analog-to-digital converters (ADCs). We tackle the problem of power consumption reduction and hardware simplification while simultaneously improving the performance of quantized systems by exploring spatial oversampling. Due to the subwavelength inter-element spacing in dense ULAs, extrinsic spatial thermal noise correlations arise from the significant coupling between adjacent antenna terminals. In addition to this correlated extrinsic noise, the noise figure caused by hardware imperfections profoundly impacts signal recovery and cannot be simply neglected in system performance analysis. We propose a low-resolution multi-user detection method based on a modified version of the vector approximate message passing (VAMP) framework. We also conduct a state evolution analysis to characterize the asymptotic behaviour of the proposed algorithm. We demonstrate that spatial oversampling in the context of low-resolution communication substantially enhances system performance, bringing it closer to the ideal scenario with infinite-resolution ADCs. This reveals the benefits of spatial oversampling as an effective strategy for enhancing the performance of low-resolution massive MIMO systems. We also thoroughly analyze the impact of noise figure on signal recovery under spatial oversampling, thereby highlighting its significance in system design considerations1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".