I/Q Imbalance Compensation in Cell-Free Massive MIMO During Uplink Transmission
Bibliographic record
Abstract
This paper considers compensation issues within cell-free massive multiple-input multiple-output (MIMO) communication systems, under the in-phase and quadrature-phase imbalance (IQI). Both access points (APs) and users are equipped with multiple antennas. We conduct an analysis into the impact of IQI and propose an efficient IQI compensation scheme to overcome the effects of IQI. Analytical expressions for the minimum mean-square error (MMSE) estimation and the achievable spectral efficiency (SE) of each user are derived, both with and without IQI compensation. In addition, to characterize the IQI effect in the massive MIMO regime, we analyze the asymptotic performance of cell-free massive MIMO when the number of APs goes to infinity. The results of our analysis demonstrate that, when the number of APs grows large, a cell-free system with perfect I/Q matching will allow the SE to increase without bound. However, if IQI is present, the system performance will saturate even if the number of APs becomes very large. The introduction of a compensation technique at the APs, that requires only an estimation of the IQI coefficients, is successful in removing this performance limit, hence significantly enhancing the system performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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 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".