Low Complexity ZF and MMSE Detectors for the Uplink MU-MIMO Systems With a Time-Varying Number of Active Users
Bibliographic record
Abstract
The classical multiuser detection algorithms such as zero forcing (ZF) and minimum mean square error (MMSE) receivers are designed with the assumption that the number of active users is constant and known. When the number of the active users changes, the receiver may exhibit a serious performance loss if it does not react quickly to such variations. In this paper, we address the problem of reducing the complexity of the reevaluation of the popular ZF and MMSE detectors for multiuser multiple-input multiple-output (MU-MIMO) systems with a time-varying number of users in the channel. For each technique, we propose a detection approach with low complexity and without performance loss. The proposed algorithms avoid the direct computation of matrix inverses required by the ZF and MMSE detectors. Moreover, the performance losses, due to the use of the ZF and MMSE detectors intended for the scenario with a fixed number of active users, are evaluated with Monte Carlo simulation results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".