Low‐complexity channel estimation for time division duplex massive multi‐user multi‐input multi‐output systems
Bibliographic record
Abstract
Abstract This paper addresses the problem of minimum mean square error channel estimator for time division duplex massive multi‐user multi‐input multi‐output systems. It is noteworthy that, the minimum mean square error has been previously proposed for multi‐cell massive multi‐user multi‐input multi‐output channel estimation. However, the minimum mean square error estimator suffers from high computational complexity due to the large dimension of the covariance matrix inversion. In this study, low‐complexity channel estimator for time division duplex massive multi‐user multi‐input multi‐output networks is designed by using the low‐rank matrix approximation techniques. The proposed estimator is referred to as an approximate minimum mean square error estimator. Furthermore, the computational complexity of the proposed approximate minimum mean square error estimator is analysed and compared to the minimum mean square error and least square estimators. The normalised mean square error and the uplink achievable sum‐rate performance criteria are used to evaluate the performance of the proposed estimator. Finally, the simulation results show the effectiveness of the proposed estimator under two different scenarios: noise‐limited and pilot contamination. These simulation results are compared to the conventional minimum mean square error and least square estimators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".