Channel estimation for large MIMO systems under hardware impairments
Bibliographic record
Abstract
In this thesis, we study the problem of channel estimation under hardware impairments for Large MIMO systems. Large MIMO are systems with tens to hundreds of antennas at the base station which offer numerous advantages over conventional MIMO, such as improved performance and energy efficiency \\cite{1}. Large MIMO are often built with low-cost components, which may lead to hardware imperfections and cause distortion at the base station and the users. In order to develop an accurate system model we take into consideration the noise caused by hardware impairments when performing channel estimation.For this system model, we extend the Linear Minimum Mean Square Error (LMMSE) estimator for Large MIMO systems proposed in \\cite{20} for a multi-user system. The proposed LMMSE estimator considers the distortion at both ends and achieves better performance in terms of relative estimation error per antenna over Signal-to-Noise ratio (SNR) compared to estimators used for conventional MIMO systems, such as the LMMSE and the Least Squares (LS) estimator. Furthermore, the Cramer-Rao bound (CRB) of the system is calculated.
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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 | 0.007 |
| 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 | 0.001 |
| 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".