Precoded Large Scale Multi‐User‐MIMO System Using Likelihood Ascent Search for Signal Detection
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Multiple antennas at each user equipment (UE) and/or thousands of antennas at the base station (BS) comprise the extremely spectrum efficient large scale multi‐user multiple input multiple output system (BS). Due to space constraints, the closely spaced numerous antennas at each UE may cause inter antenna interference (IAI). Furthermore, when one UE comes into contact with another UE in the same cellular network, multi‐user interference (MUI) may be introduced to the received signal. To mitigate IAI, efficient precoding pre‐coding is necessary at each UE, and the MUI present at the BS can be canceled by efficient Multi‐user Detection (MUD) techniques. The majority of earlier literature deal with one or more of these interferences. This paper implements a joint pre‐coding and MUD, Lenstra‐Lovasz (LLL) based Lattice Reduction (LR) assisted likelihood accent search (LAS) (LLL‐LR‐LAS), to mitigate IAI and MUI simultaneously LLL‐based LR pre‐coding mitigates IAI at each UE, and the LAS algorithm is a neighborhood search‐based MUD that cancels BS MUI. The proposed approaches' performance was evaluated using Bit Error Rate analysis, and their complexity were determined using multiplication and addition.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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 it