LDPC for receive antennas selection in massive MiMo
Bibliographic record
Abstract
Massive Multiple-Input Multiple-Output (m-MIMO) is a promising technology for improving the capacity of fifth generation (5G) wireless networks. However, m-MiMo suffers from the cost and complexity of the radio frequency (RF) chain. One solution to solve this problem is the method of antenna selection. However, this method requires information on the channel state (CSI) in order to select the most efficient subset, which is impossible in the presence of the pilot contamination. In addition, the exhaustive search method, used in conventional MiMo for selecting a subset of antennas, is ineffective for the massive MIMO system because it adds complexity to the processing and requires high energy consumption. For this purpose, a model using a water-filling algorithm and Low-Density Parity-Check (LDPC) decoded symbols is proposed in this article. This method exploits the characteristic of the Physical Layer to address the problem. It does not require supplementary chain, and it does not use pilot symbols to estimate the channel, which makes this proposed model frugal in term of energy consumption and processing resources comparing to the exhaustive method. Simulation results show that the proposed solution attains the same optimal values when the Exhaustive search method is used.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".