Fountain Codes and Linear Filtring to Mitigate Pilot Contamination Issue in Massive MiMo
Bibliographic record
Abstract
The fifth generation of cellular mobile (5G) is a future technology to meet growing capacity of users. For this prupose 5G will use advanced technologies. Very large multi- input multi- output or massive MiMo (m-MiMo) is considered as one of the promising technology. Nevertheless, the performance of m-MiMo is limited by pilot contamination issue. In fact, to mitigate pilot contamination issues in massive multi-input multi-output (m-MiMo), we proposed in previous work a new scheme where Raptor decoded symbols are used to estimate channel with Minimum Mean Square Error (MMSE) technique. The main benefit of this method is that the receiver does not need a transmitted pilot symbols to evaluate the channel, which allows saving power at transmission. The results showed that the MMSE scheme achieved the ideal case of the perfect channel. In this precedent paper, the MMSE detector and raptor code are used for their robustness among other schemes of linear detectors, and corrector codes, nevertheless, in case of m-MiMO, it was shown that all linear detectors work optimally. For this purpose, we include in this present article an additional linear filter to enhance the prior study, in which two supplementary detectors are considered, namely Zero Forcing (ZF) and Maximum Ratio Compression (MRC). The objective of this paper is to determine the ideal filtering technique and the robustness fountain code to address pilot contamination problem. In fact, the simulation results show that the ZF can attain the same ideal performance as the MMSE with raptor decoded symbols while MRC achieved lower performance compared to the other two shemes. 
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".