Pilot Decontamination in TDD Multicell Massive MIMO Systems With Infinite Number of BS Antennas
Bibliographic record
Abstract
The performance of time division duplex (TDD) multiple-input multiple-output (M-MIMO) systems is mainly limited by the pilot contamination, which is a negative effect of reusing uplink pilot sequences in the neighboring cells. A pilot decontamination scheme for TDD multicell M-MIMO systems with an infinite number of base station (BS) antennas is proposed in this paper. The proposed scheme uses Zadoff-Chu (ZC) sequences as uplink pilot sequences and implements distinct orthogonal variable spreading factor (OVSF) code rows at each BS. The set of uplink pilot sequences at each BS is multiplied element-wise with the BS-specific OVSF code row to make uplink pilot sequences orthogonal across the network. Then, a mobile station randomly selects one of these multiplied ZCs from a given set and transmits it on the random access channel at the commencement of coherence interval. The performance of the uplink and downlink rates of the proposed scheme is compared with those of the time-shifted pilot scheme for an infinite number of BS antennas. The simulation results authenticate the validity of the proposed scheme.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".