On Channel Estimation to Enable Fair Licensed Spectrum Sharing Between Two MNOs
Bibliographic record
Abstract
Licensed spectrum sharing has been proposed as a promising approach to alleviate spectrum scarcity in modern wireless communication systems. The advent of massive multiple-input multiple-output (MIMO) techniques leads to the possibility of using beamforming to enable spectrum sharing. However, channel estimation is crucial in implementing MIMO techniques. In this paper, we investigate channel estimation in a fair licensed spectrum sharing framework between two mobile network operators (MNOs), where user terminals (UTs) send pre-designed pilot sequences to the base stations (BSs). As the conventional cell-based pilot allocation scheme is inferior for the less powerful operator (i.e., the operator who provides less spectrum in the system), we propose a cluster-based pilot allocation mechanism where the pilot sequences are assigned to UTs within each newly organized cluster. The simulation results illustrate that, although the cluster-based pilot allocation scheme achieves an improvement over the conventional cell-based allocation in terms of achievable sum rate, the operator that can provide much less spectrum than the other operator will be unlikely to take part in the spectrum sharing framework. On the other hand, however, once both operators can provide a similar number of frequency slots, channel estimation does not affect the gain attained through the spectrum sharing mechanism.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".