Channel Estimation for Spectrum Sharing in Massive MIMO Communications
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Bibliographic record
Abstract
In this paper, we investigate the problem of channel estimation in a multi-user massive multiple-input multiple-output (MIMO) secondary network (SN) aiming to access the licensed spectrum of a multi-user massive MIMO primary network (PN) using the underlay spectrum sharing approach. We estimate the channels of the single-antenna primary users (PUs) and those of the secondary users (SUs) at the secondary base station by exploiting a learning phase. To do so, we design the SN’s training phase with the priority of mitigating pilot contamination at the PN. This aim is pursued under the desired restriction that the PN is not meant to change its training phase length in the presence of the SN. The proposed estimator of PUs’ channels is based on the PUs’ data in addition to their pilots. To estimate the SUs’ channels, we present two seemingly different pilot-based approaches and prove rigorously that they result in the same estimator. Our numerical results illustrate that employing the proposed technique enables the SN to control the interference that it causes at the PN at the cost of slight performance degradation in terms of the quality of SUs’ channel estimates at the SN base station.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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