Interference cancellation in full-duplex multicell networks
Bibliographic record
Abstract
The advent and advancement of full-duplex (FD) technology in radio transceivers is expected to lead to full-duplex enabled multi-cell (FD-MC) networks in next generation wireless communication systems. Additional interferences between FD nodes and devices are the main challenges for the deployment of such networks. As in an early deployment scenario where the base stations (BS) operate in FD mode while the user equipments (UE) remain in the half-duplex (HD) mode, the self-interference (SI) within the FD-BS, the mutual interference (MI) between the FD-BS's and between the HD-UE's paired for FD scheduling are the major issues that could jeopardize the capitalization of FD system gains if they are not well addressed. In this paper, we present a system architecture for multi-stage cancellation of SI and joint cancellation of MI and residual SI in an FD-MC network. Multiple orthogonal pilots and their derived forms are utilized for channel estimation during a common training period. System-level delay calibration necessary for SI and MI channel estimation and cancellation is also provided.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".