Achievable Sum-Rate of Full-Duplex-Based Small Cells With Clustered Interference Alignment
Bibliographic record
Abstract
It has been well-recognized that clustered Interference alignment (IA) can provide remarkable interference suppression performance for the existing small cell networks (SCNs). There is also a tendency that full-duplex (FD) radios would replace the half-duplex radios at future small base stations (SBSs). In this context, the intra-cell and inter-cell interference in SCNs would become much more serious, where the performance of clustered IA has not been evaluated yet. In this paper, we explore the maximum achievable sum-rate of the FD-based SCNs when clustered IA combined with power control strategy is applied. To achieve this, a mixed-integer optimization problem is formulated, which is furtherly decoupled into two subproblems for ease of handling. Then we propose the minimized rate loss (MRL) algorithm to address the clustering subproblem and a convex approximation method to address the power control subproblem. The two subproblems are performed alternatively till the sum-rate gains convergence. Preliminary simulations clearly demonstrate that the achievable sum-rate is limited by the number of antennas at the users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".