Overhearing Protocol Design Exploiting Intercell Interference in Cooperative Green Networks
Bibliographic record
Abstract
The conventional two-way relaying (TWR) protocol requires that the transmitter (receiver) in one direction must be the receiver (transmitter) in the other direction, a limitation precluding the application of the TWR for sophisticated real-world wireless networks. In this paper, we study a more general multicell system consisting of a downlink (DL) traffic in one cell and an uplink (UL) traffic in an adjacent cell, with a multiantenna relay located in the cell edge and shared by both cells. For the coexistence of DL and UL transmissions, we propose exploiting the overheard signals from the adjacent cell (commonly known as the intercell interference) to improve the quality of signal reception in both cells. To reduce the power consumption to suit for green networks, we design the optimum relay precoder to minimize the total power at the relay yet satisfying the rate constraints for both the DL and UL traffic flows. The original precoder design is a nonconvex problem that is difficult to solve. To make the problem tractable, we transform the nonconvex problem to an equivalent quadratically constrained quadratic program (QCQP), which is then solved by the semidefinite relaxation (SDR) technique. Finally, simulations validate the effectiveness of our proposed protocol together with the optimized relay precoder.
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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 | 0.001 | 0.002 |
| 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".