Performance Analysis of Underlaid Full-Duplex D2D Cellular Networks
Bibliographic record
Abstract
This paper investigates the benefits of incorporating underlaid full-duplex (FD) device-to-device (D2D) communications into cellular networks. Toward this end, we provide an analytical performance characterization of underlaid D2D cellular networks where D2D users operate in FD mode under the presence of residual self-interference. In considered networks, the base-stations (BSs) are distributed according to a hexagonal grid, while the locations of cellular and D2D users follow Poisson point processes (PPPs). Based on the stochastic-geometry approach, we develop the approximations of key performance metrics including coverage probabilities and achievable sum-rates of both cellular and D2D links, and such approximations involve quickly commutable integrals. Under a special case in which the number of D2D links is sufficiently large, the obtained approximations can be simplified to closed-form expressions, allowing characterize the sum-rate behaviors under the effects of various system parameters. We show that underlaid D2D communications in cellular network can offer a significant spectral efficiency gain as compared to pure cellular transmission. With a sufficiently low self-interference cancellation level, FD D2D can offer substantial spectral efficiency improvement over the half-duplex (HD) counterpart. Finally, the resulting performance metrics are compared with multi-cell networks operating in standard and fractional frequency reuse modes, and observe that frequency reuse provides improved coverage probabilities of both cellular and D2D links, but substantially reduces the D2D sum-rate performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".