Joint Access and Relay-Assisted Backhaul Resource Allocation for Dense mmWave Multiple Access Networks
Bibliographic record
Abstract
In dense millimeter wave (mmWave) multiple access networks, there are a large number of wireless access links and wireless backhaul links. The mmWave bandwidth is shared among two types of links. So a time-division mode is appropriate for such a scenario to determine a reasonable backhaul/access transmission duration, which is critical to network performance. Meanwhile, minimizing energy consumption is also an important design objective. However, the existence of long backhaul links is an obstacle. The introduction of relay mode can overcome it, but it also leads to more complex mutual interference relationships. On the other hand, each individual (e.g., smart wireless device) wants to obtain the highest access data rate, but it may prevent the entire network from achieving the highest energy efficiency. To cope with these challenges, we propose the new mutual interference characterization method and model the joint access and relay-assisted backhaul resource allocation problem as a Stackelberg game. The Stackelberg Nash equilibrium is guaranteed by the rational design of utility function, and the corresponding solution is solved by a backward induction method. Simulation results show that, our scheme is superior to the state-of-the-art in terms of network sum rate and network energy efficiency, and also it achieves a better balance between the access data rate and backhaul data rate for each access point.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".