Joint Resource Allocation for Ultra-Reliable and Low-Latency Radio Access Networks With Edge Computing
Bibliographic record
Abstract
This paper investigates a joint resource allocation for ultra-reliable and low-latency radio access networks (URLLRANs) with edge computing. Compared with conventional networks, URLLRANs have more restrictive latency and reliability requirements, and always feature short packet communications. It is a challenging work to provide edge computing services in URLLRANs, since the processing and transmission delay as well as packet loss during computation and communications should all be taken into considerations. Along these lines, to specify the trade-off between latency and reliability, this paper defines computation rates and transmission rates for short packets. Different from the existing work, the proposal takes effective information as well as energy consumption as performance metrics based on the definition. The packet request rates, computation latency, service rates, communication power, blocklength, and transmission information amounts are jointly optimized to reduce energy consumption and meanwhile generate more effective information for both the computation system and the communication system. To solve the NP-hard problem, the locally optimal solution and global optimal solution are both derived. Simulation results validate the performance advantage of the proposal and also indicate that the locally optimal solution can greatly reduce the computation complexity with only a small performance loss when compared with the global optimal solution.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".