On Evaluating Independent Set Heuristics for Wireless Backhaul Network Capacity of 5G Ultra-Dense Networks
Bibliographic record
Abstract
Tremendous amount of data traffic generation is anticipated through upcoming applications, such as augmented reality, virtual reality, and tactile internet. On the other hand, 5G is promising record-breaking data rates. An Ultra-Dense Network (UDN) is envisaged as a key component of 5G, where small cells operating at millimeter-wave frequencies will be deployed in hotspots to cope with extreme traffic generation. A wired backhaul is infeasible in these ultra-dense scenarios as it will not be possible to connect each small cell to the core over fiber or digital subscriber line. A viable alternative will be a wireless backhaul. There is a direct relationship between the backhaul network capacity of the wireless backhaul and the average number of simultaneous transmissions in the wireless backhaul in an UDN. The problem of finding the average number of simultaneous transmissions is similar to the minimum coloring problem, which is NP-Hard. In this paper, we adapt three different heuristic algorithms for solving this problem and we compare their performance in terms of the backhaul network capacity. From the results of our Monte Carlo simulations, it is clear that the heuristic we call Maximum Node-Degree Start outperforms other heuristics. It provides an improvement of up to 3.58% and 4.97% in the backhaul network capacity compared to Random Start heuristic and Minimum Node-Degree Start heuristic, respectively.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".