Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets
Bibliographic record
Abstract
LTE-Advanced is a promising technology which supports much higher peak rates, higher throughput and coverage, and lower latencies, resulting in a better user experience. For ex-tended indoor coverage LTE-A supports two-tier network composed of conventional macro-cellular networks and femtocell networks known as HetNets. As the femtocell shares the same frequency band with underlying macrocell, the cross tier interference needs to be mitigated. The inter-femtocell and cross tier interference near femtocell boundary may result in unwarranted degradation of system performance. In LTE-A, as an OFDM based technology, we consider the use of prudent PRB (Physical Resource Block) allocation to mitigate downlink inter-femtocell interference as well as cross tier interference in the mentioned environment for this small HetNet. Allocation of PRBs to network users is formulated as a graph coloring problem. Based on this interpretation and initial sensitivity study, we propose dynamic resource allocation algorithms namely greedy allocation (GA), SINR based allocation (SINRA) and ant colony allocation (ACA). From the simulation results and its analysis we can conclude that, ACA algorithm outperforms rest of the considered algorithms while GA and SINRA provide performance with the significant improvement over random allocation (RA) algorithm as far as performance parameters such as averaged SINR experienced by a network user, outage probability and number of PRBs needed are concerned.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".