Optimum Biasing for Cell Load Balancing Under QoS and Interference Management in HetNets
Bibliographic record
Abstract
In this paper, we consider a network in which lower power nodes (LPNs) are deployed jointly within macrocells. However, there are significant differences between the transmit power levels, coverage areas, and deployment densities of these two types of base stations. Such disparities lead to an unfair load distribution, as well as a lower throughput for picocells'users equipments (UEs). A good solution to such issues is the exploitation of the cell range expansion (CRE) technique. Although CRE has widely proven its effectiveness, it may degrade the network capacity if the cell bias is not chosen properly. In fact, it may generate severe intercell interference at extended region cell (ERC) UEs, which leads to a deterioration of their throughput. We thus propose a downlink coordinated cell range expansion for mobility management (CCREMM) strategy that analytically computes the joint optimal bias at picocells and macrocells. CCREMM mitigates the interference at ERC-UEs by accounting for their maximum tolerable interference. Moreover, CCREMM reaches the load balancing and the UE QoS satisfaction by accounting for additional parameters. It will be proven that our strategy which is associated with the maximum throughput scheduling technique, results in a cell load-balancing improvement, fairness, and a 50-90% UE throughput enhancement. These performance figures are shown to surpass those achieved by alternative approaches proposed in the existing literature.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".