Optimum Biasing for Cell Load Balancing Under QoS and Interference Management in HetNets
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it