A New Look at Optimum Macro Base Station Deployment in Heterogeneous Massive MIMO Networks for Eliminating Interference
Bibliographic record
Abstract
Massive multiple input multiple output (MIMO) Heterogeneous Networks (HetNets) are integral part of 5G systems. They can be viewed in three dimensional (3D) space where, each slice represents a unique tier that has its own Base stations (BS) and user equipments (UEs). Different slices cooperate with each other for mutual benefit. Data can be interactively exchanged among the tiers, and UEs have the flexibility to switch between the tiers. Poisson point process (PPP) has been widely used to allocate BSs and UEs among various tiers so far. However, BS locations using PPP approach may not be optimum to reduce interference. In this paper, we develop a new algorithm to find the optimum locations of Macro BSs in HetNets where every Macro BS along with its associate Micro BSs is treated as an independent unit after almost eliminating interference from neighboring Macro and Micro BSs. Hence, this approach provides increased coverage area and enhanced quality of service. Simulation results show the validity of our approach and promises enhancement to system performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".