HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless\n Cellular Networks
Bibliographic record
Abstract
A recent approach in modeling and analysis of the supply and demand in\nheterogeneous wireless cellular networks has been the use of two independent\nPoisson point processes (PPPs) for the locations of base stations (BSs) and\nuser equipments (UEs). This popular approach has two major shortcomings. First,\nalthough the PPP model may be a fitting one for the BS locations, it is less\nadequate for the UE locations mainly due to the fact that the model is not\nadjustable (tunable) to represent the severity of the heterogeneity\n(non-uniformity) in the UE locations. Besides, the independence assumption\nbetween the two PPPs does not capture the often-observed correlation between\nthe UE and BS locations.\n This paper presents a novel heterogeneous spatial traffic modeling which\nallows statistical adjustment. Simple and non-parameterized, yet sufficiently\naccurate, measures for capturing the traffic characteristics in space are\nintroduced. Only two statistical parameters related to the UE distribution,\nnamely, the coefficient of variation (the normalized second-moment), of an\nappropriately defined inter-UE distance measure, and correlation coefficient\n(the normalized cross-moment) between UE and BS locations, are adjusted to\ncontrol the degree of heterogeneity and the bias towards the BS locations,\nrespectively. This model is used in heterogeneous wireless cellular networks\n(HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic\non the network performance. This network is called HetHetNet since it has two\ntypes of heterogeneity: heterogeneity in the infrastructure (supply), and\nheterogeneity in the spatial traffic distribution (demand).\n
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".