Coverage and rate analysis in two‐tier heterogeneous networks under suburban and urban scenarios
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Massive boost in data traffic demand and inconsistent user's behavior have necessitated modern cellular networks to evolve toward heterogeneous architectural framework consisting of macro and small cells to accommodate ever‐increasing user's density. Literature survey reveals that the deployment of additional small cells can encounter the booming coverage, capacity, and QoS constraints by maintaining the overall operational cost of the network. In this paper, at first, a suitable model based on nonhomogeneous Poisson point process (NHPPP) is designed for heterogeneous wireless network (HetNet) consisting of two‐tiers eNodeBs (Macro and Small Cell), where each of the tiers is differentiated in terms of transmitting power, eNodeB density, and supported data rate. Subsequently, analytical expressions are derived for coverage probability (CP) and average rate (AR) to assess the performance of the HetNet. The contribution of the paper further lies in integrating the K‐means clustering algorithm with NHPPP to find the optimal locations of the small cell eNodeBs for extended coverage and rate improvement. The proposed model is investigated under differently dense scenarios like urban and suburban areas in India. It establishes the requisite of an optimal number of small cells along with the traditional infrastructure to maximize the performance in terms of CP and AR. Finally, the proposed integrated model is compared with the traditional homogeneous Poisson point process (HPPP) and NHPPP for coverage and rate analysis. It is observed that the K‐means clustering algorithm in integration with NHPPP overshadows both HPPP and NHPPP in terms of coverage and rate under both urban and suburban deployment scenarios.
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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 | 0.001 | 0.001 |
| 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