Overview of Multi‐Tier Cellular Wireless Networks
Bibliographic record
Abstract
The demand of wireless services through the cellular networks is ever-increasing. Small cells can support wireless applications for homes and enterprises as well as metropolitan and rural public spaces. Different types of small cells include femtocells, picocells, and microcells. Among all the small cells, femtocells or Home evolved Node Bs (HeNBs), are of great interest and importance to the research community and mobile operators. In Long-Term Evolution (LTE), the mobility management functionalities are divided into three categories: (i) intra-LTE mobility, (ii) inter-3GPP mobility, and (iii) inter-radio access technologies (RAT) mobility. In LTE-A multi-tier networks, orthogonal frequency-division multiplexing (OFDM) is used for DL and single-carrier FDM (SC-FDM) waveform is used for uplink (UL) communications over 20 MHz bandwidth. LTE or 3GPP Release-8 onward and LTE-Advanced femtocells use OFDMA as the air-interface technology. The propagation channel model can be represented by a combination of path-loss, log-normal shadowing, and multipath fading.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".