A Survey of Analytical Modeling for Cellular/WLAN Interworking
Bibliographic record
Abstract
Introduction A number of wireless technologies have evolved rapidly during the past decade. Mobile devices and gadgets (e.g., cellular phones, personal digital assistants (PDAs), laptops) supported by some of these technologies are becoming more and more important in people's everyday life. Wireless local area networks (WLANs) and cellular networks are two paradigms of such technologies in the present wireless realm. WLAN, which is based on the IEEE 802.11 standards, is able to provide services with high data rate up to 11 Mbps (802.11b) or 54 Mbps (802.11a/g) at a relatively low access and deployment cost. Moreover, 802.11n, which is still under development, promises to offer a maximum data rate of up to 700 Mbps. However, the coverage area of WLAN is typically less than 100 meters, making it only suitable for hotspot regions such as hotels, libraries, airports, and coffee shops. Compared to the WLAN, cellular networks cover a much larger area that provides ubiquitous access over several kilometers. Nevertheless, the supported service data rate of cellular networks such as GSM (Global System for Mobile Communications), GPRS (General Packet Radio Service), UMTS (Universal Mobile Telecommunication System), or CDMA2000 (Code Division Multiple Access 2000) only ranges from a few kbps to 2.4 Mbps. Furthermore, the cost of accessing and deploying cellular networks is much higher than that of the WLANs. Driven by the complementary characteristics of these two wireless technologies (high-rate, low-cost, small coverage area of WLAN versus low-rate, high-cost, large coverage area of cellular network), a strong trend of combining them into one integrated system has emerged during the past years [1]-[6].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".