Reverse-link power allocation in two-hop multimedia CDMA networks
Bibliographic record
Abstract
Recently there has been significant interest in augmenting the cellular networks with the multihop capability to obtain better high data rate performance in the context of enhanced-3G and beyond-3G networks. This paper deals with the power allocation strategies in the reverse-link of two-hop multimedia CDMA networks. In this envisioned network, the WTs (wireless terminals) which cannot establish a direct link with the BS (at the required rates) seek the assistance of those WTs which can; in other words, whenever needed, some WTs are used as relayers for some other WTs (relayees) if this is possible. In a two-hop link, the first hop (relayee to relayer) uses the unlicensed band and the second hop (relayer to BS) uses the cellular band. This arrangement not only guarantees that no additional expensive cellular spectrum will be used to facilitate a two-hop link, but it also guarantees that if anything goes wrong in the first hop, this will not affect the performance of the WTs which directly communicate with the BS in the cellular band. The performance of any CDMA network depends on the implementation of a good power allocation and control scheme. The optimum power allocation and control in the reverse link of the conventional single-hop CDMA networks is well known. The main contribution of this paper is the development of a good power allocation scheme (which is tied to the proper selection of relayers) in the first hop of the two-hop reverse-link of the envisioned CDMA network. It is worth emphasizing that the nature of the reverse-link power allocation problem is very different in the many one-to-one links (or possibly numerous several-to-one links) which collectively constitute the first hop of the two-hop CDMA network under consideration, in comparison to that in the single many-to-one link in the reverse link of a conventional single-hop CDMA network. The simulation results show that the two-hop relaying, facilitated with the developed novel power allocation scheme, yields considerable enhancements in the CDMA cell capacity, coverage, and throughput.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".