Forward-link capacity in smart antenna base stations with dynamic slot allocation
Bibliographic record
Abstract
We consider a base station, which communicates to a set of portable stations using a smart antenna operating in multibeam, packet-switched, space division multiple access (SDMA) mode. We assume that the system operates using time division duplexing (TDD) and focus on the problem of access to the stations by the base station in the forward-link direction. A polling protocol is used which permits efficient access in this type of system. The operation of the protocol is unique in that it permits dynamic slot allocation and accommodates variations in channel time coherence. In the protocol, dynamic slot assignment is integrated into the forward-link beam scheduling. This allows us to explore the value of dynamic station slot assignment when constructing the SDMA/TDMA frames. The results show the improvements in capacity, which are possible in such systems and give insight into the degradation in protocol performance that occurs when channel coherence times decrease. We find that very large improvements in capacity are possible using dynamic slot allocation, especially under harsh channel conditions. We also investigate various base station queueing issues in this type of system. It is shown that care must be taken in how buffering is performed so that blocking effects do not unnecessarily degrade the forward-link capacity.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".