Capacity of S-ALOHA protocols using a smart antenna at the basestation
Bibliographic record
Abstract
We consider the capacity performance of several slotted ALOHA protocols adapted for smart antenna SDMA operation. We assume that a set of portable stations share a single radio link with a basestation which is equipped with a smart antenna operating in a multi-beam SDMA mode. The system uses time division duplexing so that the uplink and downlink spatial channels are highly correlated. Versions of the protocols are considered where initial station access occurs in the data slots directly, and when a minislotted reservation channel is used. Both multi-beam and single-beam operation is considered in the reservation minislots. In all cases, we assume that optimal SINR beamforming is used when assigning stations to transmission slots. In the results shown, we optimize the design of each system to maximize the capacity achieved. The comparisons thus show the relative tradeoffs between capacity and complexity possible in these systems. In all cases considered, multibeam operation in both reservation minislots and data slots can achieve the highest capacity. However, we find that when operating under low SNR, and especially for long packet lengths, only very marginal improvements in capacity are achieved. The same observation is true under higher SNRs, but the advantage of multibeam reservation is improved somewhat. This is important since operating the system with multibeam minislot contention is expected to be highly complex, due to the dynamic acquisition which must take place. We also find that when packet lengths are in the ATM cell size range, there is little or no capacity advantage in using a single-beam reservation protocol over multibeam S-ALOHA. However, in the latter case, dynamic acquisition of multiple transmissions is needed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".