Ad hoc networks with smart antennas using IEEE 802.11-based protocols
Bibliographic record
Abstract
Smart antennas have been studied extensively for use in cellular radio base station applications. Recently however, low cost array technologies have suggested that adaptive antennas may soon be cost effective for mobile ad hoc networks. In this paper we consider the potential use of adaptive antenna arrays in networks using protocols based on the IEEE 802.11 distributed coordination function (DCF). In the system under study, omnidirectional RTS/CTS exchanges are used to initiate array-mode data packet transmissions. Several variations on the basic protocol are considered. When two stations communicate using their antenna arrays, the ensuing gain across the link can be very large. In many cases the transmit power can be significantly reduced while still maintaining a sufficient link margin. We show that this reduction in power is a key factor in improving the capacity of an ad hoc network. Results are presented for various parameters which show how the capacity of the system scales with the size of the system. Significant capacity improvements are possible compared with a network using conventional IEEE 802.11 protocols. In our simulations a relatively inexpensive circular antenna array configuration is used with a fairly modest number of elements.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".