Optimal antenna placement in multi-hop wireless networks with heterogeneous antennas
Bibliographic record
Abstract
The recent advances in antenna technology have made smart beamforming antennas attractive candidates to be deployed in multi-hop wireless networks. Beamforming antennas can increase the spatial reuse of the channel and consequently the network capacity. Due to practical and economical considerations, deploying beamforming antennas in the whole network is sometimes infeasible. In this paper, we focus on multi-hop wireless networks with heterogeneous antenna capabilities. We study the problem of optimal antenna placement to maximize the total network throughput. We first introduce an antenna-aware conflict graph using the notion of virtual links. With the objective of minimizing the size of the maximum clique, we formulate our problem as a mixed-integer linear programming problem. Moreover, we propose a beamforming antenna placement heuristic algorithm for the partial deployment of beamforming antennas to upgrade existing wireless networks originally equipped with traditional omni-directional antennas. Our numerical results show that the proposed algorithm produces near-optimal results in a small fraction of time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".