Unlocking Sub-THz by Robotic Aerial Base Stations: Joint Deployment and Wireless Backhaul Routing
Bibliographic record
Abstract
Despite the numerous advantages of aerial base stations (ABSs), including their relatively ease of deployment and inherent flexibility for relocation to adapt to highly dynamic networks, their service endurance is constrained by the limited capacity of their onboard batteries. To address this limiting factor, we explore the use of robotic aerial base stations (RABSs) that are equipped with grasping end-effectors able to anchor onto tall urban landforms such as lampposts. Energy-neutral anchoring conserves energy consumption by eliminating the need for hovering or flying during service time, thereby massively improving communication service endurance. In this paper, a joint RABS deployment and wireless backhauling scheme with the aim of maximizing served traffic is proposed to support future dynamic and densified wireless networks experiencing unprecedented data traffic growth. To meet this significant increase in traffic demand, which requires substantial bandwidth for both access and backhaul, we employ sub-Terahertz (sub-THz) band communication due to its ultra-wide spectrum resources. Given the sub-THz band’s susceptibility to blockages and severe propagation losses due to absorption, we propose a multi-hop wireless scheme to extend network coverage. The optimization interplay between RABS grasping locations, route flow control, and sub-band allocation to ensure link capacity, is framed as a robust optimization problem aimed at maximizing served traffic with a cardinality-constrained uncertainty set. Since the grasping locations are determined from all candidate locations, the number of corresponding candidate routes can significantly increase with the network size in this multi-hop enabled network. In this work, we propose a column generation (CG) based algorithm to tackle the curse of dimensionality due to the exponentially increased number of candidate routes. To this end, a near-optimal decision making is proposed with significantly reduced computational complexity. A wide set of numerical investigations demonstrates the superiority of the proposed network scheme over baseline schemes. For instance, the aggregated served traffic demand improved by 125% compared to a network with fixed small cell deployment which could be considered as the nominal use case and a common deployment option for increasing network capacity.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".