HAPS-ISAC: Enhancing Sensing and Communication in 6G Networks With Advanced MIMO Beamforming
Bibliographic record
Abstract
This paper introduces a novel high altitude platform station (HAPS)-based integrated sensing and communication (ISAC) system, referred to as HAPS-ISAC, designed to enhance the capabilities of future 6G networks by simultaneously optimizing communication and sensing functions. HAPS operates as a super-macro base station in the stratosphere, utilizing advanced beamforming techniques within a multiple-input multiple-output (MIMO) architecture, supplemented by multiple-input single-output (MISO) configurations, effectively enabling the system to serve ground communication users (CUs) while conducting high-resolution sensing of potential targets. A Rician channel model is employed to capture both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The performance of the system is optimized through a non-convex optimization problem that maximizes the minimum beampattern gain towards desired sensing angles while ensuring that the signal-to-interference-plus-noise ratio (SINR) requirements for CUs are satisfied, all under the power constraints of the HAPS. Compared to the traditional terrestrial and UAV-based ISAC systems, HAPS-ISAC delivers sustained and reliable service over extensive areas, leading to significantly improved overall performance. Simulation results show that HAPS-ISAC significantly improves SINR, resource allocation, sensing accuracy, and fairness, outperforming existing technologies. This establishes HAPS-ISAC as a key enabler for 6G networks and advances intelligent infrastructures like IoT and smart cities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".