Receiver Algorithms for Satellite-Terrestrial ISAC-RIS Systems
Bibliographic record
Abstract
The concept of integrating satellite and terrestrial wireless networks has emerged in recent years as one of the focal points for addressing the high demand for global connectivity. Meanwhile, integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RISs) have been studied to enable some of the applications future wireless networks are envisioned to support. To reflect these recent research directions, this paper proposes a satellite-terrestrial integrated network (STIN) model, which comprises an uplink terrestrial ISAC-RIS scenario and a downlink satellite transmission to the ISAC base station. Based on the system model, a mathematical formulation is provided to characterize the communication symbol detection and target estimation problems. Further, the maximum likelihood (ML) detection algorithm is proposed to jointly detect the symbols transmitted by the terrestrial user equipment and satellite. In an interference cancellation scheme, the reflection coefficients of the ISAC target are estimated after symbol detection using the minimum mean-squared error estimation (MMSE) method. The computational complexities of ML and MMSE algorithms are analyzed in terms of the number of real additions and real multiplications involved in their operations. Finally, Monte Carlo simulations are presented to support the findings.
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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) | 0.001 | 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 | 0.001 | 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".