Low Complexity Super-Resolution OTFS-Assisted ISAC Framework for THz Communication
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) in terahertz (THz) is a promising technology that supports simultaneous blue terabit-per-second data transmission and millimeter-level precision sensing. However, THz ISAC systems face significant challenges, including severe Doppler shifts and reduced power amplifier efficiency due to a high peak-to-average power ratio (PAPR). This paper presents a super-resolution orthogonal time frequency space-assisted ISAC (SR-OTFS-ISAC) framework aimed at enhancing robustness against Doppler effects in multipath THz channels. The framework effectively addresses both integer and fractional delay and Doppler. The proposed framework incorporates a low-complexity super-resolution sensing technique that begins with denoising the received signals using a super-resolution deep neural network (SR-DNN) for precise path detection. This is followed by a coarse estimation of the dominant paths using the max-path phase. This estimate is then refined using an alternative optimization (AO) method to improve the accuracy of the delay-Doppler (DD) parameters. This approach, called the super-resolution max-path alternative optimization (SR-M-PAO) method, provides better channel estimates. These enhanced estimates support multi-target sensing, transmitter localization, and data detection using a conjugate gradient algorithm. The proposed SR-OTFS design enables channel estimation, parameters sensing and data detection within a single OTFS frame. Additionally, computational complexity is derived in terms of the required real addition and multiplication operations, offering clear insights into the proposed algorithm's efficiency. Simulation results demonstrate that the SR-OTFS-ISAC framework delivers range estimation accuracy at the millimeter scale and velocity estimation precision at the centimeter-per-second level, all while ensuring lower computational complexity. Furthermore, it achieves an approximate 5 dB reduction in PAPR compared to the literature while maintaining a robust performance bit error rate, even under fractional DD effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".