Low Complexity Super-Resolution OTFS-Assisted ISAC Framework for THz Communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it