Photonics based frequency hopping spread spectrum system for secure terahertz communications
Bibliographic record
Abstract
Terahertz (THz) spectrum (100 GHz-10 THz) is considered the next frontier in the design of high-speed wireless communication systems. While the high-power THz sources have commercially become available, it increases the possibility of developing THz jammers to disrupt the THz communication link. Therefore, the development of novel anti-jamming solutions is the need of the hour. In this work, we present the photonics-based THz communication system and demonstrate the frequency hopping spread spectrum (FHSS) technique which acts against the single/multi-tone jamming attack in the frequency window of 110 GHz-170 GHz. By tuning the output wavelength of the distributed feedback lasers, the THz carrier frequencies are swept back and forth within the scanning window. The frequency tuning range was measured for different scanning rates of the laser which decreases rapidly with the increase in the scanning rate. Next, we demonstrate the THz FHSS technique in a real-time communication system by transmitting a 6 Gbps NRZ signal in both wireless and THz-fiber-based links within the link distance of 1.75 m. We experimentally found that the measured bit error rate in the THz FHSS system is the time average of the measured BER for individual carrier frequencies within the hopping frequency window. By combining with the forward error correction codes and by using the tunable filter in the receiver, we believe that the proposed technique will offer a novel and compact solution against the single/multi-tone jammer for high-bit rate THz communications.
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.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".