Performance Comparison of OFDM and DFT-s-OFDM in the THz-Band Communications Channels
Bibliographic record
Abstract
The terahertz (THz) band is a promising frequency band that ranges from 0.3 THz to 10 THz and being considered for the next generation 6G wireless networks due to the large available bandwidth and potentially ultra fast data rates. THz channels and systems are unique in three aspects: 1) high path loss; 2) rich scattering from rough surfaces; 3) severe phase noise from local oscillators. These pose questions on what physical waveforms should be used in THz channels. Since orthogonal frequency division multiplexing (OFDM) and discrete Fourier transform spread OFDM (DFT-s-OFDM) are adopted waveforms in 5G new radio (NR) and 4G, their possible use in the future generation wireless networks is an open question. In this paper, we first review a THz channel model for accurate representation of THz channels. Then uncoded and coded OFDM and DFT-s-OFDM systems are investigated for their performance in THz channels, demonstrating their unique behaviors in these channels. The impact of phase noise is also examined, and a non-iterative compensation method slightly modified from the literature is used to mitigate the phase noise effect. DFT-s-OFDM shows advantages over OFDM in the studied THz environment. The simulation results in this paper can be used as a benchmark for future studies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".