Neural Network Aided TeraHertz Backhaul Communications Using One-Bit ADCs
Bibliographic record
Abstract
TeraHertz (THz) communication is considered as a promising technology to satisfy the ever-increasing demand for high-rate services in the next generation of wireless communication systems. However, the high-speed high-resolution analog-to-digital converters (ADCs) of THz systems are power hungry and complex to implement. Low-resolution ADCs are considered as an efficient solution to reduce the cost and complexity of THz systems. Especially, one-bit ADCs are of particular interest because they require only a comparator and an automatic gain control is not needed anymore. In this paper, we investigate a point-to-point THz backhaul communication system wherein one-bit ADCs with temporal oversampling are deployed at the receiver side. The bit-error-rate (BER) performance of the proposed system is evaluated through an end-to-end link level simulation. Specifically, we propose a convolutional neural network (CNN) based receiver which considers not only the nonlinearity caused by the one-bit ADCs but also the correlation between the received samples due to the oversampling. Our results demonstrate that the proposed CNN-based receiver can considerably improve the BER performance especially at moderate signal-to-noise ratio (SNR) values. The idea of far-field digital dithering is also proposed to maintain the BER performance at high-SNR regime.
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) | 0.001 | 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".