Deep Learning-Based Receivers for DFT-s-OFDM in Access and Backhaul Communication
Bibliographic record
Abstract
Discrete Fourier Transform Spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) is a promising waveform for both access and backhaul communication in modern wireless systems, due to its low peak-to-average power ratio. However, its performance is impacted by hardware impairments, particularly oscillator phase noise (PN). This paper presents tailored solutions for mitigating PN in DFT-s-OFDM systems, with distinct approaches for the access layer and backhaul communication. For the access layer, we use CoDiPhy, a deep learning (DL)-based receiver that jointly performs channel estimation, equalization, and PN compensation via a conditional denoising diffusion model. As CoDiPhy’s complexity increases with larger FFT and constellation sizes typical in backhaul scenarios, we propose a DL-aided (DLA) PN compensation approach to address the PN issue while reducing computational burden. The DLA PN method outperforms traditional linear interpolation (LI) by exploiting information from all received signals within a pilot section. Simulation results show that CoDiPhy achieves near-optimal performance in the access layer, with coded BERs within 0.2 dB (at a 10−6BER) of the ideal LMMSE solution. In the backhaul scenario, the DLA PN scheme significantly outperforms LI, enabling 1024-QAM with less than 0.5% pilot overhead.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".