A Random Channel Sounding Decision Feedback Receiver for Two-Way Relay Communication With Pilotless Orthogonal Signaling and Physical-Layer Network Coding
Bibliographic record
Abstract
We propose a decision feedback (DFB) receiver at the relay of a two-phase (2P) two-way relay (TWR) communication system that employs pilotless orthogonal modulation (such as frequency-shift keying) in the uplink and physical-layer network coding over finite field in the downlink. The proposed relay receiver is able to attain a performance very close to that of an ideal coherent detector in the presence of time-selective Rayleigh fading and additive white Gaussian noise in the uplinks. It exploits the fact that when the uplink symbols from the users are different, then the fading gains affecting these symbols can be separated and individually tracked at the relay. In essence, the proposed receiver performs random channel sounding although no actual pilots are transmitted. The channel estimates obtained this way can then be subsequently used in a coherent detector to improve the reliability of the relay detected data. To ensure fast convergence, we propose to kick start the DFB receiver using a partial-coherent detector developed earlier by the authors. We compare the performance of the proposed system against a similar 2P-TWR system that employs differential phase-shift keying (DPSK) in the uplink and DFB multiple-symbol differential detection at the relay. We found that the proposed pilotless orthogonal modulation system can actually attain a significantly lower bit error rate (BER) than its DPSK counterpart. For static fading and a BER of 10-3, the signal-to-noise ratio (SNR) gap between the two approaches is 1 dB in the binary case and 8 dB in the quaternary case. These gaps increase further with time-selective fading.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".