Transmitter precoding to cancel inter-relay interference in AF systems with successive transmissions
Bibliographic record
Abstract
This paper presents an inter-relay interference (IRI) cancellation scheme in amplify-and-forward (AF) cellular-type networks with successive downlink forwarding. In these half-duplex alternate relaying systems with two hops, the wireless medium in both hops is always utilized through the simultaneous transmissions of the base station (BS) and one of the relays which in turn causes IRI. In this work, the processing to mitigate IRI is performed exclusively at the BS using channel state information (CSI) between the BS and the multiple relays supporting corresponding receivers. The proposed linear precoding exploits the principles of signal alignment at the BS and considers recursive characteristics of IRI. The scheme performance is affected by the noise accumulation which is controlled in this paper by (i) scheduling relays as to benefit from spatial attenuation of signals and (ii) periodically re-initializing BS transmissions where some of the time slots are not utilized for the BS transmissions. Simulation results show the ability of the new scheme to fully cancel IRI as well as demonstrate the performance trade-offs between the bit error rate (BER) improvements and the loss in throughput efficiency due to flushing.
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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 | 0.000 | 0.001 |
| 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".