Cooperative Amplify-and-Forward Beamforming for OFDM Systems with Multiple Relays
Bibliographic record
Abstract
In this paper, we propose frequency-domain (FD) and time-domain (TD) beamforming (BF) schemes for cooperative orthogonal frequency division multiplexing (OFDM) networks with multiple amplify-and-forward relays. Whereas for FD-BF the BF weights are applied in the FD, for TD-BF cyclic BF filters (C-BFFs) are used on the TD signal avoiding discrete- time Fourier transform operations at the relays and drastically reducing the required amount of feedback from the receiver to the relays. Adopting the average mutual information (AMI) per sub-carrier as optimality criterion, we show that the direction of the optimal FD-BF weights can be obtained in closed-form and that the optimal sub-carrier power allocation (PA) problem is convex. For solution of the PA problem an interior point method and a bisectional search dual method are provide. Furthermore, for solution of the C-BFF optimization problem an efficient gradient algorithm is proposed. Simulation results for IEEE 802.11n channels show that TD-BF with short C-BFFs closely approaches the performance of FD-BF and outperforms direct transmission without relaying.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".