Decentralized beamforming for multi-carrier asynchronous bi-directional relaying networks
Bibliographic record
Abstract
We consider an asynchronous two-way relay network, where multiple asynchronous relays cooperate to establish a connection between two transceivers. In such an asynchronous relay network, a certain signal path (originating from one transceiver and going through a certain relay) introduces a propagation and/or relaying delay to the corresponding relayed signal. We assume that such delays are different for different signal paths which correspond to different relays. Based on this model, the end-to-end communication link can be viewed as a multi-path channel, and thus, it can cause inter-symbol-interference (ISI) at the two transceivers when the data rate is sufficiently high. To tackle such an ISI, the two transceivers are herein assumed to employ orthogonal frequency division multiplexing (OFDM) technology. The relays however use amplify-and-forward relaying to materialize a distributed beamforming scheme. For such a communication scheme, we use a max-min fair design approach to optimally obtain the relay beamforming weights and the transceivers' subcarrier powers such that the smallest subcarrier signal-to-noise ratio (SNR) ismaximized under a total power budget. Furthermore, we prove that this approach (which has been shown to equivalent to a SNR balancing scheme) leads to certain relay selection solution. We then present a semi-closed-form solution to obtain the relay beamforming weights and the associated maximum balanced SNR. Simulation results show that the performance of this solution is superior to an equal power allocation approach, where all relays and two transceivers consume the same level of power.
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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 | 0.001 |
| 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) | 0.002 | 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".