Asynchronous Two-Way MIMO Relaying: A Multi-Relay Scenario
Bibliographic record
Abstract
We consider a single-carrier asynchronous relay network where two transceivers exchange information with the help of multiple multi-antenna relays. The network is assumed to be asynchronous, meaning that the signal transmitted by any of the two transceivers arrives at different relays with different delays and also signals transmitted by different relays arrive at any of the two transceivers with different delays. We further assume that each relay obtains the vector of the relay transmit signals via multiplying the vector of the relay received signals by a beamforming matrix. For such an asynchronous two-way network with multi-antenna relays, our goal is to obtain symmetric relay beamforming matrices and the transceivers' transmit powers such that the total power consumed in the entire network is minimized while guaranteeing given data rates at the two transceivers. To this end, we develop a model for the end-to-end channel and use this model to solve the total power minimization problem. Assuming symmetric relay beamforming matrices, we present a computationally efficient solution to this problem. Our simulation results suggest that for a given total number of antennas, there appears to be an optimal number of antennas per relays which results in the lowest power consumption in the network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".