Cooperative MIMO multiple-relay system with optimised beamforming and power allocation
Bibliographic record
Abstract
In this paper we investigate optimised power allocation over two-hop multiple-input multiple-output (MIMO) fixed multiple relays for a given power budget. Optimum beamforming weights under the total sum power constraint for all relays, as well as maximum per-relay power constraint, are found to maximise the received SNR at destination. Results show that optimising the allocation of power improves system performance, especially foe highly unbalanced links. The system with optimised power allocation can outperform a two-hop multiple relay system using uniform power allocation and distributed beamforming at the expense of increased computational complexity. We also study the threshold decode-and-forward fixed relay network with beamforming, which is more reliable than conventional decode-and-forward relaying. The impact of multiple antennas on the outage probability of cooperating fixed relays is considered. It is determined that increasing the number of relays and antennas at each relay increases capacity. The outage probability of threshold maximal-ratio combining and threshold selection combining for multiple-antenna multiple fixed relays is also derived. It is observed that the performance of the relay network with selection combining is close to that of the network with maximal-ratio combining, but the former is less complex and less expensive to implement.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".