Cooperative relaying in multi-antenna fixed relay networks
Bibliographic record
Abstract
Space, cost, and signal processing constraints, among others, often preclude the use of multiple antennas at wireless terminals. This paper investigates distributed decode-and-forward fixed relays (infrastructure-based relaying) which are engaged in cooperation in a two-hop wireless network as a means of removing the burden of multiple antennas on wireless terminals. In contrast to mobile terminals, the deployment of a small number of antennas on infrastructure-based fixed relays is feasible, thus, the paper examines the impact of multiple antennas on the performance of the distributed cooperative fixed relays. Threshold-based maximal ratio combining (MRC) and threshold-based selection combining (SC) of these multiple antenna signals are studied and analyzed. It is found that the end-to-end (E2E) error performance of a network which has few relays with many antennas is not significantly worse than that which has many relays each with a fewer antennas. Obviously, the former network has a tremendous deployment cost advantage over the latter. It is also observed that the E2E error performance of a network in which the multiple antennas at relays are configured in SC fashion is not significantly worse than that in which MRC is used. For implementation, SC presents a significantly lower complexity and cost than a full-blown MRC. The analysis in this paper uses the versatile Nakagami fading channels in contrast to the Rayleigh model used in most previous works
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".