Joint source-relay design in multi-antenna multi-relay networks with prefixed receivers
Bibliographic record
Abstract
In this paper, we consider the problem of joint source and relay precoder design for multi-antenna multi-relay networks with a prefixed receiver. Prefixed receivers are of practical interest since they enable low complexity at the end-user's receiver as well as backward compatibility. To compute the source and relay precoders, we propose to maximize the worst stream signal-to-interference-plus-noise ratio (SINR) at the output of the receiver subject to source and relay transmit power constraints. The problem is a non-convex optimization problem. To solve it, we propose an iterative alternating algorithm, where, in each iteration, we compute the precoders alternately. We formulate the resulting optimization problems for the computation of the source and relay precoders as second order cone programming (SOCP) and semidefinite relaxation (SDR) problems, respectively. Moreover, we provide sufficient conditions for the convergence of the proposed iterative alternating algorithm to a fixed point. Simulation results show that the performance of the proposed algorithm is close to the performance achieved if the source, relay, and receiver filters are jointly optimized.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".