Joint Design of Multiple Non-Regenerative MIMO Relaying Matrices With Power Constraints
Bibliographic record
Abstract
This paper investigates the joint design of multiple non-regenerative multiple-input multiple-output (MIMO) relaying matrices, with the purpose of minimizing the mean square error (MSE) between the transmitted signals from the source and the received signals at the destination. Two types of constraints on the transmit power of the relays are considered separately: 1) a weighted sum power constraint, and 2) per-relay power constraints. As opposed to using general-purpose interior-point methods, we exploit the inherent structure of the problems to develop more efficient algorithms. Under the weighted sum power constraint, the optimal solution is expressed as a function of a Lagrangian parameter. By introducing a complex scaling factor at the destination, we derive a closed-form expression for this parameter, thereby avoiding the need to solve an implicit nonlinear equation numerically. Under the per-relay power constraints, the optimal solution is the same as that under the weighted sum power constraint if particular weights are chosen. We then propose an iterative power balancing algorithm to compute these weights. In addition, under both types of constraints, we investigate the joint design of a MIMO equalizer at the destination and the relaying matrices, using block coordinate descent or steepest descent. The bit-error rate (BER) simulation results demonstrate that all the proposed designs, under either type of constraints, with or without the equalizer, perform much better than previous methods.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".