Joint Transceiver Optimization for MIMO Multiuser Relaying Networks with Channel Uncertainties
Bibliographic record
Abstract
This paper addresses the problem of amplify-and- forward (AF) relaying for multiple-input multiple-output (MIMO) multiuser relay networks. where each source transmits multiple data streams to its corresponding destination with the assistance of multiple relays. Assuming only imperfect channel state information (CSI) of all the source-relay and relay-destination links, we propose a robust approach to jointly design the source and relay precoders and the receive filters, in which the worst per-stream mean square error (MSE) is minimized subject to source and relay power constraints. The channel uncertainties are assumed to be Gaussian distributed and the well-known Kronecker model is employed to characterize the spatial correlations in the proposed design. The resultant optimization problem is nonconvex and therefore, an algorithmic solution with proven convergence is proposed by resorting to the iterative block coordinate update approach along with matrix transformation and convex conic optimization techniques. Simulation results show that the proposed joint transceiver design can achieve an improved robustness against the channel uncertainties when compared to the non-robust approaches.
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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".