Joint Relay Beamforming and Receiver Processing for Multi-Way Multi-Antenna Relay Networks
Bibliographic record
Abstract
We consider a multi-way relay network with multiple users exchanging information with each other via a multi-antenna relay. The multi-way relaying strategy consists of one multiple access phase and multiple broadcast phases. We jointly design relay beamforming matrices and users' linear processing receivers in the broadcast phases to maximize the minimum signal-to-interference-and-noise ratio (SINR) under the relay power budget. For the non-convex joint optimization problem, we propose to solve it by iteratively optimizing the relay beam matrices and receiver processing matrices in two sub-problems. For the receiver processing, both maximum-ratio-combining (MRC) receiver and zero-forcing (ZF) receiver are designed. We show that our iterative approach with the MRC receiver leads to a local maximum for the original joint optimization problem, while the ZF receiver has the computational advantage with a lower complexity. To further improve the performance, we design the successive interference cancellation at each user's receiver based on the SINR criterion to sequentially decode symbols from other users. Simulation shows that our proposed algorithm for joint design provides substantial improvement in the sum rate than the existing methods that use the sum rate as the design objective. Finally, we investigate the performance of our proposed algorithm under partial channel state informations (CSIs). We show in simulation that using quantized CSIs at each receiver only incurs a small performance loss for the typical range of relay channel quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".