Multiple-antenna multiple-relay system with precoding for multiuser transmission
Bibliographic record
Abstract
Multi-hop relaying will play a central role in next generation wireless systems. In this paper a novel relaying strategy that uses multiple-input multiple-output (MIMO) relays in a two-hop wireless network supporting multiuser transmission is proposed. The fixed relays linearly process the received signal, decode and forward it to multiple users. This relaying strategy employs MIMO spatial multiplexing to achieve high spectral efficiency and improve link capacity in cellular networks. In this paper the case when source (base station) and all the relays employ multiple antennas and each user has only one antenna is studied. New lower and upper bounds on the achievable sum rate for this architecture are derived. Zero-forcing dirty paper coding (ZF-DPC) at the base station is assumed and the direct link between the base station and users is neglected. We propose to jointly design precoding at the base station and linear processing at the relays to improve throughput subject to power constraints at the source and relay transmitters. The impact of multiple relaying on the achievable sum rate lower bound is investigated. The proposed lower bound improves on earlier sum rate lower bounds that were derived for simpler cases of relaying.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".