Collaborative Uplink Transmit Beamforming With Robustness Against Channel Estimation Errors
Bibliographic record
Abstract
We consider the uplink of collaborative wireless communication systems, where multiple relay terminals decode the signal of a nearby user and forward it to a distant single-antenna base station. We present a collaborative uplink transmit beamforming strategy that can be employed at the relay terminals to provide robustness against uncertainties in the channel state information. The proposed beamforming scheme is obtained using the available knowledge about the second-order statistics of the channel and the possibly erroneous channel state information. The beamforming weight vector is derived by minimizing the total transmitted power subject to a constraint that preserves the received signal at the base station for all the channel realizations within a prescribed uncertainty set. We present two beamforming algorithms based on different mathematical descriptions of the uncertainty set. Both algorithms can be applied to line-of-sight (LOS) propagation and flat-fading channels. In the first algorithm, the robust beamforming vector is computed at the base station using the uplink data and fed back to the cooperating relay terminals. This centralized processing scheme allows any additional convex constraint to be easily incorporated into the beamforming strategy. In the second algorithm, the beamforming vector of each terminal is locally computed using the available knowledge about the terminal's channel and a single parameter (Lagrange multiplier) that is broadcast from the base station to all the cooperating terminals. Simulation results are presented, showing the superior performance of our proposed algorithms compared with classical transmit beamforming techniques in both LOS propagation and flat-fading channels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
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".