Efficient Receive Antenna Selection Algorithms and Framework for Transmit Zero-Forcing Beamforming
Bibliographic record
Abstract
MIMO wireless downlinks using transmit zero-forcing beamforming (TZFBF) with MTtransmit antennas can serve up to K = MTreceivers, each equipped with one antenna. To maximize the sum rate, waterfilling can be used. It is shown that unlike classical waterfilling, TZFBF waterfilling cannot simply drop the poorer spatial modes during optimization. Instead, receive antenna selection (RAS) must be incorporated and achieving the maximum sum rate requires an exhaustive search over ∑|s|(MrC|s|) = 2MT−1 iterations to find the optimal subset S of active receivers where |s| = 1,..., MT. In principle, a separate RAS algorithm can be used in conjunction with waterfilling to reduce the exponential complexity O(2MT) of the exhaustive search to linear complexity O(MT). We develop optimization algorithms that emulate classical waterfilling by progressively reducing the effects of poor spatial modes in MTiterations. They do so by performing RAS jointly during the waterfilling process at little additional complexity. By avoiding a separate RAS process, complexity is thus further reduced. For the typical case where K>MT, we propose a 2-phase framework that helps reduce the overall complexity while meeting the TZFBF dimensional constraints. Numerical results over different channel conditions are given.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.002 |
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".