Transmit Antenna Selection for Sum Rate Maximization in Transmit Zero-Forcing Beamforming
Bibliographic record
Abstract
MIMO wireless downlinks using transmit zero-forcing beamforming (TZFBF) with MT transmit antennas can serve up to K = MT users, each equipped with one antenna. To maximize the channel sum rate, it has been shown that user selection, which is akin to receive antenna selection (RAS) in this case, is required along with waterfilling to find the optimal subset of active receivers Sa, where |Sa| = 1,...,MT. To implement TZFBF, channel state information is required at the transmitter (CSIT). When CSIT is available, it is known that additional constraints imposed on the transmitter will reduce the system sum rate. Despite this, transmit antenna selection (TAS) provides a means of increasing the sum rate in some cases when sub-optimal RAS algorithms are used. The mechanism works by assisting the RAS search path to get out of a local maximum. The proposed method requires further RAS to follow any prior TAS process and the restoration of any transmit antennas that were removed. An analysis is provided to give insight to the proposed method. The analysis and scheme are applicable to any sub-optimal RAS algorithm and guidelines on decoupled search strategies are given. The analysis also affirms the statement that given CSIT, TAS does not help improve the sum rate of TZFBF, regardless of the channel condition and signal-to-noise ratio when optimal RAS is already done. This means that joint exhaustive RAS-TAS searches are not needed to achieve the optimal sum rate and instead, only exhaustive RAS or user selection search is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".