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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".