Beamforming for Multiuser MIMO-OFDM Interference Channels With Multipath Diversity
Bibliographic record
Abstract
This paper presents three beamforming designs for multiuser multiple-input and multiple-output with orthogonal frequency-division multiplexing, where the transmit and receive beamformers are obtained iteratively with closed-form steps. In the first case, the transmit (Tx) beamformers are set and the receive (Rx) beamformers are calculated. It works by projecting the Tx beamformers into a null space of appropriate channels. This eliminates one interference term for each user. Then the Rx beamformer for each user maximizes its instantaneous signal-to-noise ratio (SNR) while satisfying an orthogonality condition to eliminate the remaining interferences. The second case is jointly optimizing the Tx and Rx beamformers from constrained SNR maximization. It uses the results from the first case. The third case is also for joint optimization of Tx-Rx beamformers but combines constrained SNR and signal-to-interference-plus-noise ratio maximization. The minimum number of antennas required is derived as part of the formulation. All cases can include a linear constellation precoder for extracting multipath diversity. Finally, the required feedback rates are derived and compared to existing beamforming methods. Using the standardized statistical channel model for IEEE 802.11n, our simulations demonstrate faster beamforming, improved error performance, and the ability to extract multipath diversity which is not possible in the least-square approach.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".