Capacity and Performance of Adaptive MIMO System Based on Beam-Nulling
Bibliographic record
Abstract
In this paper, we propose a scheme called "beam-nulling" for MIMO adaptation. In the beam-nulling scheme, the eigenvector of the weakest subchannel is fed back and then signals are sent over a generated subspace orthogonal to the weakest subchannel. Theoretical analysis and numerical results show that the capacity of beam-nulling is closed to the optimal water-filling at medium SNR. Additionally, signal-to-interference-plus-noise ratio (SINR) of MMSE receiver is derived for beam-nulling. Then the paper presents the associated average bit-error rate (BER) of beam-nulling numerically which is verified by simulation. Simulation results are also provided to compare beam-nulling with beamforming. To improve performance further, beam-nulling is concatenated with linear dispersion code. Simulation results are also provided to compare the concatenated beam-nulling scheme with the beamforming scheme at the same data rate. Additionally, the existing beamforming and new proposed beam-nulling can be extended if more than one eigenvector is available at the transmitter. The new extended schemes are called multi-dimensional (MD) beamforming and MD beam-nulling. Theoretical analysis and numerical results in terms of capacity are also provided to evaluate the new extended schemes. Simulation results show that the MD scheme with LDC can outperform the MD scheme with STBC significantly when the data rate is high.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".