A Polynomial Matrix SVD Approach for Time Domain Broadband Beamforming in MIMO-OFDM Systems
Bibliographic record
Abstract
Singular value decomposition (SVD) is a useful technique to mitigate co-space interference (CSI) in multiple input multiple output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) scheme. The SVD based frequency-domain broadband beamforming (FBBF) approach uses the beamforming matrices in both transmitter and receiver sides for each subchannel matrix. However, when the MIMO- OFDM system has a large number of subchannels, the FBBF technique is very complex due to computing the SVD of each subchannel separately. In this paper, we propose a new approach to eliminate CSI and intersymbol interference (ISI) in time- domain and frequency-domain, respectively. By the use of polynomial matrix SVD based on SBR2 algorithm, the MIMO- OFDM system is decomposed to the parallel decoupled single input single output (SISO) OFDM systems. In this way, the CSI is eliminated by time-domain broadband beamforming (TBBF) in both transmitter and receiver sides and ISI is mitigated in each SISO-OFDM system by frequency-domain equalization. The performance of the proposed method is evaluated by computer simulations for fixed broadband wireless communication system developed based on the WiMax. Simulation results show that the proposed method achieves good performance in concerned SNR interval.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".