Limited-Feedback Precoding for Closed-Loop Multiuser MIMO OFDM Systems with Frequency Offsets
Bibliographic record
Abstract
Frequency offsets negatively impact the performance of closed-loop multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Particularly, when multiple users are active, the impact can be high. Linear precoding and non-linear Tomlinson-Harashima precoding (THP) are thus developed for spatially-multiplexed multiuser OFDM and orthogonal space-time block-coded (OSTBC) OFDM. The proposed precoders employ a limited feedback structure, which is implemented with a shared codebook of precoding matrices, and only the index of the selected optimal matrix is fed back to the transmitter. The conventional limited feedback design criterion for flat-fading MIMO channels is only applicable to single-user OFDM without frequency offsets. We show that the ICI matrix due to frequency offset does not impact users precoding individually, and precoding on a per-subcarrier basis is possible. Exploiting this property, the conventional design is generalized to multiuser OFDM with frequency offsets. Nonlinear precoding uses a modulo arithmetic precoding matrix (which reduces the power efficiency loss inherent in linear precoding and leads to a lower error rate) and outperforms linear precoding. Our precoders not only offer significant bit error rate (BER) improvement for spatially-multiplexed multiuser MIMO OFDM with frequency offsets, but are equally effective for both OSTBC MIMO OFDM and spatially correlated channels.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".