Adaptive LDPC codes for MIMO transceiver with adaptive spectral efficiency
Bibliographic record
Abstract
By using a multi-rate low density parity check (LDPC) code, we propose an LDPC coded multiple-input multiple-output (MIMO) transceiver with adaptive spectral efficiency which allows data transmission in wireless networks at rates near the channel capacity with arbitrarily low probability of error. Equipped with multiple transmit and multiple receiver antennae, the proposed LDPC coded MIMO transceiver can maximize either coding gain with space-time diversity technique or channel capacity with space-time multiplexing technique. Since the proposed multi-rate LDPC code is constructed based on a single master parity check matrix using a row-removing approach, a single universal encoder (decoder) suffices to handle all rates. Thus, the proposed approach makes the proposed scheme feasible in a subscriber station, such as a cellular phone, to maximize the total channel capacity with a guaranteed quality-of-service. The simulation results indicate that an LDPC coded MIMO transceiver with a spectral efficiency of 1, 2, 3, 4, and 5 bits/symbol/Hz can achieve extremely reliable transmission in a Rayleigh fading channel at the signal-to-noise ratio (SNR) per bit of −2.5, 0.25, 2, 4.75, and 6.5 dB, respectively, when two transmit and two receiver antennae are used for space-time diversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".