LDPC Coded Wireless Networks with Adaptive Spectral Efficiency
Bibliographic record
Abstract
Low-density parity-check (LDPC) codes offer a very powerful error correction technique which allows data transmission in wireless networks at rates near the channel capacity with arbitrarily low probability of error. In this paper, we design a class of linearly encodable LDPC codes with adaptive code rates, i.e. the code rate can be adapted according to channel conditions to maximize the total capacity. Since a unique mother parity check matrix is used to construct LDPC codes with several code rates, the great advantage of the proposed codes is that a single universal encoder (decoder) is adequate to encode (decode) multi-rate codes, which makes it possible to efficiently implement multi-rate LDPC codes in a subscriber station. The implementation results into field programmable gate array (FPGA) devices indicate that a universal layered encoder for LDPC codes with 9 code rates is capable of reaching a throughput above 1.2 Gigabit per second by using 138 exclusive- OR gates and a master clock of 100 MHz. By combining multilevel modulation with the designed multi-rate LDPC codes, a transmission scheme with adaptive spectral efficiency is proposed. The simulation results indicate that the schemes 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 signal-to-noise ratios (SNR) per bit of 5, 6, 10, 14, and 16 dB, respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".