High-speed sphere decoder for wireless MIMO transceiver with adaptive modulations
Bibliographic record
Abstract
To resolve today's spectrum scarcity problem, cognitive radio network is widely expected to usher in the next wave in wireless communications. This paper has developed a high-speed sphere decoder for multiple-inputs multiple-outputs (MIMO) transceivers used in cognitive radio networks. First, a novel sphere decoding algorithm is proposed which increases the decoding speed by 30% without any noticeable performance degradation. Based on the proposed algorithm, single universal sphere decoder is designed for MIMO transceivers with adaptive modulation and variable number of antennas. Finally, a novel parallel structure is developed which increases the decoding speed n-fold by using n sphere processors. The implementation results into field programmable gate array (FPGA) device indicated that a 20-parallel decoder attained a constant speed of 500 Mbps, even if the number of antennas or the order of modulation are changed in cognitive radio networks. The de signed sphere decoder can be used in various wireless networks with MIMO antenna processing and fast adaptive modulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".