Rapid prototyping of MIMO‐OFDM based on parity bit selected and permutation spreading
Bibliographic record
Abstract
Summary In this paper, a novel MIMO‐OFDM transmission scheme is developed to effectively enable multi‐access by joint code design across multiple antennas, subcarriers, OFDM frames, and users. It achieves better spectrum efficiency while improving bit error rate performance. The proposed scheme uses either parity bit selected or permutation techniques to assign spreading codes at the transmitter side. As a result, the detection at the receiver is greatly improved because of the fact that identifying the spreading code(s) directly yields the transmitted data symbols. The paper also investigates the field‐programmable gate array implementation of the proposed algorithms; optimization techniques are proposed to reduce area, power, and time. These techniques include a pipelined architecture for inverse FFT/FFT blocks, an efficient low complexity algorithm for despreading based on counters and comparators and an optimized architecture for complex matrix inversion using Gauss–Jordan elimination (GJ‐elimination). Finally, the fixed‐point optimized field‐programmable gate array architecture for MIMO‐OFDM transceiver is developed, where the maximum allowed performance loss because of quantization is defined, the tradeoffs between BER performance and area reduction are investigated. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".