A novel implementation of sequential output based parallel processing - orthogonal wavelet division multiplexing for DAS on SDR platform
Bibliographic record
Abstract
The distributed antenna system (DAS) concept promises to enhance the capacity and diversity of next-generation wireless communication networks, due to the inherently added micro and macro diversity. Today, DAS is widely employed in cutting edge cellular communication to cover dead spots. The complexity of Orthogonal Frequency Division Multiplexing (OFDM) makes it challenging to build efficient and cost effective DAS systems. Orthogonal Wavelet Division Multiplexing (OWDM) is a system that has been proposed as an alternative to OFDM. In this paper we present a novel Sequential Output based Parallel Processing (SBPP) architecture for DAS using OWDM scheme. The experimental evaluation was done on a virtual test bed that includes one central processor with multiple antenna nodes and multiple mobile stations. The novel architecture shows a new approach that could provide a more flexible replacement for OFDM with reduced Peak to Average Power Ratio (PAPR) and Inter-Carrier Interference (ICI), while still maintaining the same channel capacity characteristics as OFDM. The architecture also demonstrates an efficient resource optimization technique based on LTE-driven data rate requirements for multimedia applications.
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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 | 0.001 | 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".