An application of multi-wavelet packets in digital communications
Bibliographic record
Abstract
Bandwidth efficiency and the ability to combat the effects of frequency selective channels are critical in the design of digital modulation schemes. To improve bandwidth utilization and to avoid ISI caused by time dispersion in the channel, a new multi-wavelet packet modulation (MWPM) scheme is proposed. The MWPM is based on orthogonal multi-wavelet packets (MWP). In particular, the orthogonality in MWP is exploited to guarantee the demodulation of the overlapped MWP coded signals in time and frequency domains. A practical implementation of the MWPM transceiver is proposed by employing matrix filter banks. For MWP with multiplicity r, the bandwidth efficiency is increased r times compared to scalar wavelet packet modulation or the conventional FFT based OFDM system. Similarly, as these prototype schemes, the MWPM is implementable either as a binary or a multilevel scheme. As a multi-carrier modulation, MWPM can mitigate the time dispersiveness in the channel effectively because the symbol duration can be extended as required by the flexible structure of MWPM. Using two orthogonal MWPs introduced by J. Geronimo et al. (see J. Approx. Theory, vol.78, p.373-401, 1994) and C. Chui and J. Lian (Ctr. for Approximation Theory, 1995), the corresponding MWPM are constructed. The performance for these MWPM in AWGN and flat Rayleigh fading channels is analyzed and simulated using binary phase shift keying (BPSK) in each subcarrier. The simulation results demonstrate the effectiveness of the proposed MWPM scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".