Adaptive Hierarchical Modulation for Simultaneous Voice and Multiclass Data Transmission Over Fading Channels
Bibliographic record
Abstract
In this paper, a new technique for simultaneous voice and multiclass data transmission over fading channels using adaptive hierarchical modulation is proposed. According to the link quality, the proposed scheme changes the constellation size as well as the priority parameters of the hierarchical signal constellations and assigns available subchannels (i.e., different bit positions) to different kinds of bits. Specifically, for very bad channel conditions, it only transmits voice with binary phase-shift keying (BPSK). As the channel condition improves, a variable-rate adaptive hierarchical M-ary quadrature amplitude modulation (M-QAM) is used to increase the data throughput. The voice bits are always transmitted in the lowest priority subchannel (i.e., the least significant bit (LSB) position) of the quadrature (Q) channel of the hierarchical M-QAM. The remaining (log/sub 2/M-1) subchannels, called data subchannels, are assigned to two different classes of data according to the selected priority parameters. Closed-form expressions as well as numerical results for outage probability, achievable spectral efficiency, and average bit error rate (BER) for voice and data transmission over Nakagami-m fading channels are presented. The adaptive techniques employing hybrid binary shift keying (BPSK)/M-ary AM (M-AM) and uniform M-QAM for simultaneous voice and two different classes of data transmission are also extended. Compared to the extended schemes, the new proposed scheme is spectrally more efficient for data transmission, while keeping the same outage probability for voice and data (both classes) as the scheme employing BPSK/M-AM. The new scheme also provides, as a by-product, a spectrally efficient way of transmitting voice and a single-class data.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".