Bibliographic record
Abstract
Wireless communication is a fundamental aspect of everyday life. Modern mobile applications such as the Internet of Things (IoT), high-resolution video streaming, connected cars, smart cities, and telehealth care have a universal presence these days. The growth in these applications will continue to require higher data rates, large bandwidth, increased capacity, low latency and high throughput. As the Radio Frequency (RF) spectrum is a limited resource, considerable effort has been expended in the search for more efficient techniques for transmitting data through limited bandwidth resources. The key component of meeting this requirement is employing an efficient adaptive modulation scheme. Adaptive M-ary Quadrature Amplitude Modulation (M-QAM) techniques have been developed to deliver better Bit Error Rate (BER) performance and higher Spectral Efficiency (SE) by taking advantage of the time-varying nature of wireless fading channels. This work further enhances the adaptive M-QAM by employing an efficient Three-Dimensional (3D) adaptive modulation scheme to significantly increase the BER and SE of the wireless networks. This paper introduces a novel 3D version of adaptive M-QAM that significantly improves the BER performance and, more importantly, doubles the SE. This results in also doubling the network capacity and throughput. Simulation results show that the SE is doubled, and the gain is increased by 6 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dB</i> in BER versus Energy per Bit to Noise ratio <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eb</i>/<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">No</i> curves. This study proposes that this improvement can be further enhanced by transmitting and receiving through more than two planes, using more than two cross-polarized antennas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".