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 6dBin BER versus Energy per Bit to Noise ratioEb/Nocurves. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".