A Power and Spectrum Efficient Uplink Transmission Scheme for QoS-Constrained IoT Networks
Bibliographic record
Abstract
<div>Abstract—Non-orthogonal multiplexing (NOM) is a novel superposition coding inspired scheme that has been recently proposed for improving the power, spectrum efficiency and delay of wireless links with packet error rate (PER) constraints. Despite its efficiency, restricting the number of multiplexed packets to two limits the throughput improvement to 100%. Therefore, this work presents a novel NOM design with unlimited number of multiplexed packets by manipulating the repeated transmissions in automatic repeat request (ARQ) to enhance the power and spectrum efficiency by multiplexing new and repeated packets while taking into account the channel conditions and varying the power per packet in different transmissions. The proposed scheme employs an efficient heuristic algorithm to perform the power assignment and multiplexing decisions. Moreover, the complexity of the proposed NOM can be controlled by enforcing a limit on the maximum number of multiplexed packets per transmission, making it suitable for different types of Internet of Things (IoT) nodes with various computational capabilities. The obtained results demonstrate the effectiveness of proposed scheme, which offers up to 200% spectral efficiency improvement at moderate signal to noise ratios (SNRs), and up to 700% at high SNRs. Furthermore, the new scheme can reduce the transmission power consumption by up to 6 dB in the high SNR region.</div>
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".