High Throughput Wireless Links for Time-Sensitive WSNs With Reliable Data Requirements
Bibliographic record
Abstract
Wireless sensor networks (WSNs) and internet of things (IoT) are expected to add a massive number of wireless devices to support various emerging applications. Consequently, adopting high throughput communications protocols is indispensable to serve such a massive number of devices. Therefore, this work presents a new framework, denoted as non-orthogonal multiplexing (NOM), that can substantially improve the throughput of wireless communications links for WSNs applications. The proposed framework utilizes power domain (PD) multiplexing to improve the throughput by recognizing that the hybrid-ARQ (HARQ) protocol and Chase combining (CC) create nonuniform power requirements for the transmitted packets. Consequently, multiple data packets can be combined and transmitted simultaneously using PD multiplexing. More specifically, the proposed framework allows combining multiple new packets, or new and retransmitted packets to increase the system throughput and reduce the delay. Moreover, to overcome channel state information (CSI) feedback limitations, a simple protocol is proposed where the number of transmitted packets is fixed for all transmission sessions. Therefore, the sensing nodes do not need to identify the number of transmitted packets for each transmission slot. The obtained results show that the proposed NOM protocol can eventually improve the link throughput by 100% at high signal to noise ratios (SNRs), and hence, reduce the delay by 50%. The system complexity and overhead are generally comparable to conventional HARQ systems, which confirms the efficiency of the proposed framework.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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".