Analysis of Superimposed LoRa in Multi-User Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emergence of Internet-of-Things (IoT) has enabled the connectivity of billions of smart devices and sensor nodes to the Internet. Such a large number of connected devices requires the development of new energy efficient and long-range technologies for the successful realization of IoT applications. Within this context, long range (LoRa) has emerged as one of the prominent low power wide area network (LPWAN) technologies that is envisioned to accommodate the future IoT requirements. Nevertheless, in dense deployments, LoRa has limited capacity since it adopts the ALOHA random access protocol which suffers from unavoidable collisions. Therefore, recently, non-orthogonal multiple access (NOMA) has been integrated with LoRa in order to improve its spectral efficiency by multiplexing users in the power domain. This work studies the performance of NOMA-enabled LoRa networks in both AWGN and Rayleigh fading channels. Finally, the effect of inter-spreading factor interference is investigated in order to highlight on the impact of the imperfect orthogonality of LoRa spreading factors.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it