Connection Density Maximization of Narrowband IoT Systems With NOMA
Bibliographic record
Abstract
Narrowband Internet of Things (NB-IoT) provides energy-efficient communications with extended coverage for the low data rate IoT devices. In this paper, we propose a power-domain non-orthogonal multiple access (NOMA) scheme for the NB-IoT systems to enhance the connection density by allowing multiple IoT devices to simultaneously access one subcarrier. We consider both single-tone and multi-tone transmission modes of the NB-IoT systems, where each device can access a single subcarrier or a bond of contiguous subcarriers, respectively. We formulate joint subcarrier and power allocation problems for both transmission modes to maximize the connection density while taking the quality of service requirements and the transmit power constraints of IoT devices into account. We solve the single-tone nonconvex mixed integer programming problem by transforming it into a mixed integer linear programming problem to obtain the optimal solution. The multi-tone problem is solved by using the difference of convex programming approach to obtain a close-to-optimal solution. We also propose low-complexity heuristic algorithms to solve both problems in a suboptimal manner. The simulations results show that our proposed scheme increases the connection density of NB-IoT systems by 87% in the single-tone mode and by 24% in the multi-tone mode compared to orthogonal multiple access.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".