Opportunistic Mobile Crowdsensing for Interference Overhead Reduction and Throughput Maximization
Bibliographic record
Abstract
Accurate onsite interference information acquisition is essential for the successful deployment of a millimetre wave (mmWave) empowered 5G Small Cell (SC) networks, given the sensitivity of signal quality to interference caused by the significant attenuation of high-frequency signals. Opportunistic mobile crowdsensing (OCS) arises as a cost-effective technology for onsite interference sensing, which schedules communication opportunities of mobile devices to perform sensing tasks. However, conventional OCS methods follow a routine schedule for updating interference levels for each user at fixed time intervals. This approach leads to unnecessary sensing overhead and degrades system performance. In this study, we utilize the interference spatial correlation to reduce the sensing frequency and devise a novel low-overhead situation-dependent OCS scheme in the indoor location-aware SC communication system. Specifically, we schedule a subset of users to perform interference sensing and leverage interference spatial correlation to predict the interference conditions of adjacent users, thus reducing the sensing overhead. Then, we jointly optimize the OCS sensing scheduling and communication time allocation to maximize the throughput of all users. Considering the non-convex challenge presented in the formulated problem, the sequential convex approximation (SCA) and block coordinate descent (BCD) methods are proposed to solve this problem efficiently. Through extensive simulation analysis, our novel low-complexity algorithm demonstrates superior performance compared to other baseline methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".