Probabilistic Analysis on QoS Provisioning for Internet of Things in LTE-A Heterogeneous Networks With Partial Spectrum Usage
Bibliographic record
Abstract
This paper investigates quality of service (QoS) provisioning for Internet of Things (IoT) in long-term evolution advanced (LTE-A) heterogeneous networks (HetNets) with partial spectrum usage (PSU). In HetNets, the IoT users with ubiquitous mobility support or low-rate services requirement can connect with macrocells (MCells), while femtocells (FCells) with PSU mechanism can be deployed to serve the IoT users requiring high-data-rate transmissions within small coverage. Despite the great potentials of HetNets in supporting various IoT applications, the following challenges exist: 1) how to depict the unplanned random behaviors of the IoT-oriented FCells and cope with the randomness in user QoS provisioning and 2) how to model the interplay of resource allocation (RA) between MCells and FCells under PSU mechanism. In this work, the stochastic geometry (SG) theory is first exploited to statistically analyze how the unplanned random behaviors of the IoT-oriented FCells impact the user performance, considering the user QoS requirements and FCell PSU policy. Particularly, to satisfy the QoS requirements of different IoT user types, the concept of effective bandwidth (EB) is leveraged to provide the users with probabilistic QoS guarantee, and a heuristic algorithm named QA-EB algorithm is proposed to make the EB determination tractable. Then, the interplay of RA between the MCells and FCells is formulated into a two-level Stackelberg game, where the two parties try to maximize their own utilities through optimizing the macro-controlled interference price and the femto-controlled PSU policy. A backward induction method is proposed to achieve the Stackelberg equilibrium. Finally, extensive simulations are conducted to corroborate the derived SINR and ergodic throughput performance of different user types and demonstrate the Stackelberg equilibrium under varying user QoS requirements and spectrum aggregation capabilities.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".