Hybrid Multiple Access and Service-Oriented Resource Allocation for Heterogeneous QoS Provisioning in Machine Type Communications
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) has emerged as one important enabling technology for future wireless communications and services, including machine type communication (MTC). Unfortunately, supporting diverse MTC services and massive connectivity is still challenging due to the very different service requirements, scarce radio resources, limited battery capacity of MTC devices, as well as rapidly changing network conditions. In this paper, a hybrid-multipleaccess (HMA) scheme for service-oriented resource allocation scheme is proposed in supporting diverse MTC services for resource constrained devices and networks. In the proposed scheme, HMA allows MTC devices to choose a suitable type of multiple access technique according to their channel conditions, power constraints, and quality of service (QoS) requirements. To support service-oriented resource allocation, the physical network is firstly sliced into several virtualized networks based on QoS requirements and hardware conditions of MTC devices. A novel utility function integrating network performance and the power consumption in MTC devices is proposed. Furthermore, the resource allocation problem is formulated as an optimization problem to maximize the different utility functions under constraints of user QoS requirements and maximum transmitted power. To improve computational capacity as well as reduce the operational latency, a cloud-edge collaborative scheme is further designed to share the computation loads between the cloud and edge. Simulation results demonstrate the proposed service-oriented resource allocation scheme is effective and illustrate that the proposed hybrid multiple access method provides better performance than NOMA in terms of effective energy efficiency.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".