Integrated Cross-Layer Security for Reliable Service Provisioning with Guaranteed Robustness
Bibliographic record
Abstract
The advancement of wireless communication technology and the rise of user-centric applications have intensified demands for extensive and diverse service provisioning in future networks, including security as a service. In this context, security will evolve from an isolated function to a fundamental component of service, integrated into the comprehensive service provisioning process. However, the unpredictable physical layer security cannot guarantee robustness, and high extra overhead caused by most digital schemes and situation-irrelevant security provisioning results in a deterioration in quality of service (QoS). To address this issue, this paper investigates a security-integrated service provisioning scheme. Specifically, a security-integrated sparse code multiple access (SI-SCMA) scheme is proposed to efficiently integrate physical layer authentication into the code-book allocation process with low overhead. To guarantee the security robustness that cannot be fully achieved by the physical layer scheme, a cryptographic encryption scheme is proposed to further enhance the security strength on the user’s specific demand. To precisely characterize users’ requirements of security provisioning, a novel system utility model including efficient data rate, cross-layer security capacity, and energy consumption is designed, and a system utility maximization problem is formulated to guide the determination of resource allocation. To solve the formulated problem, a joint resource allocation and encryption design algorithm (JSEA) is developed. Numerical results validate the effectiveness of the proposed authentication mechanism and JSEA.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".