Decentralized Radio Resource Allocation for Single-Network and Multi-Homing Services in Cooperative Heterogeneous Wireless Access Medium
Bibliographic record
Abstract
This paper studies radio resource allocation for mobile terminals (MTs) in a heterogeneous wireless access medium. Unlike the existing solutions in literature, we consider the simultaneous presence of both single-network and multi-homing services in the networking environment. In single-network services, an MT is assigned to the best wireless access network available at its location. On the other hand, in multi-homing services, an MT utilizes all available wireless access networks simultaneously. The objective of the radio resource allocation is of twofold: to determine the optimal assignment of MTs with single-network service to the available wireless access networks, and to find the corresponding optimal bandwidth allocation to the MTs with single-network and multi-homing services. We develop a sub-optimal decentralized implementation of the radio resource allocation, which relies on network cooperation to perform the allocation in a dynamic environment in an efficient manner. The MT plays an active role in the resource allocation operation, whether by selecting the best available wireless network for single-network services or by determining the required bandwidth share from each available network for multi-homing services. Simulation results are presented to demonstrate the performance of the proposed algorithm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".