Value of Service Maximization in Integrated Localization and Communication System Through Joint Resource Allocation
Bibliographic record
Abstract
The rapid proliferation of smart devices and Internet of Things (IoT) applications have brought significantly increased demands for concurrent sensing, localization and communication services. To achieve multiple functions concurrently, new unified wireless systems including integrated localization and communication (ILAC) and integrated sensing and communication (ISAC) are facing the fundamental challenge of integrative resource allocation among coexisting functions and services. In addressing this challenge, an ILAC system based on the efficient allocation of the common hardware and radio resource pool for localization and communication is proposed. A novel concept, termed Value of Service (VoS), is coined to maximize the unified performance of ILAC system for diverse service provisioning including localization accuracy and communication data rate. Furthermore, the bandwidth and temporal resource allocation problem is formulated for ILAC to maximize its VoS. Specifically, the problem is treated as a mixed-integer nonlinear problem solved by an iterative joint resource allocation (JRA) strategy. In each iteration, the resource allocation is decomposed into two steps. Firstly, the bandwidth resource is optimized with a Kelly mechanism-based continuous allocation method followed by discretization. Secondly, the temporal resource is assigned with the aid of an adaptive particle swarm optimization (PSO)-based approach. Simulation results demonstrate the significant superiority of our proposed VoS evaluation metric and JRA method in ILAC system under limited resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".