Federated Power Control for Predictive QoS in 5G and Beyond: A Proof of Concept for URLLC
Bibliographic record
Abstract
The fifth-generation (5G) mobile standard has been designed to support new use cases such as ultra-reliable and low-latency communication (URLLC). The future 6G is envisioned to support extreme URLLC with higher QoS requirements (e.g., remote surgery, autonomous driving, etc.). URLLC applications need higher QoS that require more power allocations. Consequently, QoS variance will increases, which is intolerable for URLLC. An important amount of energy can be saved through a power control scheme. In this work, we are interested in energy-aware self-organizing networks that provide satisfactory performance for URLLC. We propose a predictive QoS paradigm to enhance satisfaction and reduce power consumption under URLLC’s constraints. A predictive QoS is an intelligent paradigm that allows Mobile/IoT-device to adjust power allocation to the minimum required to achieve the target QoS. First, we model power control as a satisfactory game, where IoT-devices aim to meet their target demands instead of maximizing them. Next, we introduce a distributed satisfactory learning scheme, called Robust Banach-Picard (RBP), to allow devices to self-adjust their power allocation to maintain reliability and latency within the tolerated range of the URLLC application. The algorithm implements deep learning and a derivative concept of federated learning to account for channel variability in power control. Extensive simulations exhibit the advantages and drawbacks of the proposed scheme for URLLC applications. Results show that RBP can maintain instantaneous reliability and latency within the tolerated request at the minimum energy costs. Consequently, RBP can be safe to use for URLLC use cases compared to conventional Banach-Picard iterates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".