Handover-Free Multi-Connectivity Mobility Management for Downlink FD-RAN: A Hierarchical DRL-Based Approach
Bibliographic record
Abstract
Seamless connectivity and on-demand service provision are considered as the fundamental capabilities in next-generation mobile networks (6G). However, current configuration of single base station (BS) connection and increasingly denser BS deployment pose great challenges for mobile user equipment (UE), due to the frequent handover and limited communication serving capacity. To this end, we investigate the handover-free multi-connectivity mobility management problem in downlink over a novel 6G architecture, namely fully-decoupled radio access network (FD-RAN). Particularly, we formulate the problem as a two-layer task involving UE-BS association and link power control, whose objective is to minimize the long-term absolute difference between UE’s serving rate and rate demand. We propose a hierarchical deep reinforcement learning (HDRL)-based scheme to decompose the original problem into two subproblems for efficient resolution. Specifically, a double deep Q-network (DDQN) algorithm is employed to update the multi-connectivity BS cooperation set for each UE at the first layer of HDRL. Then at the second layer, we design a transformer-assisted soft actor-critic (TSAC) algorithm to jointly determine transmission power for all links associated with each UE. Extensive simulations validate the effectiveness of proposed scheme over benchmarks, which is capable of providing seamless connectivity and fine-grained on-demand service for mobile UEs.
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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) | 0.001 | 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 | 0.002 | 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".