Handover Reduction in 5G High-Speed Network Using ML-Assisted User-Centric Channel Allocation
Bibliographic record
Abstract
In this paper, we propose a novel user-centric channel allocation scheme for high-speed terrestrial users of the Fifth Generation (5G) network in the millimetre-Wave (mm-Wave) band small-cells named Vehicular Frequency Reuse (VFR) scheme. To adapt the VFR scheme with the 5G network, we develop a new mobility management function. This new function improves the 5G’s performance for high-speed road users such as Connected Autonomous Vehicles (CAV) in small-cells by reducing the number of handovers (HOs) in the Vehicle-to-Network (V2N) service. The VFR scheme significantly reduces the HO rate for users, control plane signalling in air interface, and improves link reliability and channel reuse ratio. A metric called Distance-Threshold is defined to determine the frequency reuse ratio for the 5G network with the VFR scheme. We also propose a new cell reselection procedure for high-speed users in RRC_Connected (Radio Resource Control) state that are using the VFR scheme and managed by our mobility management function. The proposed cell reselection procedure is defined for inter-gNB-DU (gNodeB-Distributed Unit) and intra-gNB-DU mobility. This procedure reduces traffic load on the UE’s air interface, lowers processing and signalling load for network nodes, and assists for seamless mobility management for high-speed users. These all help and facilitate the path towards the targeted zero millisecond mobility interruption time (MIT) for 5G-NR (NewRadio) users. Moreover, the proposed scheme, function, and procedure are compatible with the existing 5G structure and user equipment and can be easily added to the network by only software patches. The proposed mobility management function separates low-speed and high-speed users to serve them accordingly with different sets of channels. To separate high-speed and low-speed users, we propose a simple scalar metric defined as a Velocity-Threshold (VT). The VT value is adaptively calculated by a Machine Learning (ML) algorithm, namely K-Means, according to the road condition inferred from reported velocities. Finally, we evaluate the proposed VFR scheme and compare it with the traditional cell-centric channel allocation scheme. Computer simulations show that the proposed VFR scheme can reduce the number of HOs (HO rate) for users by over 99% compared with the traditional scheme.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".