Analysis of Self-Blockage Impact on Handover Probability for User with Mobility in 5G Mm-Wave Cellular Network
Bibliographic record
Abstract
In this paper, we present the impact of self-blockage on Handover (HO) Probability for a user with mobility in a 5G mm-wave cellular network. Self-blockage is defined as a blockage that could occur for a Mobile User (MU) resulting from its own body during its movement. As a result, frequent HOs may occur, which could cause channel fluctuations between different states, namely, Line-of-Sight (LOS) state, Non-Line-of-Sight (NLOS) state and outage state. Since the human body causes a severe attenuation to the mm-wave signal, we assume that the MU’s body blocks any signal from any mm-wave BS that lies in the blocking zone. Consequently, all mm-wave BSs that lie in the blocking zone is considered blocked. In this paper, we present a mathematical analysis for the self-blockage impact on MU where the Self-blockage zone is modelled as a sector of a disc, centred at the origin and has a radius of R. The sector is making an angle of, $\theta$, towards the MU’s body. The results show that self-blockage has an impact on HO probability, especially when the MU is moving in a radial direction, as the MU direction becomes closer to the tagged mm-wave BS as the HO probability gets closer to the case where no self-blockage incorporated into the model. Since the mm-wave signal is very susceptible to blockages interruption, this paper’s work provides us insights on understanding the impact of self-blockage on the HO probability. Therefore, the effect of self-blockage on the HO probability needs to be considered carefully in designing efficient HO management schemes and planning the 5G mm-wave cellular system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".