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 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 | 0.001 |
| 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".