
Simulation ya MATLAB/Simulink imetumia eneo la 1000 × 1000 m, base stations tano na mtumiaji mmoja anayesogea kwa kasi ya 3–60 km/h kwa random waypoint mobility. Channel imejumuisha log-distance path loss, log-normal shadowing na Rayleigh fading.
Idadi ya handover ilipungua kutoka 76.89 hadi 34.10, sawa na 55.7%. Ping-pong events zilipungua kutoka 41.16 hadi 17.56, au 57.3%. Packet loss probability ilishuka kutoka 0.2648 hadi 0.2486, huku effective throughput ikiongezeka kutoka 0.8486 hadi 0.9022.
Vipengele vya SVM
- RSS — nguvu ya signal inayopokelewa.
- SINR — ubora wa signal dhidi ya interference na noise.
- Kasi ya mtumiaji — muktadha wa mobility.
- Base-station load — matumizi ya resource, yaliyowekwa katika kiwango cha 0–1.
Mafunzo ya modeli
RBF kernel imetumika. C na kernel parameter zimetunishwa kwa grid search na k-fold cross-validation. Features zimenormalishwa; 70% ya data imetumika kwa training na 30% kwa testing.
Chanzo hakitoi final C, kernel parameter, thamani ya k au jumla ya samples.
Simulation environment
| Kigezo | Thamani |
|---|---|
| Eneo | 1000 × 1000 m |
| Base stations | 5 |
| Watumiaji hai | 1 |
| Kasi | 3–60 km/h |
| Muda | 200 s |
| Time step | 1 s |
Umuhimu unaowezekana Afrika Mashariki
Utafiti haukutumia data ya mtandao halisi wa Afrika Mashariki. Hata hivyo, maeneo yenye ukuaji wa 4G/5G, mobility ya mijini na barabara za masafa marefu yanaweza kunufaika na utafiti wa adaptive handover unaotumia channel quality, kasi na cell load kwa pamoja. Deployment halisi ingehitaji training na validation kwa radio measurements na traffic patterns za eneo husika.
Vikwazo vya utafiti
Simulation ina mtumiaji mmoja tu na base stations tano. Haipimi user-density effects, large-scale cellular interaction au computational scaling katika operator network kubwa. Baadhi ya channel parameters, SVM hyperparameters na idadi ya Monte Carlo runs pia hazijaripotiwa kwa namba.
Mbinu na Matokeo ya Utafiti
| Kipimo | Threshold | SVM | Relative change |
|---|---|---|---|
| Handover | 76.89 | 34.10 | −55.7% |
| Ping-pong | 41.16 | 17.56 | −57.3% |
| Packet loss | 0.2648 | 0.2486 | −6.1% |
| Effective throughput | 0.8486 | 0.9022 | +6.3% |
Matokeo yanaonyesha kwamba katika mazingira yaliyosimuliwa SVM imepunguza unnecessary handover na ping-pong kwa kiwango kikubwa. Waandishi wanapendekeza future work kwenye multi-user scenarios, online learning na deep reinforcement learning.
Maelezo ya Chanzo na Mbinu
Waandishi: Andicho Haryus Wirasapta; Tiara Deta Pamungkas
Jarida: Jurnal LITEK: Jurnal Listrik Telekomunikasi Elektronika
Volume/Issue: 23(1), 30–37
Publication: 1 Machi 2026
DOI: 10.30811/litek.v23i1.104
Funding / data availability / conflict of interest: hakuna taarifa tofauti katika toleo lililochunguzwa.
Hii ni MATLAB/Simulink simulation, si field trial ya operator network. Parameter values ambazo hazikuwekwa wazi katika chanzo hazijaongezwa.

Acha maoni
Anwani yako ya barua pepe haitachapishwa. Sehemu za lazima zimewekewa alama ya *