A Hybrid Multi-Criteria and Factorial Analysis Framework for CIRFLINK CubeSats Communication Design

Warinthorn Kiadtikornthaweyot Evans, Ratchanon Chuphet

Abstract


This research presents the design and optimization of a low-cost Inter-Satellite Link for the CIRFLINK project, a 1.5U CubeSat mission aimed at advancing smart farming and disaster monitoring in rural area. To address complex design trade-offs, this study introduces a novel integrated framework combining the Analytic Hierarchy Process for qualitative prioritization and 2k factorial design for quantitative validation. The primary objective was to evaluate and optimize LoRa-based communication parameters for constrained satellite environments. Analytic Hierarchy Process results prioritized LoRa technology over optical and traditional RF subtypes due to its superior power efficiency and simplicity. Subsequently, 2k factorial experiments and ANOVA revealed that distance and physical obstruction are the dominant factors affecting performance, while parameters like Spreading Factor and Bandwidth showed less immediate impact in the tested ranges. Experimental results using ESP32 and SX1278 modules demonstrated that the Signal-to-Noise Ratio and Received Signal Strength Indicator maintain reliable connectivity, with correlation analysis showing a strong negative relationship approximate -0.913b etween distance and signal quality. Field data confirmed that the system achieves stable communication with an average SNR in line-of-sight conditions. The novelty of this work lies in the systematic fusion of multi-criteria decision-making with statistical experimental design, providing a transparent engineering roadmap for small-satellite communication. This contribution offers a validated, cost-effective ISL solution that meets mission requirements with minimal complexity, serving as a scalable model for future educational and IoT-based CubeSat constellations.


Keywords


CubeSats Constellation; Analytic Hierarchy Process; Inter-Satellite Link; LoRa; APSCO Competition

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DOI: https://doi.org/10.47738/jads.v7i2.1101

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Journal of Applied Data Sciences

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