Plasma State-Aware Adaptive Filtration (PSA-AF): A Hybrid stochastic modeling for multi-band satellite communication systems. (Comprehensive Journal of Science, 2026)
the propagation of radio signals through the solar plasma environment introduces random fluctuations that can severely impair the e-performance of satellite and deep-space communication links. Plasma turbulence generates time varying scintillation and waveform distortion, which in turn reduce signal quality and increase error rates, particularly during periods of intense solar activity. In response to these challenges, this study is proposed technique employs a Continuous-Time Markov Chain (CTMC) to model the probabilistic evolution of solar plasma conditions, categorized into Calm, Moderate, and Stormy states. This stochastic representation is combined with a frequency-dependent scattering model to continuously estimate the noise characteristics of the propagation channel. Based on this estimate, the plasma state-aware adaptive filter algorithm (PSA-AF) that dynamically mitigates plasma noise and updates its parameters to improve the received signal quality. Performance evaluations conducted over the L, C, X, and Ka bands confirm L-band is the highest scattering (~3×10¹³ m²) with high variability and Ka-band is Lowest scattering (~1×10¹⁵ m²) with relative stability this matched with theoretical relationship: σ ∝ 1/f² for coefficient of determination R2=0.999. Relative to conventional fixed-gain receivers, the PSA-AF algorithm an average SNR gain of 4.95 dB for a target BER of ×110-6, and 52.4% reduction in Bit Error Rate (BER) under stormy plasma conditions.
Keywords: Solar plasma effects, satellite links, adaptive signal processing, stochastic channel modeling, Markov processes, signal-to-noise ratio (SNR), bit error rate (BER