Local Predictability Analysis-Based Significant Multipath Component Identification for OFDM Systems

In this letter, we mainly focus on the significant multipath component identification issue for orthogonal frequency division multiplexing (OFDM) wireless communication systems in a fast time-varying scenario with a low signal-to-noise ratio (SNR). Considering the time-domain channel prediction demand of OFDM systems, we introduce the joint singular spectrum analysis (JSSA) to identify the significant multipath components of the channel impulse response (CIR) by investigating the local predictability of the multipath component in detail. The JSSA has two parts, i.e., the random SSA (R-SSA) and the predictable signal reconstruction (PSR). The former is used for multipath component decomposition, while the latter is used to reconstruct the multipath component based on the local predictabilities of those subcomponents. Therefore, the JSSA can reduce the negative effect of noise and solve the significant multipath component identification issue. The simulations indicate that the JSSA in our letter has good identification performance in the given communication scenario.