The field of signal processing is witnessing a shift towards more structured and complex signal models, driven by the need to better represent real-world data. Recent studies are focusing on understanding phase transitions in structured sparse signals, which are prevalent in various applications. This involves exploring the thresholds at which these signals can be accurately recovered, a topic that has been largely unaddressed for structured sparsity. Additionally, there is a growing interest in the reconstruction of graph signals from noisy dynamical samples, with advancements in both theoretical conditions and practical algorithms for sensor placement optimization. Another notable trend is the development of novel training matrix designs for spatial modulation systems, particularly those utilizing sparse zero correlation zone arrays, which offer improved performance in channel estimation under frequency-selective fading conditions. These developments collectively indicate a move towards more sophisticated signal processing techniques that account for the inherent complexity and structure of real-world signals.