The field of wireless communication is moving towards more efficient and adaptive solutions for beam alignment, error correction, and channel estimation. Researchers are exploring the use of Bayesian optimization, machine learning, and novel algorithms to improve the performance and reliability of wireless systems. One of the key areas of focus is the development of fast and reliable beam alignment techniques, which are essential for sustaining high-throughput links in intelligent indoor wireless environments. Another area of research is the design of error correction codes that can achieve high accuracy and computational efficiency, with some studies proposing the use of consistency flow models and generative channel estimation methods. The use of array-based multipath detection and joint low-rank and sparse Bayesian estimation is also being investigated to improve the accuracy of positioning and channel estimation. Noteworthy papers in this area include: The Refined Bayesian Optimization framework for beam alignment, which achieves 97.7% beam-alignment accuracy within 10 degrees and reduces probing overhead by 88%. The Error Correction Consistency Flow Model, which attains lower bit-error rates than autoregressive and diffusion-based baselines and delivers inference speeds up to 100x faster than denoising diffusion decoders.