Channel Estimation

Channel estimation reconstructs the wireless channel between transmitters and receivers, which is needed for tasks such as beamforming, equalization, and data detection. Traditional methods such as least squares and linear minimum mean-square error use pilot signals and channel statistics, while sparse recovery exploits structure in settings such as massive MIMO and millimeter-wave links. Recent approaches use CNNs, recurrent networks, Transformers, and diffusion models, often combined with physical channel models, temporal context, or environmental geometry to work with fewer pilots and changing conditions. A central challenge is maintaining accuracy across unseen channels and hardware impairments while meeting the latency, energy, and pilot-overhead limits of real systems.