Our paper introduces the dropout and cluster deletion (DCD)-K-Means (KM) Gibbs sampling (GS) algorithm, which estimates the uplink channel under a magnitude-only (MO) single-input single-output (SISO) system. The receiver employs a MO radio-frequency (RF) chain, using envelope detectors to capture RF signal magnitudes. In comparison to traditional SISO, MO-SISO system enjoys simplified structure, lower cost and circuit power. However, due to the absence of phase information and the Wirtinger flow (WF) algorithm lose its effectiveness when the pilots are few. The issues are resolved by the DCD-KMGS algorithm within a Bayesian framework. The GS iterations are utilized to reconstruct the channel, which is based on the MO measurements and discrete channel prior distribution. The real channel state informations (CSI) are obtained by two ESP32s under a indoor scenario and the pilots are generated based on 802.11ac. To simplify the GS iterations, the prior channel distributions are converted to discrete, with their centroids determined through K-means clustering applied to CSIs. Additionally, the algorithm employs damp for robustness and weighted-sum estimation to improve the mean squared error (MSE) performance. Our paper also employs the dropout, CD mechanisms and a closed-form solution for phase ambiguities to lower the computational complexity. Simulation experiments shows the DCD-KMGS algorithm successfully reconstructs the channel and its superiority over the WF algorithm.