With social characteristics integrated into cyber-physical systems (CPS), the wireless channel has been a complex electromagnetic environment due to the subjectivity of human behaviour. For the low-power and resource-constrained nodes in cyber-physical-social systems (CPSS), minimum research is available focusing on conquering the issues of computational complexity, external interference and transmission fading simultaneously. This study aims to explore channel estimation with interference suppression based on machine learning. A novel channel estimation scheme is proposed, which combined interference suppression in channel impulse response (CIR) of frequency domain with K-means algorithm and noise cancellation in CIR of time domain with K-nearest neighbor (KNN) algorithm into an integrated process. Complexity analysis and simulation results showed that the proposed scheme has relatively lower complexity and the performance is proven better than traditional schemes, which meets the requirements of CPSS in complex electromagnetic environments.
Keywords: K-means; KNN; channel estimation; interference suppression; machine learning; noise cancellation.