Channel Estimation Using Gaussian Approximation in a Factor Graph for QAM Modulation

Joint channel estimation and decoding using belief propagation on factor graphs requires the quantization of probability densities since continuous parameters are involved. We propose to replace these densities by standard messages where the channel estimate is accurately modeled as a Gaussian mixture. Upward messages include symbol extrinsic information and downward messages carry a mean and a variance for the Gaussian modeled channel estimate. Such unquantized message propagation leads to a complexity reduction and a performance improvement. For QAM modulated symbols, the proposed belief propagation almost achieves the performance of expectation-maximization under good initialization and surpasses it under bad initialization.