Abstract Iterative receiver techniques, multiple-input – multiple-output (MIMO) processing, and orthogonal frequency division multiplexing (OFDM) are amongst the key physical layer technologies when aiming at higher spectral efficiency for a wireless communication system. Special focus is put on iterative detection, decoding, and channel estimation for a MIMO-OFDM system. After designing separately efficient algorithms for the detection, channel decoding, and channel estimation, the objective is to optimize them to work together through optimizing the activation schedules for soft-in soft-out (SfISfO) components. A list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal loss of performance. It is shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. The convergence of the iterative detection and decoding is improved by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. Unlike in pilot based channel estimation, the least-squares (LS) channel estimator based on symbol decisions requires a matrix inversion in MIMO-OFDM. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively, avoiding the matrix inversion with constant envelope modulation. The performance and computational complexity of the FD-SAGE channel estimator are compared to those of pilot based LS channel estimation with minimum mean square error (MMSE) post-processing exploiting the time correlation of the channel. A time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in channel estimation based on symbol decisions for MIMO-OFDM systems also with non-constant envelope modulation. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. It is proposed to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and to find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of the channel estimate’s mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the channel decoder. Trellis search algorithms are shown to find the convergence with the lowest possible complexity using the EXIT functions. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.