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In this paper, we propose a high-performance low-complexity data detector for frequency-selective multi-input multi-output (MIMO) channels. This detector applies the principles of factor graph and Gaussian approximation in modeling interference. Compared to the available algorithms based on Gaussian approximation, the proposed detector goes one step further by applying an independence approximation, which reduces the complexity significantly while incurring only a marginal performance loss. The amount of operations needed is polynomial in the number of receive antennas, the number of transmit antennas, and the channel memory length.

Superposition mapping (SM) is a modulation technique which loads bit tuples onto data symbols simply via linear superposition. Since the resulting data symbols are often Gaussian-like, SM has a good theoretical potential to approach the capacity of Gaussian channels. On the other hand, the symbol constellation is typically nonbijective and its characteristic is very different from that of conventional mapping schemes like QAM or PSK. As a result, its behavior is also quite different from conventional mapping schemes, particularly when applied in the framework of bit-interleaved coded modulation. In this paper, a comprehensive analysis is provided for SM, with particular focus on aspects related to iterative processing.

Multilayer a posteriori probability (APP) detection for interleavedivision multiplexing (IDM) is shown to significantly outperform conventional multilayer detection based on Gaussian approximation. Using multilayer APP detection for IDM with equal power allocation, one can achieve very high bandwidth efficiencies on the AWGN channel.