Iterative detection in MIMO channels using particle filtering

To achieve near-capacity in systems employing multiple antennas, an iterative detection scheme must be used. A full a posteriori probability (APP) MIMO detector is associated with a convolutional code or a turbo code. However, the APP inner detector presents quickly a prohibitive complexity and schemes known as list sphere decoders have been developed to reduce this complexity. In this paper, we provide a new reduced-complexity MIMO list detector based on the particle filtering (PF) methods. This detector can be seen as a tree-search detector, which uses the a priori information to explore the paths of the tree. We show that the average number of paths analyzed by the PF detector decreases at each iteration. Moreover, it is adapted according to the signal-to-noise ratio. We compare the performance and the complexity of the PF detector and the list sphere decoder.