Iterative Mitigation of Intercell Interference in Cellular Networks Based on Gaussian Belief Propagation

The mitigation of 2-D intersymbol interference (ISI) has recently emerged as an important problem in the field of storage technology and wireless communications. Several solutions based on message passing have been proposed to perform equalization on 2-D channels with ISI. We present an algorithm based on Gaussian belief propagation (GaBP). We show that the proposed algorithm has interesting features compared with the corresponding message passing solutions available in the literature. First, the complexity of the proposed equalizer is independent of the size of the constellation used for modulation. Second, since the complexity is not exponential in the size of the state space, the computational burden is reduced for channels with long memory. As an application, we consider decentralized intercell interference mitigation in uplink cellular networks, where base stations communicate with their neighbors to recover their own users' signals.