On Hybrid Detection of Wireless Communications Over Interference Channels: A Generalized Framework

Modern wireless systems face interference due to rising spectrum efficiency demands and increasingly aggressive network designs. Despite its optimality, the huge complexity of the maximum likelihood (ML) detection hinders its deployment in the future wireless communication systems, which require low latency and high energy efficiency. In this paper, we develop a novel generalized framework for data detection in interference channels. In particular, we factorize the joint likelihood function of the transmitted symbols to obtain the marginal distribution of a single symbol following the sum-product (SP) algorithm. Motivated by the fact that the complexity of the SP algorithm is dominated by the summation process, we introduce Gaussian and Gaussian mixture models to reduce the state space of symbols, which helps to reduce the detection complexity. The proposed hybrid detection framework consists of three kinds of symbol distributions, i.e., original discrete, Gaussian, and Gaussian mixture distributions. To strike a balance between complexity and error performance, we can simply modify the components of different symbol distributions, offering high flexibility in practical applications. Furthermore, we analyze the performance of our proposed detection scheme and discuss the design guidelines for the mixture Gaussian messages. Simulation results demonstrated the effectiveness of the proposed algorithm.