MIMO systems can simultaneously transmit multiple data streams within the same frequency band, thus exploiting the spatial dimension to enhance performance. MIMO detection poses considerable challenges due to the interference and noise introduced by the concurrent transmission of multiple streams. Efficient Uplink (UL) MIMO detection algorithms are crucial for decoding these signals accurately and ensuring robust communication. In this paper a MIMO detection algorithm is proposed which improves over the Expectation Propagation (EP) algorithm. The proposed algorithm is based on a Gaussian Mixture Model (GMM) approximation for Belief Propagation (BP) and EP messages. The GMM messages better approximate the data prior when EP fails to do so and thus improve detection. This algorithm outperforms state of the art detection algorithms while maintaining low computational complexity.

翻译：MIMO系统可在同一频段内同时传输多个数据流，从而利用空间维度提升性能。由于多流并发传输引入的干扰与噪声，MIMO检测面临显著挑战。高效的上行链路MIMO检测算法对于准确解码信号、保障通信鲁棒性至关重要。本文提出一种改进期望传播算法的MIMO检测算法，其核心在于采用高斯混合模型对置信传播与期望传播消息进行近似建模。当期望传播无法有效逼近数据先验分布时，高斯混合消息能提供更优的近似，从而提升检测性能。该算法在保持较低计算复杂度的同时，性能优于现有先进检测算法。