Blind Source Separation (BSS) separates mixed signals by leveraging source signals’ statistical independence and non-Gaussian characteristics, requiring no prior channel knowledge. As FastICA achieves rapid convergence, its complexity scales quadratically with signal dimensions. Although ICA-R enhances convergence via reference signals, iterative frameworks persist. We propose a low-complexity architecture: pre-analyzing mixing characteristics through correlation matrix decomposition, constructing reference-target correlation models, and deriving single-step closed-form solutions. The experiments show lower complexity compared to conventional methods, without merely performance loss.

Multiple-input Multiple-output (MIMO) technologies play an important role in modern and future wireless communication systems as they can improve the capacity without increasing the bandwidth. MIMO detection is one of the key technologies for MIMO system designs. MIMO detection schemes based on Message Passing (MP) algorithms have attracted extensive attention in recent years. The MIMO detection scheme based on Expectation Propagation (EP), which is also a kind of MP algorithms, has been proved to achieve the Bayes-optimal performance under the conditions of large system limit and compression rate threshold. However, there is improvement space for the detection performance of the EP algorithm when the conditions are not satisfied, which are the common cases in the practical applications. In this paper, when the conditions of the large system limit and compression rate threshold are not satisfied, we analyze the influences of two factors, the initial parameters selection and the moment matching strategy, on the performance of EP based detection scheme. As a result, we propose an improved EP detection scheme based on optimizing the two factors. Simulation results show that the proposed scheme outperforms the original EP detection scheme in different MIMO scenarios.

This paper proposes a low-complexity expectation propagation (EP) algorithm for massive multiple-input multiple-output (MIMO) detections. The original EP detection algorithm shows a near-optimal performance but suffers from the unaffordable computational complexity. In this paper, we use an iterative successive updating scheme to reduce the complexity caused by the exact matrix inversion in each iteration and ameliorate the efficiency and accuracy of messages updating to accelerate the convergence, which leads to a low-complexity high-performance massive MIMO detector. Numerical analysis shows the proposed algorithm can outperform 0.2 dB in bit error rate (BER) with a huge of computational complexity saved compared with the previous EP detector.