Approximate signal detection for massive MIMO: Neumann series, iterative techniques, and machine learning enhancements

With the advent of 5G and next-generation communication systems, massive multiple-input multiple-output technology is critical for enhancing spectral efficiency and link reliability. However, as the number of antennas increases, the computational complexity of signal detection escalates, rendering traditional methods such as maximum likelihood detection and sphere decoding inefficient for large-scale arrays. To address these challenges, approximation techniques have emerged as effective alternatives, offering ways to reduce computational burden while maintaining satisfactory detection accuracy. This paper presents a detailed analysis of three approximation techniques for signal detection in massive MIMO systems, focusing on their theoretical foundations and hardware implementation. The first method leverages the Neumann series expansion to approximate matrix inversion, significantly reducing computational complexity but at the cost of slower convergence and reduced accuracy in high-dimensional settings. The second type, based on Gauss-Seidel and successive over-relaxation, optimizes the iterative process to mitigate the need for matrix inversion, improving efficiency while still facing challenges related to convergence speed and hardware resource consumption. The third method incorporates machine learning to enhance detection performance by adaptively optimizing key parameters, improving robustness to interference. Finally, we compares the strengths and weaknesses of these methods and provides guidelines for their application in different scenarios: the Neumann series is effective in resource-constrained environments, iterative techniques offer a balance of efficiency and performance in real-time systems, and machine learning is most suitable for complex, dynamic scenarios requiring high adaptability. The paper concludes by suggesting optimization strategies for each method to overcome the challenges posed by hardware implementation.