Efficient Data Detection Techniques for Massive MIMO Systems Using Improved Newton Iteration
Mahmoud A. M. Albreem, Layan Alami, Hassan Idries, Sam Ansari, Saeed Abdallah, Shahriar Shahabuddin, Abir Jaafar Hussain · 2025
Massive multiple-input multiple-output (M-MIMO) technology is a critical driver of fifth-generation (5G) and beyond-5G (B5G) communication systems, significantly enhancing capacity, efficiency, and overall network performance. However, the performance of M-MIMO systems is heavily influenced by the detection techniques employed, which also determine the complexity of the system. To address the trade-off between performance and computational efficiency, this study explores advanced signal detection methods within the M-MIMO framework. This paper introduces two novel hybrid detectors, combining the Gauss-Seidel (GS) and Jacobi (JA) methods with an enhanced Newton iteration (NI) approach, designed to minimize iteration time while maintaining high detection accuracy. By leveraging the upgraded NI approach, these detectors achieve efficient data decoding while significantly reducing computational complexity. Extensive analysis and simulation results demonstrate the superiority of the proposed architectures in terms of computational complexity and bit error rate (BER). In particular, the proposed approach achieves a significant reduction in complexity, lowering it from O(NK2+ NK) to O(NK) for Detector 1, and from O(NK2+ NK) to O(NK + K) for Detector 2, effectively demonstrating substantial improvements in both efficiency and performance.