Signal Detection Using Compressive Sensing in Massive MIMO Systems with multidimensional Modulation

Large-scale multiple-input-multiple-output (MIMO) has been recognized as a major technology for wireless communication systems. In massive MIMO systems, detection is a main issue because there are a large number of antennas at the base station in comparison to traditional MIMO systems. While multidimensional modulation is effective in minimizing interference and simplifying detection, it has not been utilized for detection in massive MIMO systems. This work introduces a novel detector for large-scale MIMO systems using multidimensional modulation. We model the system with sparse signal sources and propose an approximate Maximum Likelihood (ML) relaxed detector that minimizes the Euclidean distance to the received signal while maintaining a constant $\ell 1$-norml. The proposed method offers a balance between accuracy and computational efficiency, making it well-suited for systems with sparse structures. Simulations show that the proposed detector achieves optimal performance and outperforms the other method.