Optimization and Evaluation of Channel Estimation Methods in Massive MIMO Systems for 5G/6G Communications

Abstract

Abstract The evolution of wireless communication systems towards 5G and future 6G networks has been increasingly demanding in terms of higher data rates, ultra-low latency, enhanced reliability, and massive connectivity. Massive MIMO and its extension XL-MIMO have been developed as a key enabling technology to meet these require- ments by improving spectral efficiency, coverage and system capacity. However, the performance of such systems is highly dependent on the availability of accurate Chan- nel State Information (CSI). As the number of antennas increases and the wireless propagation environments become more complex, conventional channel estimation methods are confronted with the issues of high computational complexity, large pilot overhead and limited estimation accuracy. The work reviews and studies channel estimation techniques for Massive MIMO and XL-MIMO systems, with an emphasis on sparse channel estimation and optimization-based approaches. In particular, clas- sical methods such as LS and LMMSE, as well as compressed sensing techniques such as OMP, CoSaMP, AMP, GAMP and SAMP, are reviewed. Moreover, the advanced estimation frameworks based on SOMP and ROMP are simulated in far-field, near- field and hybrid-field propagation environments. The obtained results show that the exploitation of channel sparsity can significantly improve the estimation accuracy and reduce the pilot overhead and computational burden. The results highlight the importance of optimised channel estimation strategies for reliable and efficient com- munications in future 5G and 6G wireless networks