Metamaterial-Based Structures for Mutual Coupling Reduction Technique and Performance Enhancement in 5G Smartphone MIMO Antennas: A Systematic Review

Metamaterial based approaches have emerged as promising solutions for mitigating mutual coupling in compact multiple input multiple output antenna systems operating at fifth generation sub-6 GHz frequencies for smartphone devices. This systematic literature review analyzes some peer-reviewed publications published between 2020 and 2025 sourced from the Scopus database to evaluate the effectiveness of metamaterial strategies in reducing mutual coupling between closely spaced antenna elements. A structured search methodology employing the PRISMA framework identified studies focusing exclusively on sub-6 GHz implementations in handheld platforms, excluding base-station or millimeter-wave applications. Technical design analysis examined antenna configurations employing diverse metamaterial types, including split ring resonator, complementary split ring resonator, frequency selective surface, and electromagnetic bandgap structure. Results demonstrated that metamaterial-enhanced MIMO systems consistently achieved isolation levels ranging from 12 to 65 dB, envelope correlation coefficient values below 0.1, and radiation efficiency improvements exceeding 60%. Split ring resonators dominated implementations at 38.1%, while frequency selective surfaces provided dual benefits of isolation enhancement and 1.5 to 2.4 dB gain improvement. Advanced configurations combining parasitic metamaterials with metamaterial superstrates achieved 52.94% antenna miniaturization while maintaining performance specifications. Our synthesis reveals that while SRR and CSRR provide excellent compactness suitable for dense handset chassis environments, FSS and EBG structures offer superior broad-band isolation but often face spatial integration challenges due to their larger unit cell requirements. Consequently, from the authors' perspective, exploration of reconfigurable metamaterial structures, and machine learning-assisted design optimization as critical areas for future research advancement.