Near-field Communications with XL-MIMO: Cost-Effective Designs

ongoing

Extremely large-scale multiple-input multiple-output (XL-MIMO) has been recognized as a crucial enabling technology for the sixth generation (6G) networks to realize extremely high throughput and high-resolution localization. XL-MIMO will bring new features of near-field communication which requires new algorithms and transmission scheme design. Meanwhile, given the extra-large number of antennas on XL-MIMO, the cost (operation overhead, hardware cost, power consumption, algorithm complexity) could be unacceptable in practice. Thus, it is key to realise the promising gains of XL-MIMO at an affordable cost, which however has not been comprehensively studied. This proposal aims to propose a cost-effective framework for implementing XL-MIMO in near fields and gain an in-depth understanding of the working mechanisms behind it. The first objective is to implement cost-effective XL-MIMO in the current commercialized sub-6 GHz band (for macro-cell and uniform coverage), by exploiting statistical channel state information (CSI) for beamforming design and exploiting sparse array architectures to reduce the number of antennas while maintaining the array size. The second objective is to propose cost-effective design for XL-MIMO in the mmWave band with rich spectrum resources (for micro-cell hotspots and indoor communication) by using two-timescale design for hybrid precoding and employing energy-saving metamaterial antennas. The third objective is to solve the blockage issue for XL-MIMO and realise ubiquitous coverage in a cost-effective way, by leveraging the nearly passive reconfigurable intelligent surface. Besides, this proposal plans to conduct prototype measurements to validate the effectiveness of the proposed algorithms and architectures. This project is expected to significantly enhance the fellow’s career prospects, enabling him to develop new research skills, establish professional networks, learn leadership skills, and grow into an influential expert in academia.