Reconfigurable Massive MIMO: Precoding Design and Channel Estimation in the Electromagnetic Domain
<|reference_start|>Reconfigurable Massive MIMO: Precoding Design and Channel Estimation in the Electromagnetic Domain: Reconfigurable massive multiple-input multiple-output (RmMIMO) technology offers increased flexibility for future communication systems by exploiting previously untapped degrees of freedom in the electromagnetic (EM) domain. The representation of the traditional spatial domain channel state information (sCSI) limits the insights into the potential of EM domain channel properties, constraining the base station's (BS) utmost capability for precoding design. This paper leverages the EM domain channel state information (eCSI) for antenna radiation patterns design at the BS. We develop an orthogonal decomposition method based on spherical harmonic functions to decompose the radiation pattern into a linear combination of orthogonal bases. By formulating the radiation pattern design as an optimization problem for the projection coefficients over these bases, we develop a manifold optimization-based method for iterative radiation pattern and digital precoder design. To address the eCSI estimation problem, we capitalize on the inherent structure of the channel. Specifically, we propose a subspace-based scheme to reduce the pilot overhead for wideband sCSI estimation. Given the estimated full-band sCSI, we further employ parameterized methods for angle of arrival estimation. Subsequently, the complete eCSI can be reconstructed after estimating the equivalent channel gain via the least squares method. Simulation results demonstrate that, in comparison to traditional mMIMO systems with fixed antenna radiation patterns, the proposed RmMIMO architecture offers significant throughput gains for multi-user transmission at a low channel estimation overhead.<|reference_end|>
@article{ying2024reconfigurable,
    title={Reconfigurable Massive MIMO: Precoding Design and Channel Estimation in
  the Electromagnetic Domain},
    author={Keke Ying, Zhen Gao, Yu Su, Tong Qin, Michail Matthaiou, Robert
  Schober},
    journal={arXiv preprint arXiv:2405.02823},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02823},
    primaryClass={cs.IT eess.SP math.IT}
}