MIMO Capacity Analysis and Channel Estimation for Electromagnetic Information Theory
<|reference_start|>MIMO Capacity Analysis and Channel Estimation for Electromagnetic Information Theory: Electromagnetic information theory (EIT) is an interdisciplinary subject that serves to integrate deterministic electromagnetic theory with stochastic Shannon's information theory. Existing EIT analysis operates in the continuous space domain, which is not aligned with the practical algorithms working in the discrete space domain. This mismatch leads to a significant difficulty in application of EIT methodologies to practical discrete space systems, which is called as the discrete-continuous gap in this paper. To bridge this gap, we establish the discrete-continuous correspondence with a prolate spheroidal wave function (PSWF)-based ergodic capacity analysis framework. Specifically, we state and prove some discrete-continuous correspondence lemmas to establish a firm theoretical connection between discrete information-theoretic quantities to their continuous counterparts. With these lemmas, we apply the PSWF ergodic capacity bound to advanced MIMO architectures such as continuous-aperture MIMO (CAP-MIMO) and extremely large-scale MIMO (XL-MIMO). From this PSWF capacity bound, we discover the capacity saturation phenomenon both theoretically and empirically. Although the growth of MIMO performance is fundamentally limited in this EIT-based analysis framework, we reveal new opportunities in MIMO channel estimation by exploiting the EIT knowledge about the channel. Inspired by the PSWF capacity bound, we utilize continuous PSWFs to improve the pilot design of discrete MIMO channel estimators, which is called as the PSWF channel estimator (PSWF-CE). Simulation results demonstrate improved performances of the proposed PSWF-CE, compared to traditional minimum mean squared error (MMSE) and compressed sensing-based estimators.<|reference_end|>
@article{zhu2024mimo,
    title={MIMO Capacity Analysis and Channel Estimation for Electromagnetic
  Information Theory},
    author={Jieao Zhu, Vincent Y. F. Tan, Linglong Dai},
    journal={arXiv preprint arXiv:2406.04881},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04881},
    primaryClass={cs.IT eess.SP math.IT}
}