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Holographic MIMO (HMIMO)

Updated 31 December 2025
  • Holographic MIMO (HMIMO) is an advanced wireless transceiver technology characterized by nearly continuous electromagnetic apertures composed of dense, sub-wavelength metamaterial elements, enabling direct wave-domain manipulation.
  • It leverages stacked intelligent metasurfaces and dual-polarization architectures to achieve refined beamforming, enhanced spectral efficiency, and significant energy savings compared to conventional MIMO systems.
  • HMIMO utilizes Fourier plane-wave expansions and EM integral operator models to capture near-field and far-field propagation effects, spatial degrees of freedom scaling, and mutual coupling challenges that inform design for future 6G networks.

Holographic MIMO (HMIMO) is an advanced wireless transceiver technology in which extremely dense arrays of sub-wavelength radiating elements or metamaterial units are deployed over large, often quasi-continuous, electromagnetic apertures. This paradigm enables direct wave-domain manipulation of electromagnetic (EM) fields for communication, sensing, localization, and integrated signal processing, and is notable for its capacity, energy efficiency, spatial degree-of-freedom (DoF) scaling, and hardware tractability. HMIMO contrasts with conventional massive MIMO by operating with nearly continuous surfaces, exploiting both far-field and near-field physics, and embedding intelligence into the EM-domain via programmable or stacked metasurfaces.

1. Fundamental Principles and Physical Modeling

HMIMO is characterized by arrays with inter-element spacing much smaller than a wavelength, formally such that the aperture can be seen as a continuous surface from an EM perspective. The underlying physics is captured by Maxwell’s equations, and the end-to-end channel is best described by surface integral operators involving the dyadic Green’s function: E(r)=∫SG(r,s)J(s) ds,E(\mathbf{r}) = \int_{\mathcal S} G(\mathbf{r}, \mathbf{s}) J(\mathbf{s})\,d\mathbf{s}, where S\mathcal S is the continuous transmit surface, JJ is the current distribution, and GG is the (tensor) Green’s function linking points on the two surfaces (An et al., 2023, Wei et al., 2024, Gong et al., 2022). This operator formalism explicitly includes both near-field (spherical wave) and far-field (plane-wave) propagation, spatial correlation, polarization, and mutual coupling effects.

Key physical metrics and limitations include:

  • Degrees of Freedom (DoF): For a planar aperture of area AA, the spatial DoF scale as 2Aλ2\frac{2A}{\lambda^2}, where λ\lambda is the wavelength. In multi-user and near-field regimes, DoF can exceed this due to evanescent mode excitation but may degrade due to spatial blocking (Chen et al., 2024, An et al., 2023).
  • Physical limits: Ultimate directional gain, bandwidth, and efficiency of HMIMO are bounded by Chu’s, Harrington’s, and Hannan’s EM constraints. Higher DoF via denser packing trade off with higher Q-factor and increased sensitivity to loss and mutual coupling (Wei et al., 2024).

The channel can be discretized via Fourier plane-wave expansions (wavenumber-domain representation) or sampled onto sub-wavelength grids, yielding high-rank channel matrices with strong spatial structure (Bahanshal et al., 2023, Guo et al., 2024).

2. Holographic Beamforming, Stacked Metasurfaces, and Dual-Polarization Architectures

A distinctive HMIMO property is its ability to spatially shape fields not only in the digital domain (baseband) but also directly in the EM wave domain via engineered metasurfaces:

H=Q(Θrx) G P(Θtx),H = Q(\Theta_\text{rx}) \, G \, P(\Theta_\text{tx}),

where QQ and PP are wave-domain combiners/precoders synthesized by appropriately configuring SIM phase shifts per layer.

  • Optimization Techniques: Nonconvex performance metrics (e.g., mutual information, sum-rate, channel cutoff rate) are optimized over the entire chain of digital and metamaterial degrees of freedom via gradient-based, alternating projected-gradient, or model-based deep neural network methods (Chen et al., 28 Apr 2025, Perović et al., 2024). Model-derived constraints—such as amplitude masks, phase quantization, or permutation equivariance—are directly embedded.
  • Dual-Polarization SIMs (DPSIM): By employing dual-polarized meta-units, HMIMO supports independent signal processing on orthogonal polarizations, fundamentally doubling the wave-domain multiplexing potential and suppressing polarization cross-interference (PCI) and inter-stream interference (ISI) (Zhang et al., 27 May 2025, Zeng et al., 2024). Block-diagonalization of the channel via DPSIM yields both higher spectral efficiency and robustness to polarization leakage.

3. Degrees of Freedom, Near-Field Propagation, and Channel Modeling

The spatial DoF of HMIMO arrays, especially in the near field, is a central theoretical and practical concern:

  • Single- and Multi-User DoF: In the near field—i.e., for array sizes DD with r<dR=2D2/λr < d_R=2D^2/\lambda—the DoF depends on both the aperture sizes and user locations, governed by integrals over spatial wavenumber footprints. In multi-user near-field regimes, spatial blocking (overlap of users’ spatial spectra at the array) reduces the aggregate DoF, with losses up to 21.2%21.2\% reported in two-user settings (Chen et al., 2024).
  • Channel Modeling Techniques:
    • Spherical-wave models capture phase and amplitude variations across the aperture and are essential for near-field or hybrid near-/far-field operation (Gong et al., 2024, Chen et al., 2024).
    • Wavenumber-domain (Fourier) models provide a sparse, physically accurate representation of the random field, enabling high-resolution channel estimation and mitigating angular power leakage inherent to conventional DFT-based approaches (Guo et al., 2024, An et al., 2023).
    • Expectation-Maximization (EM) and subspace-based methods adaptively determine whether each multipath component is near- or far-field, yielding robust modeling for massive apertures and mixed propagation regimes (Chen et al., 2024).

4. Energy Efficiency, Hardware Constraints, and Practical Implementations

HMIMO achieves superior energy efficiency primarily through analog EM-domain preprocessing and the elimination or drastic reduction of power-hungry RF chains and digital phase shifters:

  • Reconfigurable Refractive Surfaces (RRS): RRS transmitters, functioning as active HMIMO apertures, operate with per-element ultra-low-power biasing (e.g., 0.5 mW per element), enabling order-of-magnitude reductions (10210^2–103×10^3\times) in total power consumption compared to phased arrays of equal aperture size (Zeng et al., 2022).
  • Per-RF Chain Constraints and Binary Tuning: Practical metamaterial elements often support only discrete tuning states (binary or Lorentzian phase profiles). Optimization algorithms based on hidden-convexity, sphere decoding, and majorization-minimization efficiently solve the resulting combinatorial problems even at large element counts (Zhi et al., 23 Feb 2025).
  • Scalability and System-Level Insights: System design balances element count, aperture area, feed architecture, SNR regime, and user arrangement. Oversampling beyond the spatial Nyquist rate is beneficial in noise-limited regimes; under interference-limited operation, it suffices to deploy elements matched to the channel DoF (Bahanshal et al., 2023). For cell-free or distributed networks, stacking multiple SIM layers linearly increases achievable rate, provided hardware impairments are properly compensated (Li et al., 2024).

5. Information-Theoretic Foundations, Capacity, and Performance Analysis

HMIMO’s capacity scaling and ultimate information-theoretic potential are grounded in both probabilistic (Shannon) and functional-analytic (Kolmogorov) information theory, unified under Electromagnetic Information Theory (EIT) (Wei et al., 2024):

  • Capacity expressions: For an M×MM\times M sampled HMIMO channel, the capacity is

C=log⁡2det⁡(IM+PN0HHH),C = \log_2\det\left(I_M + \frac{P}{N_0} H H^H \right),

but the effective number of non-negligible singular values (the DoF) can vastly exceed that of conventional MIMO for the same aperture area.

  • Spatial Oversampling and FTN: Closer-than-Nyquist spatial sampling (e.g., element spacing <λ/2<\lambda/2) can realize more modes per unit area than allowed by traditional antenna theory, so long as advanced detection and mutual-coupling-aware design are used (Wei et al., 2024, Gong et al., 2022).
  • Stacked SIM/Metasurface Capacity: Multilayer SIM-based HMIMO architectures, when properly optimized, attain quadratic scaling in the number of meta-atoms per layer and per site, surpassing both massive MIMO and RIS-aided architectures at equal radiated power (An et al., 2023, Chen et al., 28 Apr 2025). Digital and analog beamforming hybridization further closes the gap to theoretical limits.
  • Integrated Sensing and Communication (ISAC): HMIMO supports joint radar-communication via angularly and range-selective beams. The Pareto boundary for sensing/communication rate trade-offs is fully characterized, with distinct optimal strategies for downlink and uplink SIC ordering (Zhao et al., 2024).

6. Challenges, Open Problems, and Future Directions

Despite rapid research progress, HMIMO faces a series of open challenges:

  • Mutual Coupling and Super-Directivity: Dense meta-element packing leads to nontrivial mutual coupling, requiring circuit- and EM-theoretic models for loss, Q-factor, and super-directivity/robustness trade-offs (Gong et al., 2022, Wei et al., 2024).
  • Channel Estimation and Training Overhead: The sheer number of spatial DoF/variables in large apertures renders classical pilot-based estimation infeasible. Model-driven, wavenumber/domain-sparse, and unsupervised learning approaches are under active development (Guo et al., 2024, Chen et al., 28 Apr 2025).
  • Hybrid Digital-Analog and Wave-Domain Computation: Bridging digital and EM/wave-domain processing, and allocating complexity between layers, feeds, and control circuits, remains a systems-level design question (An et al., 2023, Chen et al., 28 Apr 2025).
  • Integration with 6G/NTNs: In non-terrestrial networks (NTN), lightweight, deployable HMIMO surfaces offer drastic cost and energy savings for satellites, but broad challenges in materials, radiation hardening, distributed AI/optimization, and satellite coordination must be addressed (Iacovelli et al., 2024).
  • Dual-Polarization, Wideband, and Sensing Extensions: Further work is ongoing in extending HMIMO to support wideband/multi-band operation, robust near/far-field dual-polarized links, and joint communication-and-sensing protocols (Zhang et al., 27 May 2025, Zeng et al., 2024, Zhao et al., 2024).

7. Comparative Performance and Design Guidelines

Quantitative studies demonstrate HMIMO’s advantages and offer practical guidelines:

Architecture Peak SE gain vs. Massive MIMO EE gain Key Limitation
Single-layer HMIMO/SIM Up to 1.5x 10–100x Residual ISI, lower DoF
Multilayer SIM (stacked) Up to 2x or higher 10–100x Control complexity, volume
Dual-polarized SIM/DPRIS 2x multiplexing (at SNR→∞) Maintained XPD/imperfection sensitivity
Reconfigurable refractive Matching SE, 10210^2–10310^3x EE Energy efficiency Amplitude/element control

Spectral/energy efficiency scales favorably under multilayered or dual-polarized configurations, peaking at moderate element counts and saturating when the number of elements greatly exceeds the physical DoF. System design must balance layer count, aperture area, polarization purity, hardware impairments, and power allocation (e.g., via water-filling vs. equal allocation), tuning these per deployment requirements (Zeng et al., 2022, An et al., 2023, Zhang et al., 27 May 2025, Wei et al., 2024).

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