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Holographic MIMO Communications

Prof. Luca Sanguinetti

Dipartimento Ingegneria dell’InformazioneUniversity of Pisa

WiLab-Huawei JIC Workshop

MIMO Technologies

Where are we now?

Massive MIMO (multiple-input multiple-output): what is it?

Large Intelligent Surfaces Prof. Luca Sanguinetti

TDD cellular network (at sub-6 GHz or higher bands)

• Fully digital linear processing

• Many antennas per base station M ≥ 64

• Spatial multiplexing of many users K ≥ 8

• Antenna-user ratio: M/K > 1

… and several Massive MIMO products

Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” TWC10.

In less than 10 years: from science fiction to

tons of papers, 3 books …

Prof. Luca Sanguinetti - Large Intelligent SurfacesProf. Luca Sanguinetti – Holographic MIMO Communications WiLab-Huawei JIC Workshop – January 11, 2021


Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE TWC10.

Björnson, Hoydis, Sanguinetti, “Massive MIMO Has Unlimited Capacity,” IEEE TWC18.

No upper limit,MMSE, spatially correlated fading, full

or partial knowledge of statistics(BHS, TWC18)

Finite upper boundMR, i.i.d. fading

(Marzetta, TWC10)

Observation 1 Spatial channel models and advanced

signal processing are crucial

5G BSs: 32-64 antennas but only 8-16 per row.People leave mostly on the ground!

Fundamental limits of Massive MIMO


Observation 2 ‘Truly’ large antenna arrays are needed to

fully exploit the technology

Pilot contamination limit

How can we practically approach the Massive MIMO limit?


Hu, Rusek, and Edfors, “Beyond massive MIMO: The potential of data transmission with large intelligent surfaces,” IEEE TSP, 2018.

Holographic MIMO refers to an array (possibly large) with a massive number of antennas in a compact space.In ultimate form, spatially-continuous electromagnetic apertures that actively generate/process radio signals.

Alternative names

• Large Intelligent Surfaces• Extremely large arrays• Truly Massive MIMO• …

Holographic MIMO Communications


D. Dardari, “Communicating with Large Intelligent Surfaces: Fundamental Limits and Models,” IEEE JSAC, 2020.

Pizzo, Marzetta, Sanguinetti, “Holographic MIMO Communications under Spatially Stationary Channels,” IEEE ASILOMAR, 2020.

Way to implement

• Metasurfaces (EM field manipulation)

• …

7Prof. Luca Sanguinetti - Large Intelligent SurfacesProf. Luca Sanguinetti – Massive MIMO and Intelligent Reconfigurable Surfaces Huawei Workshop – November 23, 2020

Key potential benefits and challenges

Key potential benefits

• Higher flexibility, higher spectral efficiency, higher spatial resolution

• Paving the way for processing at EM level (at the speed of light)

• Reducing significantly the latency and computational complexity (power consumption)

SP and COM challenges

• Revise established models and algorithms

• Start from EM principles to ensure that technology builds on solid foundation

Lessons learned from Massive MIMO – the channel is everything!

• Accurate channel modelling is crucial, and…

• …scalable channel acquisition protocols are fundamental!

Communications under LoS (deterministic) Channels

When the surface has size comparable to distances:

geometric near-field

1. Different distances to antennas2. Different effective areas3. Different polarization losses Distance to

element n

Element n,area A

Near-field and far-field communications

D. Dardari, “Communicating with Large Intelligent Surfaces: Fundamental Limits and Models,” IEEE JSAC, 2020.


E. Björnson, L. Sanguinetti, “Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces,” 2020

Channel gain (right in front, distance d, N elements):

α! =Nβ!

3(Nβ!π + 1) 2Nβ!π + 1+23π

tan"#Nβ!π

2Nβ!π + 1where β! =

$%&!!

Asymptotic limit (N → ∞)α! → 1/3

Geometric far-field (d ≫ L = NA)α! ≈ Nβ!

Channel and interference gains: A = (λ/4)2, λ = 0.1m, d1 = 2.5 m


• L ≥ 1 m is enough to notice approximation gap• An upper limit of 1/3 is approached for L ≥ 10 m

• Two UEs with angles θ1 = 2◦ and θ2 = −2◦, andsame distance d1 = d2 = 2.5 m

• Different behaviours for L ≥ 1m

Modeling the near-field is very important, when the distance is comparable to the surface size!

10-1 100 101 102-45

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-35

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0

10

20

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Upper limit 1/3

Practical sizes

Linear signal processing: let’s get the best!


• SE saturates with both schemes as L increases.• MMSE quickly converges to interference-free case• Gap with MR is substantial with exact model, while

it asymptotically vanishes with far-field model

• SE gap between MMSE and MR is relatively largefor the values of z < 10 m

• MMSE converges to MR only when z ≥ 40 m.

MMSE outperforms MR – Advanced signal processing is still needed (despite higher dimensions)!

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MR

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MMSE

L = 6 md1 = d2 = 2.5 m

Communications under Stochastic Channels


The END of i.i.d. Rayleigh fading model

In multi-antenna technologies, i.i.d. Rayleigh fading is common practice:

1) every antenna observes an independent realization;

2) each antenna is statistically equally good.

When i.i.d. fading can occur:

• ULA with λ/2-separation between antennas observes i.i.d. fading in isotropic scattering

• Apart from ULA, there exists no other case where i.i.d. fading can occur.

Pizzo, Marzetta, Sanguinetti, “Holographic MIMO Communications under Spatially Stationary Channels,” IEEE ASILOMAR, 2020.Bjornson, Sanguinetti, “Rayleigh Fading Modeling and Channel Hardening in Reconfigurable Intelligent Surfaces,,” IEEE WCL, 2020.


The NEED for physically-accurate and tractable channel models

Clarke, “A statistical theory of mobile-radio reception,”The Bell SystemTechnical Journal, July 1968

Existing parametric physical models are inspired by array signal processing

• Vast majority is driven by ‘analytical’ arguments (leaving out significant physical phenomena)

• Capture the propagation environment and array geometry jointly.

Clarke’s model [Clarke, 68]

• NLoS scenario with scalar radio waves in isotropic (i.e., no dominant spatial directivity) scattering

• Autocorrelation function in 3D space is sinc(2r/λ) (spatial correlation even with no spatial directivity).

Lack of channel models for non-isotropic random scattering (most of practical scenarios)

• physically meaningful; mathematically tractable; and numerically reproducible.


Communication under spatially-stationary channels


k̂

κ̂

j(s)

x̂ẑ

ŷ

scatterervolume

sourcevolume

receivevolume

Vs

H− H+

L0

e(r)ei(r′)

Vr

currentdensity

incidentfield

transmittedfield

incidentdirection

transmitteddirection

Ls,x

Ls,z

Ls,y

Lr,x

Lr,z

Lr,y

materialobjects

Pizzo, Marzetta, Sanguinetti, “Communication with Waves under Spatially-Stationary Channels,” IEEE IT, 2020.

Major contributions

1. A 4D Fourier Fourier plane-wave series expansion (in Cartesian coordinates)2. Asymptotically accurate (already for normalized sizes larger than a few wavelengths) 3. Physics meaningfulness, tractability and reproducibility: fulfilled!

Assumptions and steps taken

• Inspired by EM principles (scalar waves)• Spatially-stationary scattering• Fourier plane-wave spectral representation


Holographic MIMO channel

• Entries of Ha independent, circularly-symmetric, complex-Gaussian random variables

• Ur and Us are DFT matrices - unitary-independent-unitary (UIU) model [Tulino, IT2005]

Physical interpretation

• Wavenumber (i.e., angular) domain representation over fixed ns x nr angular sets with

• Entries of Ha indicate the fraction of power transferred within angular sets

When sampled in space at arrays with Ns and Nr of sub-wavelength spacing antenna elements:


Capacity and DoF evaluation


Capacity expression

Degrees of freedomRows and columns of Ha are linearly independent

except for those that are identically zero

• nDoF equals the upper bound when non-zero power is received from all angular sets

• In this case, the DoF per m2 are given by π/λ2

Is Holographic MIMO (already) a good option for 6G?

19Prof. Luca Sanguinetti - Large Intelligent Surfaces

Faster science is not better science!...still 10y aheadSuccess technologies in 5G

• Massive MIMO: 10x increase in spectral efficiency; • mmWave communications: 10x more bandwidth.

Any 6G technology should provide a 10x improvement (not just marginal gains)!

No good answer yet for Holographic MIMO…because of good competitors:

• Cell-free Massive MIMO (radio stripes from Ericsson)!• Fully digital Massive MIMO BSs are scaling up fast (at MmWave as well)

Less successful technologies

• NOMA• Spatial modulation• …

Prof. Luca Sanguinetti – Holographic MIMO Communications WiLab-Huawei JIC Workshop – January 11, 2021

Pushing further the limits of MIMO technologies is hard and requires a lot of thinking! • Tight synergy with EM theory may reveal new results!

High-frequency bands (THz) may boost Holographic MIMO: • Fully digital systems are truly challenging: metasurfaces may solve many issues!• SP and COM at EM level may reduce cost, consumed power, and latency

20

Thank you!

[email protected]

Homepage: https://people.unipi.it/luca_sanguinetti/


mailto:[email protected]://people.unipi.it/luca_sanguinetti/

Impact of polarization mismatch


Polarization mismatch has a double impact

1. Channel gain converges to 1/3 instead of 1/22. Interference between UEs changes due to the

varying power footprint

Two effects have impact on both MMSE and MR

• For L = 100, the SE reduction is 6.5% with MMSE,whereas it is 15% with MR.

• If between L = 1 and L = 10 meters, the polarizationeffects loss is limited to 5%.

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MR

About me

General research interests

• Wireless communications, Signal processing for wireless communications

• Multiple antenna technologies (MIMO, MU-MIMO, Massive MIMO, Cell-free…)

• Energy-Efficiency Maximization, Resource Allocation, Optimization


Collaborators

• Emil Björnson (KTH, SE)

• Jakob Hoydis, (Nokia-Bell Labs, FR)

• Merouane Debbah (Huawei, FR)

• Thomas L. Marzetta, (NYU, US)

• …(2020)

(2017)

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