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M. Ferrari

6 papers hereh-index 13470 citations52 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author4
  • last author1

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.IT6
same name
  • M. Ferrari — 34 papers, h 18
  • M. Ferrari — 4 papers, h 42
  • M. Ferrari — 4 papers, h 45
  • M. Ferrari — 4 papers, h 6
  • M. Ferrari — 2 papers, h 11
  • M. Ferrari — 2 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142022
collaborators
Showing 2021Show all

4 papers · 1 filter

cs.IT2021

Asymptotic MIMO Channel Model for Diffusive MC with Fully-absorbing Receivers

Fardad Vakilipoor, Marco Ferrari, Maurizio Magarini

This letter introduces an analytical model that gives the asymptotic cumulative number of molecules absorbed by spherical receivers in a diffusive multiple-input multiple-output (M…

cs.IT2021

A Sphere Packing Bound for Vector Gaussian Fading Channels under Peak Amplitude Constraints

Antonino Favano, Marco Ferrari, Maurizio Magarini +1

An upper bound on the capacity of multiple-input multiple-output (MIMO) Gaussian fading channels is derived under peak amplitude constraints. The upper bound is obtained borrowing…

cs.IT2021

Channel Characterization of Diffusion-based Molecular Communication with Multiple Fully-absorbing Receivers

Marco Ferrari, Fardad Vakilipoor, Eric Regonesi +2

In this paper an analytical model is introduced to describe the impulse response of the diffusive channel between a pointwise transmitter and a given fully-absorbing (FA) receiver…

cs.IT2021

The Capacity of the Amplitude-Constrained Vector Gaussian Channel

Antonino Favano, Marco Ferrari, Maurizio Magarini +1

The capacity of multiple-input multiple-output additive white Gaussian noise channels is investigated under peak amplitude constraints on the norm of the input vector. New insights…

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