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For the faithful reconstruction of the signal from few measurements or samples, the sensing matrix must satisfy the restricted isometry property (RIP).
In CS, the measurement matrix must satisfy certain conditions.
The compression matrix must satisfy the omni-directional low-dimensional characteristics, as shown in formula (7).
It is required that the CS matrix must satisfy certain conditions for effective reconstruction of the coefficient vector θ.
In addition, to apply CS algorithms, the sampling matrix must satisfy the restricted isometry property (RIP) for guaranteeing reliable estimators.
From (11), notice that for F(x t ) to be full rank and hence nonsingular, at least one matrix must satisfy ∃ k : rank G k ⊙ ∂ τ k ∂ x t 1 G k ⊙ ∂ τ k ∂ x t 1 T ≥ 1, k = 1, 2, 3. (12).
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Note that those matrices must satisfy the physical realizability condition (8), and thus they cannot be freely chosen.
Note that, for the Hamiltonians (hat{H}_{ K}) and (hat{H}_{mathrm{int}}) to be Hermitian, the matrices must satisfy (R_{ K}=R_{ K}^{top }) and (R_{1}^{top }=R_{2}); these are the physical realizability conditions in the scenario of direct interaction.
The blocking matrix B must satisfy two conditions: BH B = I and B H F ~ m H c ( 0 ) = 0.
The matrix S must satisfy the following constraints: The conflict-free constraint: Every link must check the SINR-based model, and so inequalities 4 and 5 and can only be scheduled during TUs of the link scheduling period T s.
A sufficient condition for these methods to work is that the matrix Φ·Ψ must satisfy the so-called restricted isometric property (RIP) [75, 83, 90]; which will be discussed in the following section.
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