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Exact(7)
which is again due to independence between and, and the inequality on the last line follows from.
So (mathcal {T}_{n}capmathcal {T}_{k}=emptyset), which, due to independence of (xi_{i}), implies independence of (H^{s}(n)) and (H^{s} k)).
where f(h1,…,h M ) is the pdf for the channel vector,given by f ( h 1, …, h M ) = ∏ i = 1 M ( m i λ i ) m i h i m i − 1 Γ ( m i ) e − m i λ i h i for Nakagami fading (due to independence amongst the parallel channels).
Due to independence, <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0010912.e033.PNG" class= inline-graphic"/>, a definition that can easily be extended to more than three dimensions.
Moreover, the reduced network is well separated into two due to independence of S8from S8fromS8from
Therefore the conditional density of the image G x given L x is given by the following due to independence assumption.
Similar(53)
where due to the independence of signals, reordering is not needed.
where the first equality is due to the independence of the subchannels.
Due to the independence of the model particles, the parallelisation of the main compute intensive loops is relatively straightforward.
Due to the independence between the main and wiretap links, the random variables γ (n) and ρ are also independent.
where the first inequality follows from [27, Theorem 8.6.5], and the last equality is due to the independence between and.
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