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Consequently, is also a zero mean random variable.
Note that our basic model may be used to study some NLOS scenarios since each tap of the channel impulse response is a zero mean random variable.
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But their restrictions to the finite-neighborhood are not necessarily zero mean random variables.
First, it is based on the correct assumption that and are modeled as zero mean random variables.
Assuming that the channel tap coefficients are independent with zero mean, this implies that are zero mean random variables and pairwise uncorrelated for different.
Theorem 2.2 Let α > 0. Suppose that { X n, n ≥ 1 } is a sequence of zero mean random variables which is stochastically dominated by a random variable X with E | X | ln + | X | < ∞.
Theorem 2.1 Let α > 1 / 2, α p ≥ 1 and p > 1. Suppose that { X n, n ≥ 1 } is a sequence of zero mean random variables which is stochastically dominated by a random variable X with E | X | p < ∞.
Corollary 2.1 Let 1 < p < 2. Suppose that { X n, n ≥ 1 } is a sequence of zero mean random variables which is stochastically dominated by a random variable X with E | X | p < ∞.
Input vector x t) is a stationary sequence of independent zero mean Gaussian random variables with a finite variance s x 2. z(t) is an independent zero mean random variables with variance s z 2. h ˜ n is independent of x t).
We assume that is approximated as a zero-mean random variable with the variance [17].
In [6, 7], the interference was modelled as zero-mean random variable.
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