For the first time, characterization has been provided for equivalent SIR

random process, which arises in wireless communication channel due to effects of kappa-mu shadowed fading and multiple Rayleigh CCIs, by deriving closed-form expressions for standard first order statistic measures, PDF and CDF.

The process {bar.x](t), [bar.y](t), [X.sub.[epsilon]] (t), [Y.sub.[epsilon]]} depends only on its state at the time moment t; that is, by definition (17) the two-dimensional

random process [X.sub.[epsilon]] (t), [Y.sub.[epsilon]] (t) is filtration {[F.sup.t] t [greater than or equal to] 0} adapted and, for a given solution of the system of (15), has the Markov property.

Equation (3) indicates that the Allan variance is proportional to the total power output of the

random process when passing through a filter with the transfer function of the form [sin.sup.4](x)/[(x).sup.2].

In CSO's rankings, most companies (48.4 percent) fell in the middle of these hierarchies, somewhere between being approved vendors with

random processes and trusted partners with dynamic processes.

"Largely it is a

random process, however we do have travel issues and long-distance trips to consider with supporters in mind," says Snellgrove.

Consider a

random process which can be described by discrete random variable X with n possible outcomes {[x.sub.1], [x.sub.2], .........

In this diagram: [[omega].sub.o] is the setting task (cyclic load speed); [omega](t) is the electric drive adjustable output value (cyclic speed); [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the random perturbation signal that served as the sum of the mean [bar.M](t) and centered

random process [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]), [W.sub.reg](s) is the transfer function of the regulator, [W.sub.U](s) is the transfer function motor control signal, [W.sub.M](s) is the transfer function motor disturbance (torque of resistance).

The Ricean K-factor, ratio of powers dominant and scatter components, has been already treated as log-normal

random process in [10], but for the narrow-band fixed wireless channels.

Under the assumption that the wall temperature is a stationary

random process, the autocorrelation functions are analytically derived for the fluid temperature and flow velocity.

[15,16] proposed a bands method to estimate the fatigue damage of a wide-band

random process in the frequency domain.

In contrast, we can observe from (26), (30), (33), and (34) that the proposed channel model is a non-WSSUS

random process, because its 4D TF-CF is a TF-varying function, meaning that ([R.sub.H]([t.sub.1], [f.sub.1]; [DELTA]t, [DELTA]f) [not equal to] ([R.sub.H]([t.sub.2], [f.sub.2]; [DELTA]t, [DELTA]f) for different observation instants ([t.sub.1], [f.sub.2]) and ([t.sub.2], [f.sub.2]).