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1.5 Mathematical expectation and variance of the simplest call flow

Lecture



Determine the mathematical expectation of the number of calls arriving in time [0, t):

  1.5 Mathematical expectation and variance of the simplest call flow

- expression of the initial moment of the first order.

The first term of the sum at K = 0 is zero, therefore summation can be started with K = 1:

  1.5 Mathematical expectation and variance of the simplest call flow

Denoting K − 1 = r, using the Maclaurin series, we obtain:

  1.5 Mathematical expectation and variance of the simplest call flow

But on the other hand: Λ (t) = μ⋅t - by definition for a stationary flow. Therefore, for the simplest flow, the intensity is numerically equal to the parameter - μ = λ. The variance of a random variable distributed according to Poisson’s law will be determined from the expression:

  1.5 Mathematical expectation and variance of the simplest call flow

Where M k is the mathematical expectation, M k = Λ (t) = λ⋅t, α2 is the initial moment of the second order.

By definition:

  1.5 Mathematical expectation and variance of the simplest call flow

Consequently:

α2 = λ⋅t⋅ [λ⋅t + 1]

Dispersion of the simplest flow:

Dk = α2 − MK 2 = λ⋅t⋅ (λ⋅t + 1) - (λ⋅t) 2 = λ⋅t

Thus, the variance of the simplest call flow is equal to the expectation:

M k = Dk = λ⋅t

From this property of the simplest flow follows an important conclusion for practice: the relative variability of the simplest call flow is the smaller, the greater its mathematical expectation. Relative variability is estimated by the coefficient of variation, the ratio:

  1.5 Mathematical expectation and variance of the simplest call flow

Consider two extreme cases: the limiting value at which the relative oscillation is zero (corresponds to the deterministic flow) and the second case as Δ t → 0 (the relative variability will increase indefinitely).

  1.5 Mathematical expectation and variance of the simplest call flow

In the first case, with three lines of loss will not, and η = 100%,

where η = t Zan / t OBS.

In the second case, there will be no losses with three lines, but as Δ t → 0 η → 0.

η is the average use of channels

t obb - the duration of the observation,

t Zan - the duration of employment of one channel.

The higher the relative variability of the call flow, the lower the average utilization of channels in the beam with a fixed quality of service (P = const). This property of the flow explains the dependence:

  1.5 Mathematical expectation and variance of the simplest call flow

λ⋅t is the mathematical expectation of the number of calls coming in [0, t).

Hence, the efficiency of the telephone communication system is the higher, the greater the intensity of the incoming call flow to the system. This fundamental property of random call flows is widely used in queuing systems: in telecommunications, high-capacity telephone exchanges and switching nodes are built to concentrate the call flows; in trade - super-and hypermarkets; in transport - major airports and stations

Union and separation of independent simplest threads:

The union of independent simplest flows with parameters λ1, λ2, λ3, ..., λi, ..., λn will also be the simplest flow with parameter λ = ∑ λi equal to the sum of the parameters of the combined flows.

Poisson's recurrent formula:

  1.5 Mathematical expectation and variance of the simplest call flow

Denote by t in - the average length of stay in the system of one call (usually accepted t in = 1). Divide and multiply t by t in:

  1.5 Mathematical expectation and variance of the simplest call flow

Given this, in order to more efficiently service call flows, it is desirable to merge them.

Without proof, we note another interesting property of the simplest flow: when summing a large number of independent ordinary stationary flows with almost any after-effect, we obtain a flow that is arbitrarily close to the simplest .

Analogy: “when summing up a large number of independent random variables, subordinate to virtually any distribution laws, we get a value that is approximately distributed according to the normal law”.

created: 2017-07-02
updated: 2021-03-13
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Teletraffic Theory

Terms: Teletraffic Theory