A system and implementation method for constructing a wireless network service distribution map
A service distribution and wireless network technology, applied in the wireless network field, can solve the problems of coarse service model granularity, uneven base station deployment density, uneven spatial service density distribution, etc., to achieve the effect of reducing the amount of calculation and flexible service scheduling
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Embodiment 1
[0044] Embodiment 1 is realized by Kriging interpolation, assuming that the base station b is obtained through the third step 1 ,b 2 ,...,b m The real-time traffic density of the grid where it is located is z(b 1 ), z(b 2 ),...z(b m ), and satisfy the second-order stationary characteristics and eigenassumptions, then at the estimated point b 0 The estimated traffic density at is:
[0045]
[0046] Among them, λ i is the weight coefficient, indicating the contribution of the i-th base station to the estimated point. According to the requirements of unbiasedness and optimal variance, the weight coefficient λ can be determined by the covariance matrix C of the sample i :
[0047] Unbiasedness: E[z * (b 0 )-z(b 0 )]=0 simplification can get:
[0048]
[0049] Optimal (minimum) variance: minσ 2 =E[(z * (b 0 )-z(b 0 )) 2 ], use the Lagrange multiplier method to solve the extreme value under the unbiased condition, namely:
[0050]
[0051] Among them, μ is ...
Embodiment 2
[0055] The second embodiment is realized through natural neighbor interpolation, assuming that the base station p 1 ,...,p 4 The real-time traffic density of the grid where it is located is z(p 1 ),…,z(p 4 ),like Figure 5 As shown in (a), a Thiessen polygon is constructed for the estimated point p, and p 1 ,...,p 4 The constructed Thiessen polygon overlaps to form a second-order Thiessen polygon, and the base station p corresponding to the Thiessen polygon overlapping with the Thiessen polygon of point p 1 ,...,p 4 Called the natural neighbor of point p, the estimated traffic density at point p is:
[0056]
[0057] Among them, m is the number of natural neighbors of point p, in this example m=4, λ i is the weight coefficient, indicating the contribution of the i-th base station to the estimated point.
[0058] Weight factor λ i It is determined by the mutual positional relationship between point p and each natural neighbor, specifically, it is measured by the ove...
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