Method for predicating position of mobile user in WCDMA (Wideband Code Division Multiple Access) and WLAN (Wireless Local Area Network) heterogeneous network environment
A technology for heterogeneous networks and mobile users, applied in electrical components, wireless communications, etc., can solve the problems of complex decision-making algorithms for mobile user location prediction, and achieve the effects of reducing prediction overhead, simple operation, and improving prediction accuracy.
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specific Embodiment approach 1
[0034] Specific implementation mode one: combine figure 1 and figure 2 Describe this implementation mode:
[0035] The mobile user position prediction method in the WCDMA and WLAN heterogeneous network environment of the present embodiment is realized by the following steps:
[0036] 1. Divide the dual coverage area into different sensitive areas
[0037] The dual coverage area is divided into three different sensitive areas according to the different positions of the mobile users: the central area, the ring area and the boundary area; wherein, the central area is the minimum distance D between the user and the boundary of the dual coverage area now A circular area greater than D, which is a minimum distance D from the boundary of the dual coverage area now An annular area less than D and greater than d, the boundary area is the minimum distance D from the boundary of the double coverage area now Areas smaller than d, where D>d;
[0038] Wherein, the value of described D...
specific Embodiment approach 2
[0066] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is: S' in step three pre (n+1)=P(t n+1 ); among them, P(t)=a 0 t 0 +a 1 t 1 +a 2 t 2 +…+a k t k , t is the prediction time, a i As a coefficient, t=t n+1 Substituting into P(t), we get S' pre (n+1). Other steps and parameters are the same as those in Embodiment 1.
specific Embodiment approach 3
[0067] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: the coefficient a i Using the principle of minimum variance to treat each ai as a variable and differentiate the equations separately, the following equations can be obtained:
[0068]
[0069] Use the Gaussian exclusion method to solve the equation system, and solve each coefficient a i The value of , where k represents the highest power, H represents the location information of H moments, R h is the real value of the user at the hth prediction time. Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.
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