針對異質性網路所設計以毫微微基地台輔助之合作式定位追蹤方法

Abstract

近年來，手機的定位和追蹤問題吸引了很多研究學者的關注。對於室內定位而言，進階的長期演進技術 (long term evolution advanced (LTE-A))系統相較於全球定位系統(global positioning system (GPS))具有較佳的室內訊號，因為LTE-A異質性網路環境中可同時存在大基地台(macro base station (mBS))和毫微微基地台(femto BS (fBS))。由於使用者可自行在家中或工作場所內架設fBS，所以我們無法獲得fBS的確切位置資訊。這些具有位置不準確性的fBS，我們將使用信賴資訊(belief information)來描述。在考慮fBS的信賴資訊下，本論文提出一個以處理室內手機定位的方法-毫微微基地台輔助之合作式定位追蹤(femto-assisted cooperative location tracking (FACLT) algorithm)，該演算法討論在合作式定位追蹤的貝葉斯估計問題，並採用粒子濾波器實現。此外，在無法取得來自其他手機的合作式定位訊號情況下，我們提出了一個毫微微基地台輔助之定位追蹤(femto-assisted location tracking (FALT))方法探討僅採用fBS輔助的定位效能。另一方面，為了降低利用粒子濾波器所面臨的高運算複雜度問題，我們進一步提出一個具低複雜度的簡化版FALT方法。在LTE-A異質性網路的環境下，模擬結果顯示我們所提出的方法相較於常見的定位方法(例：基地台識別碼)可獲得較佳的手機定位結果。

Location estimation and tracking for mobile stations (MSs) have attracted a significant amount of attention in recent years. In indoor environment where the signals from global positioning system (GPS) are either weak or blocked, signal sources from long term evolution advanced (LTE-A) system can be adopted to provide location estimation for MS. Based on the range signals from macro base station (mBS), femto BS (fBS), and neighbor MSs which can be cooperated in LTE-A heterogeneous networks (HetNet), we propose femto-assisted cooperative location tracking (FACLT) algorithm to estimate MS's position especially for indoor environments with insufficient signal inputs from mBSs. Since fBSs are usually deployed by users in their residential or business buildings, the locations of these fBSs generally cannot be known exactly. We depict the imprecise fBSs' positions as belief information to investigate a cooperative location tracking problem based on Bayesian estimation method and utilize particle filtering technique to develop the FACLT algorithm. The femto-assisted location tracking (FALT) scheme based on FACLT algorithm is proposed for practical HetNet scenario. Moreover, a simplified FALT (FALT-S) scheme is proposed to reduce the computation cost resulting from the particle filter in original FACLT method. Performance evaluation is conducted based on the LTE-A HetNet environments. Compared to conventional scheme (i.e. Cell Identification), simulation results show that the proposed FACLT algorithms can provide better location estimation of MS in different scenarios through the assisted fBS and cooperative MSs.