於2008年國際電信聯盟之無線電通信部門推出第四代通訊技術(Fourth Generation, 4G)，其進階長期演進技術(Long Term Evolution-Advanced, LTE-A)為最廣泛使用的標準，而於至今的2019年已推動第五代通訊技術(5^th generation wireless systems, 5G)，以順應更複雜的使用環境，分別為增強型行動寬頻通訊(enhanced Mobile Broadband, eMBB)，高可靠度低延遲通訊(Ultra-reliable and Low Latency Communications, URLLC)與大量機器傳輸(Massive Machine Type Communications, mMTC)。為提高資料傳輸率並降低延遲時間，故將整體使用之頻段提高已取得更多頻寬，但使用更高的頻段將會使覆蓋範圍相較4G的LTE-A為小。為解決此問題，透過佈署更多的小細胞基站(Small Cell)是解決覆蓋範圍不足的方法之一。
由於5G網路環境下增加佈署了小細胞基站，使用者可透過小細胞基站連線以確保傳輸的可靠度。另一方面，考慮到傳統無線網路環境下，使用者由原基地台至下一個基地台間只需透過一次換手即可。而在傳統換手策略中，當使用者移動至訊號較強基地台時，將會提出換手需求，也就是說當接收訊號強度(Received Signal Strength, RSS)或訊號接收功率(Reference Symbol Received Power, RSRP)大於門檻值時，則將使用者換手至下一個基地台。但於5G環境下有更為密集的小細胞基站佈署，因小細胞基站功率較小只能進行小範圍傳輸，因此涵蓋範圍較小。因此換手時若單純考慮RSS或RSRP將會導致換手次數過於頻繁，進而影響到網路服務品質(Quality of Service, QoS)。為解決此問題，本文將會先對使用者位置進行預測，確認使用者後續行進路，再利用啟發式演算法求出最佳換手策略，藉此讓環境中之基地台與小細胞基站都能達到負載平衡。模擬結果顯示，利用啟發式演算法所求得之換手策略明顯優於只考慮最短距離換手策略。

As technology evolved wireless network, next generation network (5G) is an important milestone at the future. Because of 5G have the high data rate, low latency and support the Multiple-In-Multiple-Out (MIMO). 5G was used to high-frequency band to achieve their requirements. However, that will cause coverage range smaller than Long Term Evolution-Advanced (LTE-A). Small cell and base station were deployed in order that solved the problems of coverage in an environment of 5G. On the other hand, User Equipment (UE) is mobile that will cause a connection to be breaking when UE left coverage of service in small cell and base station. According to above content, handover guarantee UE remain UE's connecting state.
In traditional wireless networks usually make once handover could UE connect to next base station or small cell. Due to deploy many base station or small cell in the 5G network that results in a greater number of handovers than LTE-A. In addition, whenever UE could get stronger RSS or RSRP than the previous access point that sends handover requests, but, this threshold ignored the 5G network supporting MIMO and problem of overloading. RSS and RSRP aren't guaranteed to occur overloading and remain this QoS in base station or small cell. According to above content, in this research, we will consider overloading of base station and small cell and add loading about parameter to handover accessing target strategy. We used Hill-Climbing algorithm and Simulate Anneal-algorithm to search optimal UE’s accessing target. Both algorithms compare based on distance accessing strategy. Finally, we will follow the result and obviously notice accessing strategy through Simulate-Anneal algorithm better than used in Hill-Climbing algorithm a little and both algorithms are better than based on distance accessing strategy more.

第一章 緒論 1
第二章 相關研究 7
第三章 問題定義 15
第四章 基於啟發式演算法之換手策略 23
第五章 實驗模擬 33
第六章 結論與未來展望 43
參考文獻 45

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