臺灣大學: 電信工程學研究所陳光禎朱峰森Chu, Feng-SengFeng-SengChu2013-03-272018-07-052013-03-272018-07-052011http://ntur.lib.ntu.edu.tw//handle/246246/252766隨著使用者越來越多，人們對無線通訊系統的要求是越來越嚴苛。根據國際電信 聯盟所認定，下一代無線通訊系統的設計標準為(1) 對靜止的使用者提供至少1 Gbps 的資料傳輸速率(2) 對移動的使用者提供至少100 Mbps 的資料傳輸速率。要達到這兩個目的，其中一個重要的方法就是在原先的廣域基地台下增加各種新型的基地台(例:中繼基地台、小基地台與微型基地台)來減少傳送端跟接收端的傳 輸距離及減少廣域基地台的負擔。而目前在制定下一代通信系統LTE 跟 LTE-Advanced 的組織3rd Generation Partnership Project (3GPP) 也的確是這樣做 的。除此之外，許多更先進的技術(如:多輸出多輸入及正交分頻多工存取) 也都被整合進基地台跟使用者設備以增進頻譜使用效率。然而，在建立這樣擁有許多尖端基地台與使用者設備的通訊網路時，干擾控制與能源消耗是兩個無可迴避的問題。由於基地台跟使用者設備的數目大幅度上升，網路中的能源消耗將變成一個不可忽視的數字。這些新增的能源消耗不但會為營運商的營利帶來負面的效果，對日益嚴重的全球氣候變遷更是雪上加霜。此外由於頻譜是十分稀少的無線資 源，下一代通信系統為了提供超高資料傳輸速率勢必要讓許多基地台都共用同一 塊頻譜。但如此一來基地台間的干擾控制將構成嚴重的挑戰。這篇論文主要就是 針對干擾控制與能源效率這兩個問題提出解決方案。 在能源效率這方面，我們首先透過調整計算能源與執行能源的量來最小化基地台 的總能源消耗。這是因為一個通信系統的所有元件可被分為計算元件與執行元件 兩種，而消耗在執行元件的能源可以透過內建在計算元件的最佳化演算法來降 低。但是很顯然地，如果我們使用較複雜的最佳化演算法，執行元件的能源消耗 就會較低，但計算元件卻需要消耗較多的能源在執行最佳化演算法上。這就是為 什麼我們認為系統的總能量消耗可以透過適當的平衡計算能量與執行能量來最小 化的理由。除了基地台端之外，我們還考慮了使用者設備的能源使用效率。我們 注意到現代的通信系統都擁有數個頻帶可以同時傳輸資料，而使用者在甚麼時間 點接收哪個頻帶的資料是由基地台決定的。這告訴我們，如果基地台有意識地將 每個使用著的資料安排在同一時間點的多個頻帶中傳輸，那每個使用者所需要的 接收資料的時間就會下降。若是允許使用者設備在不用接收資料的時候關閉，我 們就可以降低使用者端的能源消耗。 在干擾控制方面，我們將注意力集中在微型基地台與廣域基地台間的干擾上。這 是因為微型基地台與廣域基地台間往往沒有後端協調的介面，因此雙方不容易知 道對方使用那些頻帶，再加上同一個廣域基地台往往會覆蓋許多微型基地台，讓 中央控管更顯困難。因此，我們提出三種分散式干擾控制的機制：後端限制資源 分配，空間通道隔離與感知無線電資源管理，來解決這樣的挑戰。首先，我們發 現微型基地台的後端傳輸速率往往低於無線端的傳輸速率。因此每一個無線基地 台事實上只能使用一部份的系統頻寬，就算周圍都無人也無法佔住全部的頻帶。 於是我們就提出一個基於季伯取樣的分散式頻帶選擇演算法，讓每一個微型基地 台自行選擇使用的頻帶，同時讓每一個基地台所受到的干擾最小化。接著，我們 提出利用空間通道將廣域基地台與微型基地台隔離的方法來消除兩者間的干擾。 最後一個避免干擾的方法則是奠基於感知無線電的觀念，我們提出一個通道偵測 的演算法來讓每一個微型基地台判定那些頻帶是可以使用的。除此之外，我們也 提出感知資源分配演算法來讓每一個微型基地台分配它所可以使用的頻帶給使用 者。以上所提及的每一種方法都經過實際的模擬驗證，由於此模擬平台是仿照 3GPP 評價下一代的無線通信系統( LTE 與LTE-Advanced ) 的平台所建立的，因此所提出的方法皆有相當大的可實現性。Next generation cellular systems are expected to support data rate higher than 1 Gbps for static user equipment (UE) and 100 Mbps for mobile UE [1], even when the number of UEs increases significantly. One way to achieve this tough criterion is to introduce more base stations (BS) (e.g., relay base station, pico base station and femto base station) into coverage of existing macro base station, to decrease the transmission distance and to reduce the load of macro base station. Currently, such approach has been adopted by advanced cellular systems such as 3GPP LTE and LTE advanced. Otherwise, aggressive technologies such as multiple input and multiple output (MIMO) and Orthogonal Frequency Division Multiple Access (OFDMA) are also integrated into both BSs and UEs to enhance spectrum utilization efficiency. As a result, future cellular systems are expected to include more BSs and UEs with higher complexity, and thus induces two critical challenges on energy efficiency and interference control. It is due to the increasing number of advanced BSs and UEs significantly impacts operator energy cost and global environment. On the other hand, due to the trend to allow every BS to reuse system spectrum to maximize spectrum efficiency, the inter-cell interference emerges as a serious challenge. In this dissertation, both kinds of challenges are investigated based on OFDMA systems. For energy efficiency, we start from minimizing overall energy consumption of base station via proper trade-off between computation energy and execution energy. It is due to that, the components of a system fall into two types as computation units and execution units, and the energy consumption of execution units can be minimized by the optimization implemented in computation units. It can be expected that, the higher the complexity of the optimization algorithm executed in computation units results in lower energy consumption in execution units. It implies the overall energy consumption can be minimized by proper trade-off between the energy consumption for computation and the energy consumption for execution. In addition to the energy efficiency of BS, we further consider the energy efficiency of UE. We note that there are multiple frequency bands in one time slot, and the reception schedule of each UE is determined by BS. The duration that each UE has data to receive can be reduced by allowing BS to arrang transmissions to each UE into fewer time slots. If UE can turn off its circuit when there is no data to be receive, we can enhance the UE energy efficiency. For interference control, we focus on the scenario that femto base stations are deployed under coverage of macro base station. Due to the difficulties of backhaul coordinations among macro base station and femto base stations, distributed interference mitigation schemes are necessary. Three approaches are proposed as backhaul constrained resource allocation, spatial channel separation and cognitive resource management. In the first approach, we note that the backhaul throughput of femto base station is usually lower than air interface, and thus each femto base station could occupy partial system spectrum. A spectrum selection mechanisms based on Gibbs sampler is proposed, by which the overall interference can be minimized. In the second approach, we propose to separate the transmissions of macro base station and femto base station by spatial channels, if multiple antennas are available. In the third approach, we exploit the cognitive radio for each femto base station to avoid interference. A generalized likelihood ratio test is proposed to identify which spectrum is not being used by other base stations, and a resource allocation is proposed for femto base station to allocate spectrum among UEs. As all of the proposed approaches are evaluated under the simulation environment specified by 3GPP LTE/LTE-Advanced, they are ready for advanced cellular systems.2917245 bytesapplication/pdfen-US能源管理與效率分散式干擾控制無線資源分配正交分頻多工存取系統Heterogeneous networkspower managementenergy efficiencydistributed interference controlresource allocationOFDMA.[SDGs]SDG7thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/252766/1/ntu-100-D94942017-1.pdf