在LTE-A異質網路下適用於物聯網通訊之高效率存取控制機制及因應流量之蜂巢間干擾協調機制

Abstract

隨著智慧型手持裝置的廣泛使用及新興應用服務的快速崛起，如即時影音串流服務與多媒體檔案分享服務，使得近年來無線網路上的資料傳輸量有顯著的成長。為了處理這些龐大的資料傳輸，長期演進技術升級版(LTE-A)採用了異質網路(HetNets)架構以擴增LTE-A之傳輸頻寬。LTE-A HetNets中包含了高功率大型基地台(macro eNB)及低功率微型基地台(pico eNB)/中繼節點(relay node)。在本論文中，我們將解決LTE-A HetNets中三大重要議題，分別是物聯網通訊之存取控制、節能、以及蜂巢間干擾協調。 針對物聯網通訊之存取控制議題，我們提出了具負載平衡之高效能協同式存取等級限制機制(CACB-LB)及因應流量之無線資源管理機制(TARRM)。我們提出的CACB-LB利用兩台相鄰基地台間分別只能存取其中一台基地台的機器型通訊裝置(MTC devices)的數量比率來決定在此兩台相鄰基地台在重疊覆蓋範圍中的MTC devices要連哪一台基地台的比率。透過此方式，我們所提的CACB-LB較目前最好相關研究CACB達到更好的基地台之間的負載平衡。我們所提的CACB-LB也利用MTC device從基地台收到的頻道品質指標(CQI)及連接到此基地台的MTC device數量之比值來調整預測到可能存取到此基地台的MTC device數量。因此，我們所提的CACB-LB可以比CACB取得更佳的存取等級限制之限制率集合，也可以降低MTC device的隨機存取延遲時間。我們所提的CACB-LB也可適用於使用者裝置(UE)。此外、我們提出的TARRM在同質性MTC device網路下會根據MTC device隨機存取的頻率及MTC device上傳或下載的資料量來配置無線資源給MTC device。在異質性MTC device網路下則是根據MTC device的優先權來配置無線資源給MTC device。因此，TARRM能更有效地利用無線資源。 針對節能議題，我們提出了根據自我組織網路可調適能量節省機制(AES)。我們提出的AES利用兩層級(蜂巢間層級及蜂巢內層級)多門檻負載管理機制來管理每個中繼節點。我們提出的AES能動態開關中繼節點以達到節省能源。AES也能動態調整中繼節點的傳輸訊號強度以達到節省能源及增加網路資源利用度。此外，我們提出的AES採用類神經網路來預測每個中繼節點的負載，藉以判斷將中繼節點關掉是否適當。 針對蜂巢間干擾協調議題，我們提出了基於自我組織網路動態蜂巢大小調適機制(SCSA)及因應流量之蜂巢間干擾協調機制(TAeICIC)以解決干擾問題。我們所提出的SCSA採用動態多門檻負載管理機制以動態調整低功率微型基地台的傳輸訊號強度。此外，我們提出的TAeICIC是根據一個排程參考，proportional-fair，在一個高功率大型基地台中動態配置適當數量的幾乎空閒子訊框(ABS)，以降低此高功率大型基地台對其鄰近低功率微型基地台之干擾，其中proportional-fair定義為依據UE從基地台收到的CQI所預估的UE吞吐量與預估的UE長期吞吐量的比值。

Wireless data traffic has seen prolific growth in recent years because the use of smart handheld devices and new emerging services are widespread, such as real-time video streaming and multimedia file sharing. To handle huge wireless data traffic, LTE-A (Long Term Evolution-Advanced) has adopted heterogeneous networks (HetNets) architecture, which consists of macro eNB and pico eNB/relay node (RN), to increase the capacity of LTE-A. In LTE-A HetNets, access control for Machine-to-Machine (M2M) communications, energy saving, and inter-cell interference coordination are three important issues, which are to be resolved in this thesis. For the access control for M2M communications issue, we propose two efficient cooperative access class barring with load balancing (CACB-LB) and traffic adaptive radio resource management (TARRM) schemes for M2M communications. The proposed CACB-LB uses the percentage of the number of MTC devices that can only access one enhanced Node B (eNB) between two adjacent eNBs as a criterion to allocate those MTC devices that are located in the overlapped coverage area to each eNB. Note that an eNB is a base station of LTE-A. In this way, the proposed CACB-LB can achieve better load balancing among eNBs than CACB, which is the best available related work. The proposed CACB-LB also uses the ratio of the channel quality indication (CQI) that an MTC device received from an eNB over the number of MTC devices that attach to the eNB as a criterion to adjust the estimated number of MTC devices that may access the eNB. As a result, the proposed CACB-LB can have a better set of barring rates of access class barring than CACB and can reduce random access delay experienced by an MTC device, which is also applicable to user equipment (UE). In addition, the proposed TARRM allocates radio resources for an MTC device based on the random access rate of the MTC device and the amount of data uploaded and downloaded by the MTC device in a homogeneous MTC device network, and the priority of an MTC device in a heterogeneous MTC device network so as to effectively utilize the radio resources. For the energy saving issue, we propose a self-organizing network (SON)-based adaptive energy saving (AES) mechanism for LTE-A self-organizing HetNets. The proposed AES uses two-level multi-threshold load management for each RN under different eNBs (inter-cell level) and for each RN within the same eNB (intra-cell level) so as to reduce the congestion in hot spot eNBs and RNs. In addition, the proposed AES can dynamically switch an RN between active and sleep modes to maximize the number of sleep RNs for adaptive energy saving. It can also dynamically change an RN’s coverage area to reduce energy consumption and to increase radio resource utilization. Besides, the proposed AES adopts a neural network predictor to forecast the loading of each RN to determine whether it is appropriate to switch an RN to sleep mode. For the inter-cell interference coordination issue, we propose SON-based cell size adaption (SCSA) and traffic adaptive enhanced inter-cell interference coordination (TAeICIC) to resolve the interference problem. The proposed SCSA uses dynamic multi-threshold load management to dynamically set the transmission power of each pico eNB by adjusting the pilot power. In addition, the proposed TAeICIC utilizes a scheduling metric, proportional-fair (PF), which is the estimated throughput based on the CQI reported by a UE divided by the estimated long term average throughput achieved by the UE, to dynamically allocate an appropriate number of Almost Blank Subframes (ABSs) in each ABS period in a macro eNB so as to mitigate the interference from the macro eNB to its adjacent pico eNBs.