於異質性正交分頻多重接取網路中資源配置與允入控制之聯合最佳化研究

Abstract

異質性網路為提高無線通訊網路的覆蓋範圍及系統通訊吞吐量之關鍵技術。由於小細胞為覆蓋於現行蜂巢式網路上，並與現有之網路系統共享相同的頻譜，故干擾管理為關鍵且重要的議題之一。本論文旨在研究異質性正交分頻多重接取(OFDMA)網路中資源管理及允入控制之聯合最佳化問題。吾人考慮一個小細胞網路與蜂巢式網路共存之架構，小細胞不僅僅受現有蜂巢式網路干擾所限制，並且亦受制於次級用戶最小速要求之限制。故吾人之目標除了關注於使小細胞基地台中盡可能服務最多的用戶，並使每個用戶均滿足最低傳輸資料速率之需求。為滿足上述之目的，吾人針對最大化網路頻譜效率及能量效率等議題，各別制定兩種聯合功率及允入控制之最佳化問題模型，其中針對網路能量效率所制定之聯合功率及允入控制問題於現有文獻中未被探討過。有鑑於允入控制及頻譜效率最大化均為困難性非常定多項式問題(NP-hardness)，故於先前之文獻經常分別探討，並無同時考慮OFDMA限制下之允入控制及頻譜效率最大化問題。在本論文中，吾人提出一種新的聯合最佳化框架，使其能同時考慮功率控制、允入控制及資源區塊之配置。經由先進的凸集近似技術和順序性次級用戶緊縮程序，吾人進一步開發高效能之演算技術，使其最大化頻譜/能量效率，並同時服務最多用戶數。經由電腦模擬驗證本論文所提出的演算法及架構皆能提供優越的頻譜/能量效率及較低的運算需求，極適用於下世代之寬頻無線通訊系統。

Heterogeneous network is a promising technology for improving coverage and throughput of wireless communication systems. Since small cells are overlaid on and share the same spectrum with the existing cellular network, interference management is paramount for small cells. This dissertation studies the joint power and admission control (JPAC) problem for orthogonal frequency division multiplexing access (OFDMA) based heterogeneous networks. We consider a small-cell network coexisting with a macro-cell network. Small cells are not only subject to constraints imposed by interference with the macro-cell network but also by the minimum achievable rates of secondary user equipment (SUE). The goal is to admit as many SUE as possible to satisfy the minimum rate requirements while maximizing a certain network utility associated with the admitted SUE. To this end, we formulate two JPAC problems aimed at maximizing the network spectral efficiency (SE) and network energy efficiency (EE), respectively, where the latter has not been considered before. In light of the NP-hardness of the admission control and SE maximization problems, prior works have often treated the two problems separately without considering OFDMA constraints. In this dissertation, we propose a novel joint optimization framework that is capable of considering power control, admission control, and resource block assignment simultaneously. Via advanced convex approximation techniques and sequential SUE deflation procedures, we develop efficient algorithms that jointly maximize the SE/EE and the number of admitted SUE. Simulation results show that the proposed algorithms yield substantially higher SE/EE and admit more SUE than existing methods. The proposed optimization framework can provide a promising solution for heterogeneous OFDMA networks in future wireless communication systems.