正交分頻多工無線網路之資源管理

Abstract

本文旨在探討正交分頻多工無線網路之資源管理並針對各式頻譜與功率分配問題提出最佳與次佳的演算法。我們的演算法採取的是使用者去除的概念來解耦(decouple)多用戶多載波分配的關連性。無線資源分配牽涉到眾多用戶需求條件與系統參數與設計之選擇及實務考量，其複雜度相當高，在大部分情況下無法有最佳的解決方案。本文所考量的情境(scenario)為單一基地台與多個移動台用戶的細胞式通訊系統。我們主要解決的問題有下列幾項。第一個問題為「在滿足不同用戶之不同傳輸率的要求下使用最少的總功率或能量來分配既有的無線電資源（傳輸功率、能量及次載波）。」其中，我們考慮各通道之增益雜訊比(channel-gain-to-noise-ratio)之不同，提出最佳與次佳的演算法。第二問題則是試圖在單一用戶尖峰傳輸功率的限制下對總加權傳輸率極大化。我們提出兩種次佳的頻譜分配演算法，其中之一利用了對偶分解(dual-decomposition)的方法。第三問題考量了用戶公平性的問題，因此針對用戶傳輸率總乘積之極大化提出一個次佳演算法。傳輸速率總乘積之極大化可使系統在傳輸速率總和增加的同時儘可能地維持各用戶一定的傳輸率! 最後一個問題，我們針對多輸入多輸出的通訊系統提出低複雜度的資源管理演算法，使系統在滿足用戶之不同傳輸率要求下能讓總傳輸功率極小化。在此多輸入多輸出的通訊系統中我們採取主對角線塊狀化(block diagonalization)來使其每一載波通道皆可允許多個用戶在無彼此干擾下傳輸。我們開發的演算法亦應用了對偶分解法來解決用戶的移除與選擇。與第二個問題的不同是：每一載波可以保留給多個用戶。基於主對角線塊狀化的特性我們所提出的演算法在保留使用效率高的用戶之同時亦兼顧了保持其空間通道正交的優勢。對於上述各類資源管理問題所提出的最佳或次佳分配解我們均分析了其複雜度並以電腦模擬證明所提出的演算法皆能有甚佳的效能表現。

Algorithms for finding suboptimal and optimal solutions to total power minimization or capacity maximization resource allocation problems in OFDMA-based networks haven been studied by many authors. But the complexities of finding the optimal solution and some suboptimal are prohibitively high and only few numerical examples for low dimension cases can be found. On the other hand, low-complexity suboptimal solutions often give unsatisfactory performance.

In this thesis, we propose optimal and suboptimal resource allocation solutions for OFDMA and MIMO-OFDMA wireless networks. Various design criteria and system constraints are considered. The corresponding complexities for all suboptimal solutions are relatively low while that for the optimal algorithm is only moderate high. We first investigate the problem of transmit power minimization in an OFDMA downlink network subject to user rates and BER requirements constraints. We provide near-optimal and optimal solutions based on the dynamic programming and branch&bound methodologies. The second scenario we consider is a weighted sum rate maximization problem which is solved via dynamic programming and dual decomposition. We then proceed to consider the product rate maximization scenario and present a suboptimal solution. Finally, we consider a total power minimization problem for a MIMO-OFDMA wireless networks and present a low-complexity solution.

The main concept in our proposed algorithms can be easily applied to obtain a near-optimal solution for many similar multi-constraints optimization problem with low complexity.