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無處不在的無線業務以其不斷增長的需求促進了對稀缺無線資源的高效利用。多年來，優化技術被廣泛地運用在無線資源分配的方案設計上，從而達到改善系統性能之目的。在此領域的大部分工作中，用於定義優化問題的系統參數被假設為精確可知。然而，實際的系統參數往往是時變且隨機的。忽略系統參數的不確定性極易導致資源分配決策偏離最優狀態，或者甚至違反系統運行約束而使分配決策不可行。, 本論文提出了一套用於無線通信的動態資源分配的隨機優化框架。具體而言，本文抓住了不確定系統參數的隨機本質，從而建立結合實際的問題模型，並且開發了高效的算法，以獲得最佳的分配決策。本文將提出的框架成功地應用於三個很有前景的無線通信系統中：自適應正交頻分多址接入（OFDMA）系統，多輸入多輸出（MIMO）天線系統，以及位置感知網絡。每一個應用系統中都存在與實踐相關的挑戰，而這些挑戰則源自於傳統基於靜態優化的設計在提供滿意的服務質量（QoS）中遇到的困難。結果表明，使用隨機優化的動態資源分配，可達到了更加穩健的QoS性能，並且顯著增強系統的實用性。, 在自適應OFDMA系統中，本文提出了一套“慢適應“的最優子載波分配方案。該方案通過採用更新遠慢於無線信道波動的資源分配策略，從而使計算複雜度和控制信令大大降低。本文根據不同的應用背景，將慢適應子載波分配問題描述成為幾個不同的隨機規劃問題。其中，我們設計了一個高效的算法專門用以求解機會約束規劃類型的子載波分配問題。, 在MIMO天線系統中，本文提出了一套天線和發射功率聯合分配的方案，使利用多天線支持單一移動終端上的多個無線電模塊同時運行成為可能。該方案最大化了長期系統吞吐量，同時以容許偶爾違反系統約束的方式滿足每個無線模塊的短期傳輸速率需求。結果表明，最優天線和發射功率分配顯著提高系統的吞吐量和滿足QoS的成功概率；而最優天線分配與最優功率分配相比，對提高系統吞吐量有更大的貢獻。, 在位置感知網絡中，本文提出了一套魯棒功率分配方案，用以抵抗網絡參數的不確定性，這些參數包括用戶位置以及信道狀態。本文提出了一種新的魯棒優化方法，用以獲得最優功率分配，從而提高定位精度和網絡能效。結果表明，魯棒方案顯著優於非魯棒的功率分配和平均分配方案。, 本論文著眼於縮短傳統基於靜態優化的設計與其現實針對性之間的差距。鑒於許多無線系統的參數在本質上都具有隨機性，本文所提出的採用隨機優化的資源分配方法，有望在未來無線通信中得到更多的應用。, The growing demand of ubiquitous wireless services has prompted the efficient utilization of scarce radio resources. Over the years, optimization techniques have been widely employed to design optimal resource allocation schemes to achieve performance improvement. Most work in this area assumes that the system parameters defining the optimization problem are precisely known. In practical systems, however, these parameters are often time varying and random. Ignoring the parameter uncertainties would easily lead to suboptimality or even infeasible solutions that violate system operation constraints., This thesis presents a stochastic optimization framework for the dynamic resource allocation in wireless communications. In particular, practice-relevant problem formulations are proposed to capture the stochastic nature of the uncertain system parameters, and efficient algorithms are developed to obtain the optimal allocation decisions. The proposed framework has been successfully applied in three promising wireless systems: adaptive orthogonal frequency division multiple access (OFDMA) systems, multiple-input and multiple-output (MIMO) antenna systems, and location-aware networks. Each application contains practice-relevant challenges, where the conventional designs using deterministic optimization fail to provide satisfactory quality of service (QoS). The results demonstrate that the dynamic resource allocation using stochastic optimization achieves more robust QoS performance and remarkably enhances the system practicality., In adaptive OFDMA systems, a slow adaptation scheme is proposed for optimal subcarrier allocation. The proposed scheme updates the resource allocation decisions on a much slower timescale than that of channel fluctuation, which drastically reduces the computational complexity and control signaling overhead. The problems are formulated into several stochastic programs based on different application scenarios. An efficient algorithm is developed for solving the chance constrained subcarrier allocation problem., In MIMO antenna systems, an antenna-and-power allocation scheme is proposed to enable the use of multiple antennas to support multiple radios co-operating on the same mobile device. The proposed scheme maximizes the long-term system throughput while satisfying the short-term data rate requirement of each radio transmission with occasional outage. The results show that both system throughput and success probability of QoS satisfaction are improved, and the optimal antenna allocation contributes to a larger portion of throughput increase comparing with the optimal power allocation., In location-aware networks, robust power allocation schemes are proposed to combat the uncertainties in network parameters including user positions and channel conditions. A novel robust optimization method is developed to obtain the optimal power allocation, which improves both localization accuracy and network energy efficiency. The results show that the robust schemes remarkably outperform both non-robust power allocation and uniform allocation., The goal of this thesis is to bridge the gap between the current designs under the deterministic optimization framework and their practical relevance. Given the fact that many wireless system parameters are stochastic in nature, the proposed resource allocation methods using stochastic optimization are expected to find further applications in wireless communications., Detailed summary in vernacular field only., Li, Weiliang., Thesis (Ph.D.)--Chinese University of Hong Kong, 2012., Includes bibliographical references (leaves 157-175)., Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web., also in Chinese., 摘要, p.iii, Acknowledgement --- p.vi, Contents --- p.ix, List of Figures --- p.xiii, List of Tables --- p.xvii, List of Acronyms --- p.xviii, List of Notations --- p.xxi, Chapter 1 --- Introduction --- p.1, Chapter 1.1 --- Resource Allocation in Wireless Communications --- p.2, Chapter 1.2 --- Stochastic Optimization and Its Applications --- p.4, Chapter 1.2.1 --- Robust Optimization --- p.5, Chapter 1.2.2 --- Chance Constrained Optimization --- p.8, Chapter 1.3 --- Motivation and Research Focus --- p.10, Chapter 1.3.1 --- Motivation --- p.10, Chapter 1.3.2 --- OFDM and OFDMA Systems --- p.14, Chapter 1.3.3 --- MIMO Antenna Systems --- p.16, Chapter 1.3.4 --- Location-Aware Networks --- p.18, Chapter 1.4 --- Contributions --- p.20, Chapter 1.5 --- Organization --- p.23, Chapter 2 --- Slow Subcarrier Allocation in Adaptive OFDMA Systems --- p.25, Chapter 2.1 --- System and Channel Model --- p.29, Chapter 2.1.1 --- Channel Model --- p.29, Chapter 2.1.2 --- Slow Adaptive OFDMA --- p.30, Chapter 2.2 --- Slow Adaptive OFDMA with Average Rate Constraints for Elastic Traffics --- p.32, Chapter 2.2.1 --- Problem Formulation --- p.33, Chapter 2.2.2 --- Computation of Expected Average Data Rate --- p.34, Chapter 2.2.3 --- Numerical Results --- p.37, Chapter 2.3 --- Slow Adaptive OFDMA with Average Rate Constraints for Inelastic Traffics --- p.40, Chapter 2.3.1 --- Problem Formulation --- p.40, Chapter 2.3.2 --- Numerical Results --- p.43, Chapter 2.4 --- Slow Adaptive OFDMA with Probabilistic Rate Constraints --- p.46, Chapter 2.4.1 --- Problem Formulation --- p.47, Chapter 2.4.2 --- Safe Tractable Constraints --- p.48, Chapter 2.4.3 --- Algorithm Design --- p.51, Chapter 2.4.4 --- Problem Size Reduction --- p.59, Chapter 2.4.5 --- Numerical Results --- p.61, Chapter 2.5 --- Summary --- p.70, Chapter 3 --- Dynamic Antenna-and-Power Allocation in Composite Radio MIMO Networks --- p.72, Chapter 3.1 --- System Model --- p.76, Chapter 3.1.1 --- Composite Radio System --- p.76, Chapter 3.1.2 --- Channel Model --- p.77, Chapter 3.1.3 --- Dynamic Antenna-and-Power Allocation --- p.78, Chapter 3.2 --- Problem Formulation --- p.80, Chapter 3.2.1 --- MIMO Channel Capacity --- p.80, Chapter 3.2.2 --- Chance Constrained Formulation --- p.81, Chapter 3.2.3 --- Safe Tractable Formulation --- p.82, Chapter 3.3 --- Search for Feasible Solutions --- p.85, Chapter 3.3.1 --- Algorithm Design --- p.87, Chapter 3.4 --- Approach to Optimal Solution --- p.89, Chapter 3.4.1 --- Cutting-Plane-Based Algorithm --- p.91, Chapter 3.4.2 --- Optimal Antenna-and-Power Allocation --- p.95, Chapter 3.5 --- Simulation Results --- p.96, Chapter 3.6 --- Summary --- p.106, Chapter 4 --- Robust Power Allocation for Energy-Efficient Location-Aware Networks --- p.107, Chapter 4.1 --- System Model --- p.110, Chapter 4.1.1 --- Network Settings --- p.110, Chapter 4.1.2 --- Position Error Bound --- p.111, Chapter 4.1.3 --- Directional Decoupling of SPEB --- p.113, Chapter 4.2 --- Optimal Power Allocation via Conic Programming --- p.115, Chapter 4.2.1 --- Problem Formulation Based on SPEB --- p.115, Chapter 4.2.2 --- Problem Formulation Based on mDPEB --- p.117, Chapter 4.2.3 --- Formulations with QoS Guarantee --- p.120, Chapter 4.3 --- Robust Power Allocation under Imperfect Network Topology Parameters --- p.122, Chapter 4.3.1 --- Robust Counterpart of SPEB Minimization --- p.123, Chapter 4.3.2 --- Robust Counterpart of mDPEB Minimization --- p.131, Chapter 4.4 --- Efficient Robust Algorithm Using Distributed Computations --- p.132, Chapter 4.4.1 --- Algorithm for SPEB Minimization --- p.132, Chapter 4.4.2 --- Algorithm for mDPEB Minimization --- p.136, Chapter 4.5 --- Simulation Results --- p.137, Chapter 4.5.1 --- Power Allocation with Perfect Network Topology Parameters --- p.137, Chapter 4.5.2 --- Robust Power Allocation with Imperfect Network Topology Parameters --- p.140, Chapter 4.6 --- Summary --- p.144, Chapter 5 --- Conclusions and Future Work --- p.145, Chapter 5.1 --- Conclusions --- p.145, Chapter 5.1.1 --- Slow Adaptive OFDMA Systems --- p.146, Chapter 5.1.2 --- Composite Radio MIMO Networks --- p.147, Chapter 5.1.3 --- Energy-Efficient Location-Aware Networks --- p.148, Chapter 5.2 --- Future Work --- p.150, Chapter A --- Bernstein Approximation Theorem --- p.153, Chapter B --- Ergodic MIMO Capacity and Moment Generating Function --- p.155, Bibliography --- p.157, http://library.cuhk.edu.hk/record=b5549457, Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

In this paper a scheme is presented for calculating the land surface energy budget based on the first law of thermodynamics. It takes into account the effects of water multiphase changes and hydrologic process. The enthalpy expression for a heterogeneous land surface is deduced, during which latent heats are assumed to be functions of temperature. Also, a solution is derived for calculating ground temperatures during the water phase transition and the hydrologic process. This study results in a more conservative estimate of the ground energy in comparison with many other land surface schemes.