On optimization of the resource allocation in multi-cell networks.

Chen, Jieying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 58-62).Abstract in English only.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Literature Review --- p.5Chapter 1.3 --- Contributions Of This Thesis --- p.7Chapter 1.4 --- Structure Of This Thesis --- p.8Chapter 2 --- Problem Formulation --- p.9Chapter 2.1 --- The JBAPC Problem --- p.9Chapter 2.2 --- The Single-Stage Reformulation --- p.12Chapter 3 --- The BARN Algorithm --- p.15Chapter 3.1 --- Preliminary Mathematics --- p.15Chapter 3.1.1 --- Duality Of The Linear Optimization Problem --- p.15Chapter 3.1.2 --- Benders Decomposition --- p.18Chapter 3.2 --- Solving The JBAPC Problem Using BARN Algorithm --- p.21Chapter 3.3 --- Performance And Convergence --- p.24Chapter 3.3.1 --- Global Convergence --- p.26Chapter 3.3.2 --- BARN With Error Tolerance --- p.26Chapter 3.3.3 --- Trade-off Between Performance And Convergence Time --- p.26Chapter 4 --- Accelerating BARN --- p.30Chapter 4.1 --- The Relaxed Master Problem --- p.30Chapter 4.2 --- The Feasibility Pump Method --- p.32Chapter 4.3 --- A-BARN Algorithm For Solving The JBAPC Problem --- p.34Chapter 5 --- Computational Results --- p.36Chapter 5.1 --- Global Optimality And Convergence --- p.36Chapter 5.2 --- Average Convergence Time --- p.37Chapter 5.3 --- Trade-off Between Performance And Convergence Time --- p.38Chapter 5.4 --- Average Algorithm Performance Of BARN and A-BARN --- p.39Chapter 6 --- Discussions --- p.47Chapter 6.1 --- Resource Allocation In The Uplink Multi-cell Networks --- p.47Chapter 6.2 --- JBAPC Problem In The Uplink Multi-cell Networks --- p.48Chapter 7 --- Conclusion --- p.50Chapter 7.1 --- Conclusion Of This Thesis --- p.50Chapter 7.2 --- Future Work --- p.51Chapter A --- The Proof --- p.52Chapter A.l --- Proof of Lemma 1 --- p.52Chapter A.2 --- Proof of Lemma 3 --- p.55Bibliography --- p.5

Models for Budget Constrained Auctions: An Application to Sponsored Search & Other Auctions

The last decade has seen the emergence of auction mechanisms for pricing and allocating goods on the Internet. A successful application area for auctions has been sponsored search. Search firms like Google, Bing and Yahoo have shown stellar revenue growths due to their ability to run large number of auctions in a computationally efficient manner. The online advertisement market in the U.S. is estimated to be around $41 billion in 2010 and expected to grow to$50 billion by 2011 (http://www.marketingcharts.com/interactive/us-online-advertising-market-to-reach-50b-in-2011-3128/). The paid search component is estimated to account for nearly 50% of online advertising spend. This dissertation considers two problems in the sponsored search auction domain. In sponsored search, the search operator solves a multi-unit allocation and pricing problem with the specified bidder values and budgets. The advertisers, on the other hand, regularly solve a bid determination problem for the different keywords, given their budget and other business constraints. We develop a model for the auctioneer that allows the bidders to place differing bids for different advertisement slots for any keyword combination. Despite the increased complexity, our model is solved in polynomial time. Next, we develop a column-generation procedure for large advertisers to bid optimally in the sponsored search auctions. Our focus is on solving large-scale versions of the problem. Multi-unit auctions have also found a number of applications in other areas that include supply chain coordination, wireless spectrum allocation and transportation. Current research in the multi-unit auction domain ignores the budget constraint faced by participants. We address the computational issues faced by the auctioneer when dealing with budget constraints in a multi-unit auction. We propose an optimization model and solution approach to ensure that the allocation and prices are in the core. We develop an algorithm to determine an allocation and Walrasian equilibrium prices (when they exist) under additive bidder valuations where the auctioneer's goal is social welfare maximization and extend the approach to address general package auctions. We, also, demonstrate the applicability of the Benders decomposition technique to model and solve the revenue maximization problem from an auctioneer's standpoint

Distributed and dynamic traffic congestion controls without requiring demand forecasting: Tradable network permits and its implementation mechanisms

Tohoku University赤松隆課

A GNN based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time-consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN) based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems