Approved for public release; distribution is unlimited / The identification, selection and employment of better resource allocation models or practices is the aim of this research. As nations seek to employ their resources in a more efficient manner while deriving more effective outputs, those elected to public office must be willing to involve other members of the society in their decision-making. National security is one such area that is in need of a shared vision if it is to achieve the desired results. This paper examines the resource allocation process for national security in Jamaica. The purpose of this study is to establish whether the current process is adequate for addressing this aspect of the country's expenditure, as it impacts upon every citizen and every other area of the nation's affairs. In establishing whether the Jamaican model is adequate, a study of the processes used in three developed countries, namely Canada, the United Kingdom, and the United States is done with a view of identifying the strengths and weaknesses of each process. The understanding of best practices in the field of national security is important, since after all, foreign trade and hence economic prosperity are more likely to be associated with nations that create secure environments. How much to allocate to defense and the consideration of all other viable alternatives is crucial. Only then can the nation look objectively at its unique situation. / Major, Jamaica Defence Force

Since 2004, when the Long Term Evolution (LTE) was first proposed to be publicly available in the year 2009, a plethora of new characteristics, techniques and applications have been constantly enhancing it since its first release, over the past decade. As a result, the research aims for LTE-Advanced (LTE-A) have been released to create a ubiquitous and supportive network for mobile users. The incorporation of heterogeneous networks (HetNets) has been proposed as one of the main enhancements of LTE-A systems over the existing LTE releases, by proposing the deployment of small-cell applications, such as femtocells, to provide more coverage and quality of service (QoS) within the network, whilst also reducing capital expenditure. These principal advantages can be obtained at the cost of new challenges such as inter-cell interference, which occurs when different network applications share the same frequency channel in the network. In this thesis, the main challenges of HetNets in LTE-A platform have been addressed and novel solutions are proposed by using self-organising network (SON) management approaches, which allows the cooperative cellular systems to observe, decide and amend their ongoing operation based on network conditions. The novel SON algorithms are modelled and simulated in OPNET modeler simulation software for the three processes of resource allocation, mobility management and interference coordination in multi-tier macro-femto networks. Different channel allocation methods based on cooperative transmission, frequency reuse and dynamic spectrum access are investigated and a novel SON sub-channel allocation method is proposed based on hybrid fractional frequency reuse (HFFR) scheme to provide dynamic resource allocation between macrocells and femtocells, while avoiding co-tier and cross-tier interference. Mobility management is also addressed as another important issue in HetNets, especially in hand-ins from macrocell to femtocell base stations. The existing research considers a limited number of methods for handover optimisation, such as signal strength and call admission control (CAC) to avoid unnecessary handovers, while our novel SON handover management method implements a comprehensive algorithm that performs sensing process, as well as resource availability and user residence checks to initiate the handover process at the optimal time. In addition to this, the novel femto over macro priority (FoMP) check in this process also gives the femtocell target nodes priority over the congested macrocells in order to improve the QoS at both the network tiers. Inter-cell interference, as the key challenge of HetNets, is also investigated by research on the existing time-domain, frequency-domain and power control methods. A novel SON interference mitigation algorithm is proposed, which is based on enhanced inter-cell interference coordination (eICIC) with power control process. The 3-phase power control algorithm contains signal to interference plus noise ratio (SINR) measurements, channel quality indicator (CQI) mapping and transmission power amendments to avoid the occurrence of interference due to the effects of high transmission power. The results of this research confirm that if heterogeneous systems are backed-up with SON management strategies, not only can improve the network capacity and QoS, but also the new network challenges such as inter-cell interference can also be mitigated in new releases of LTE-A network.

In this thesis, optimization techniques for a joint admission control and radio resource allocation are developed for multicasting over high altitude platforms. First, a primary system model in a multicellular high altitude platform system is considered, in which each user can receive any requested multicast session in its cell from no more than only one HAP antenna simultaneously. All the users have equal priority for admission. The users are selected to join the respective multicast groups and the power, subchannels and time slots are allocated such that the spectrum utilization is maximized while satisfying the quality of service requirements. Lagrangian relaxation and the subgradient algorithm are used to obtain solution bounds for the primary system model problem formulation. These bounds were then used in the branch and bound algorithm for pruning of nodes. The numerical results illustrate the goodness of the bounds for different constraint set dualizations and for different subgradient step size rules. The system model is then extended to allow the multicast group users to receive a session's transmission from more than one antenna simultaneously at different frequencies. This also allows the user to receive multicast sessions transmitted in neighboring cells too, not just those transmitted in the cell which the user resides in. The users have different priority levels of admission and the objective is to maximize the admission of highest priority users to the system. A much efficient formulation is obtained for the extended model in terms of size, as compared to the primary model. Linear outer approximation using McCormick underestimators are used for the relaxation of the mixed binary quadratically constrained problem. The solution method is based on branch and cut scheme in which cutting planes, domain propagation and heuristics are integrated. Various branching schemes are considered and a presolving reformulation linearization scheme for a specific set of quadratic constraints is considered. The numerical experiments compare the performances in terms of the duality gap, number of nodes, number of iterations, the number of iterations per node, the time needed to obtain the first feasible solution and the percentage of instances a feasible solution was found. / October 2016

The goal of this paper is to benchmark Answer Set Programming (ASP) systems to test their performance when dealing with a complex optimization problem. In particular, the problem tackled is resource allocation in the area of Business Process Management (BPM). Like many other scheduling problems, the allocation of resources and starting times to business process activities is a challenging optimization problem for ASP solvers. Our problem encoding is ASP Core-2 standard compliant and it is realized in a declarative and compact fashion. We develop an instance generator that produces problem instances of different size and hardness with respect to adjustable parameters. By using the baseline encoding and the instance generator, we provide a comparison between the two award-winning ASP solvers clasp and wasp and report the grounding performance of gringo and i-dlv. The benchmark suggests that there is room for improvement concerning both the grounders and the solvers. Fostered by the relevance of the problem addressed, of which several variants have been described in different domains, we believe this is a solid application-oriented benchmark for the ASP community. / Series: Working Papers on Information Systems, Information Business and Operations

這篇論文示主要討論在信息不完全情況下的無線網絡資源分配的兩個問題。傳輸節點不固定和信道狀態的不確定性將影響資源分配的選擇。因為不完整的信息，或在這些情況下可能無法確切獲得準確的信息，將對資源優化配置產生一定的影響。對於不完整信息對無線網路的影響，其中用戶的移動性和信道增益的不確定性，將是本論文中討論的兩個主要問題。 / 本論文的第一部分是關於移動傳輸節點的資源分配問題。我們主要分析上行系統的移動用戶，其中每個用戶會盡量優化他或她自己的功效函數，以達到最佳的性能。此外，我們提出對移動用戶的功率分配優化的方案當所有信道信息可用的時候。此外，我們提出了幫助每個用戶預測信道信息總幹擾來優化功效函數，當信息不完備的時候。這形成了一個不完全信息博弈。我們提出了預測的規則，幫助動態預測總幹擾。我們採用了卡爾曼濾波器來處理測量噪聲 我們也說明了利用預測來優化的功效函數和由完整的信息得出的之間的差異。此外，從動態規劃，我們在預測的基礎上給出一個動態的功率分配方案。 / 第二部分討論了在資源分配時，當有關信道增益是不完整時的不確定規劑。我們主要考慮認知無線電模型。在第二部分中，我們考慮，當二級用戶的干擾不會超過一定限制時，他們能夠使用共享的頻率的情況。我們首先利用約束幹擾的限制進行建模，使得二級用戶的干擾，即使在最壞的情況下，也不會超過限制，這將有助於他或她以避免不可行的解決方案。然後，我們擴展我們的概率約束條件來代表不確定性的干擾限制。由於概率約束一般都是難以解決的，而基於無線信道的衰落效應，有關變量的完整的信息是很難獲得。我們重新將概率約束條件轉化為隨機期望約束條件。利用樣本平均近似法，我們提出了隨機學習算法，以幫助次級用戶從主要用戶那裡獲得反饋信息，最大限度地提高自己的功效函數。此外，我們分析了在認知無線電網絡定價條件的頻譜共享方案。我們展示的聯合優化配置方案，幫助次級用戶從主要用戶購買頻譜和優化功率。當有信道增益的不確定性時，二級用戶希望最大限度地提高功效函數的期望值並且採用相對穩定的採購策略追求最佳平均收益。它是一個有鞍點的隨機優化問題。我們展示一個分佈式的隨機算法，以幫助二級用戶更新資源分配策略。在一些實際的情況下，為了減少計算複雜性和希望實施越來較為容易，我們利用迭代平均來為二級用戶進行資源配置。 / Two main issues of resource allocation in wireless networks with incomplete information are addressed in this thesis. Transmission node is not fixed in the wireless system and uncertainties of the channel states would also affect the choices of resource allocation, since full information cannot be provided or may not be exact under these scenarios. For incomplete information in wireless networks, mobility of the users and uncertainties of channel gains are two main issues that would be considered in this thesis. / The first part of this thesis is concerning the resource allocation problems with mobile trans- mission nodes. We consider mobile users in an up-link system. We analyze the mobile system where each user would try to maximize his or her own utility to achieve the best performance. Besides, we propose a power allocation scheme for the mobile users when all channel information is available. We show that our model can form a game. Moreover, we illustrate that each user would expect to predict the aggregate interference to maximize the utility when channel information is incomplete. It can be shown that this forms a game with incomplete information. We demonstrate the prediction rules which help predict the aggregate interference dynamically. We apply the Kalman filter to tackle measurement noises. We also illustrate the bound on the difference between the utility with prediction and that with complete information. Moreover, applying dynamic programming, we give a dynamic power allocation scheme based on the predictions. / The second part discusses the issue of uncertain programming in resource allocation when information about channel gains is incomplete. We mainly consider the model of cognitive radio networks. We introduce a resource allocation scheme for secondary users with spectrum sharing in a cognitive radio network. Secondary users can exploit the spectrum owned by primary links when their interference level does not exceed certain requirements. We first model the interfer- ence constraints as robust constraints such that secondary users would satisfy the interference constraints even under the worst cases, which would help him or her to avoid the unfeasible solutions. We then extend our consideration of the interference constraints as chance constraints to represent uncertainties. Since chance constraints are generally difficult to solve and full in- formation about the uncertain variables is not available due to the fading effects of wireless channels, we reformulate the constraints into stochastic expectation constraints. With sample average approximation method, we propose stochastic distributed learning algorithms to help secondary users satisfy the constraints with the feedback information from primary links when maximizing the utilities. Moreover, we introduce a resource allocation scheme for secondary users to share spectrum and optimize usage of power with pricing. Secondary users need to buy spectrum from primary users. In the process, secondary users also enhance the utilization of the unused bandwidth by primary users. We first demonstrate the resource allocation scheme when full information about channel gains is available. When there are uncertainties of channel gains, secondary users would like to maximize the expected value of the utilities to pursue the best benefits on average with relatively stable buying strategies. It can be shown that it is a stochastic optimization problem with saddle points. We demonstrate a Distributed Stochastic Algorithm to help secondary users update their resource allocation strategies. For some practical scenarios, to reduce computation complexity and make implementation easy, we illustrate an Iterate Average from Distributed Stochastic Algorithm for secondary users. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhou, Kenan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 123-134). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivations --- p.1 / Chapter 1.2 --- Contributions and Outline of the Thesis --- p.2 / Chapter 2 --- Background Study --- p.5 / Chapter 2.1 --- Slow and Flat Fading Wireless Channel --- p.5 / Chapter 2.2 --- Cognitive Radio Networks --- p.7 / Chapter 2.3 --- Multiple-Access Channel --- p.8 / Chapter 2.4 --- Mobility Model --- p.10 / Chapter 2.5 --- Convex Optimization --- p.12 / Chapter 2.6 --- Uncertain Programming --- p.13 / Chapter 2.7 --- Game Theory --- p.14 / Chapter Part I --- Resource Allocation in Wireless Networks with Mobility --- p.16 / Chapter 3 --- Resource Allocation with Mobile Users in an Up-link System --- p.19 / Chapter 3.1 --- System Model --- p.20 / Chapter 3.2 --- Power Allocation for Mobile Users with Complete Information --- p.22 / Chapter 3.3 --- Power Allocation with Incomplete Information --- p.26 / Chapter 3.3.1 --- Bound on the difference between the utility with prediction and that with complete information --- p.27 / Chapter 3.3.2 --- Prediction Scheme for Incomplete Channel Information --- p.30 / Chapter 3.3.3 --- Power Allocation with Dynamic Programming --- p.32 / Chapter 3.4 --- Numerical results and discussion --- p.35 / Chapter 3.4.1 --- Simulation Model --- p.35 / Chapter 3.4.2 --- Numerical Results --- p.36 / Chapter 3.5 --- Chapter Summary --- p.42 / Chapter 3.6 --- Appendices --- p.44 / Chapter 3.6.1 --- Proof of Theorem 3.1 --- p.44 / Chapter 3.6.2 --- Proof of Theorem 3.2 --- p.47 / Chapter Part II --- Resource Allocation inWireless Networks with Uncertain Programming --- p.48 / Chapter 4 --- Resource Allocation with Robust Optimization --- p.52 / Chapter 4.1 --- System Model --- p.52 / Chapter 4.2 --- Resource Allocation with Robust Optimization Approach --- p.54 / Chapter 4.3 --- Trade-Off Between Robustness and Performance --- p.59 / Chapter 4.4 --- Numerical results and discussion --- p.62 / Chapter 4.4.1 --- Choice of the Penalty Function --- p.62 / Chapter 4.4.2 --- Simulation Model --- p.63 / Chapter 4.4.3 --- Simulation Results --- p.64 / Chapter 4.5 --- Chapter Summary --- p.65 / Chapter 5 --- Resource Allocation with Chance Constraints --- p.67 / Chapter 5.1 --- System Model --- p.68 / Chapter 5.2 --- Power Allocation with Complete Information about Probabilistic Constraints --- p.69 / Chapter 5.3 --- A Stochastic Approximation Approach Based on the Outage Event --- p.72 / Chapter 5.3.1 --- Feasibility of the Stochastic Approximation Method --- p.74 / Chapter 5.3.2 --- Stochastic Distributed Learning Algorithm I (SDLA-I) --- p.76 / Chapter 5.3.3 --- Stochastic Distributed Learning Algorithm II (SDLA-II) --- p.80 / Chapter 5.4 --- Numerical Results and Discussion --- p.82 / Chapter 5.4.1 --- Examples of uk(.) for Simulation --- p.82 / Chapter 5.4.2 --- Simulation Model --- p.83 / Chapter 5.4.3 --- Simulation Results and Discussions --- p.84 / Chapter 5.5 --- Chapter Summary --- p.86 / Chapter 5.6 --- Appendices --- p.88 / Chapter 5.6.1 --- Proof of Lemma 5.3 --- p.88 / Chapter 6 --- Priced Resource Allocation with Stochastic Optimization --- p.90 / Chapter 6.1 --- System Model --- p.91 / Chapter 6.2 --- Price-Based Optimization with Complete Information --- p.94 / Chapter 6.3 --- Price-Based Stochastic Optimization with Uncertainties --- p.96 / Chapter 6.4 --- Distributed Stochastic Algorithms for the Price-Based Stochastic Optimization --- p.100 / Chapter 6.4.1 --- Iterate Averages of DSA --- p.104 / Chapter 6.5 --- Numerical Results and Discussion --- p.106 / Chapter 6.5.1 --- Simulation Model --- p.106 / Chapter 6.5.2 --- Numerical Results --- p.107 / Chapter 6.6 --- Chapter Summary --- p.112 / Chapter 6.7 --- Appendices --- p.113 / Chapter 6.7.1 --- Proof of Lemma 6.1 --- p.113 / Chapter 6.7.2 --- Proof of Proposition 6.1 --- p.114 / Chapter 6.7.3 --- Proof of Lemma 6.3 --- p.114 / Chapter 6.7.4 --- Proof of Lemma 6.4 --- p.115 / Chapter 6.7.5 --- Proof of Proposition 6.2 --- p.116 / Chapter 7 --- Conclusion and Future Work --- p.117 / Chapter 7.1 --- Conclusion --- p.117 / Chapter 7.2 --- Future Work --- p.119 / Chapter 7.2.1 --- Joint Power and Channel Access Scheduling for Mobile Users --- p.119 / Chapter 7.2.2 --- Power Control for Heterogeneous Mobile Users --- p.120 / Chapter 7.2.3 --- More on Uncertain Programming in Cognitive Radios --- p.120 / Chapter 7.2.4 --- Transmissions in Complex Networks with Uncertainties --- p.121 / Chapter 7.2.5 --- Secure Transmissions in Wireless Networks with Uncertainties --- p.122 / Bibliography --- p.123

This study examines the role of tertiary education in the development of a knowledge based economy, with particular reference to developing countries and in the particular case of the University of Botswana (UB). It presents evidence to suggest that the commodification of knowledge is important to the fulfilment of this role and that if UB were to embrace this notion, it would help focus the fragmented approach of business practices in the University. The study also asks whether the resource allocation model at UB supports commodification of knowledge and examines what alternative approaches could be adopted, concluding that the adoption of a formulaic resource allocation model would better support this aim. A case study approach was adopted especially for its potential to capture explanatory and descriptive data. The three phases of study used were firstly the documentary research where reports were inspected and classified into categories. The second phase concentrated on the semi-structured interviews with members of the senior management staff at UB that served to clarify, confirm, refute and/or corroborate documentary research outcomes. The third phase of the study combined the data collected from the different sources. In combining both quantitative and qualitative data this thesis took advantage of data complementarity, facilitation and triangulation. The findings of the study show that UB embraces the notion of knowledge based economy through advancing the intellectual and human resource capacity of the nation and the international community. However the definition of commodification was not uniformly understood across UB faculties. The study also finds that UB did not seem to have a resource allocation model but had a budgeting system that did not support commodification of knowledge. While the human resource accounted for 67% of the total budget of the institution there was no staff allocations model to manage it. The inadequate database was found to be a major contributing factor which was exacerbated by lack of clear business processes in certain critical areas. The principles underpinning commodification of knowledge could be traced in UB policies even though there was a need to strengthen systems, processes and quality control mechanisms to facilitate and support data collection and establishment of database. A practical system which has the potential to help establish a systemic approach that would interrogate business process in UB is proposed.

A central concern in economics is explaining the allocation of resources, its consequences for economic activity and the distribution of the associated economic revenues. This dissertation contains three essays examining the average and distributional effects of reallocations resulting from liberalization reforms or trade shocks. Chapter 1 examines the distributional effects of trade liberalization. A vast literature demonstrates that liberalization is associated with higher wage inequality. Nearly the entire literature considers comparative statics or steady states, which ignore dynamics and of necessity feature monotonic changes. I address these limitations by developing a micro-founded model that emphasizes the dynamics of reallocation between heterogeneous firms and workers in the presence of costly labor adjustments. Trade liberalization provides firms both new export markets and new sources of competition. Expanding high-paying firms increase wages to recruit better workers faster. Workers at firms threatened by competition accept wage cuts to delay their employers' exit and keep their job. This provides novel implications for both aggregate and within-firm inequality across a distribution of firm types. I show that key mechanisms of the model are consistent with a wide range of facts, some of which being examined in greater details in chapter 2. Results from the calibrated model suggest an overshooting of inequality on the path to a new steady state. This is consistent with evidence based on an event study of recent liberalization episodes. Inequality appears to peak about six years after liberalization, with one-fourth of the overshooting disappearing in the following ten years. Chapter 2 investigates the effects of firm growth on hiring and separations. I contribute to the literature on worker flows by studying the wages and characteristics of new and separated workers. First, I show that separations are an essential and robust component of firm growth. I argue that this may be the result of a more intense search for better matches at faster growing firms. Second, I find that wage offers to new hires increase with firm hiring rates. This is partly the result of the selection of more experienced workers. However fixed unobservable and variable observable worker characteristics cannot fully explain this relationship: the residual wage of new hires is significantly associated with the firm hiring rate. We interpret this as direct evidence of the firm-level upward-sloping labor supply curve predicted by the canonical models. We provide estimates of the slope of the curve using an instrumental variable approach to control for supply shocks. We find that a 10% increase in the hiring rate results in a wage increase of 1%. In chapter 3 Jaromir Nosal, Jonathan Vogel and I ask the following: What is the contribution of industry reallocation and productivity changes to the economic gains resulting from banking deregulation? How does local industrial structure determine the outcomes of banking deregulation? This chapter uses the staggered reforms of the banking sector in the U.S. between 1977 and 1997 to empirically investigate these questions. In the private sector, we show that the deregulation-induced reallocation of workers was directed towards industries with lower GDP per worker. Moreover, employment gains were associated with a reduction in productivity. Nevertheless we find that these effects are offset by across the board within-industry productivity gains. In addition, total output and aggregate productivity increased because of the reallocation of workers out of unemployment, self-employment and non-private industries towards the more productive private sector. Finally we find that initial industry mix can explain up to one third of the variation in state aggregate responses.

Hui, Tan Hing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 151-162). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Background --- p.7 / Chapter 2.1.1 --- "Tools for Analyzing Vehicles' Speeds, Traffic Flows and Densities" --- p.7 / Chapter 2.1.2 --- Tools for Analyzing Bytes Received by the Vehicles from an AP --- p.9 / Chapter 2.1.3 --- Effort-Fairness vs Outcome-Fairness --- p.10 / Chapter 2.1.4 --- Quantifying Fairness on the Bytes Received by the Vehicles from an AP --- p.10 / Chapter 2.2 --- Delay-Tolerant Networks(DTNs) --- p.12 / Chapter 2.3 --- Drive-thru Internet Systems --- p.14 / Chapter 2.4 --- Resource Allocation/Scheduling in Drive-thru Internet and Related Systems --- p.20 / Chapter 2.4.1 --- Resource Allocation/Scheduling Algorithms for Multiple Vehicles --- p.20 / Chapter 2.4.2 --- Rate Adaptation Algorithms for Fast-varying Channels due to Vehicular Movement/Mobility --- p.29 / Chapter 3 --- Performance Evaluation of Round-robin Scheduler with IEEE 802.11 MAC --- p.33 / Chapter 3.1 --- System Model --- p.34 / Chapter 3.2 --- Description of the Real-life Vehicular Traffic Trace --- p.36 / Chapter 3.2.1 --- Analysis on Hourly Single-lane Traffic Flow of 1-80 Highway --- p.40 / Chapter 3.2.2 --- Analysis on Hourly Directional Traffic Flow of 1-80 Highway --- p.43 / Chapter 3.2.3 --- Analysis on Hourly Single-lane Vehicles' Speeds of 1-80 Highway --- p.45 / Chapter 3.2.4 --- Analysis on Daily Vehicles' Speeds of 1-80 Highway --- p.48 / Chapter 3.2.5 --- "Relationship among Average Traffic Densities, Flows and Vehicles' Speeds in Singlelane Scenarios" --- p.51 / Chapter 3.2.6 --- "Relationship among Average Traffic Densities, Flows and Vehicles' Speeds in Multilane Scenarios" --- p.52 / Chapter 3.3 --- Trace-driven Simulations of Drive-thru Internet Scenarios using Round-robin Scheduler with IEEE 802.11 MAC --- p.54 / Chapter 3.3.1 --- Simulation Setup --- p.54 / Chapter 3.3.2 --- Scenarios of using Fixed Data Rate --- p.57 / Chapter 3.3.3 --- Scenario of using Auto-rate Algorithm --- p.67 / Chapter 4 --- The Design and Implementation of VECADS --- p.73 / Chapter 4.1 --- Towards the Design of an Intelligent Scheduling for Drive-thru Internet --- p.74 / Chapter 4.1.1 --- System Throughput Maximization vs Fairness --- p.74 / Chapter 4.1.2 --- Antenna --- p.75 / Chapter 4.1.3 --- Speed --- p.76 / Chapter 4.1.4 --- Noisy Measurement of Predicting Channel Condition based on RSSI(or Similar Metrics) only --- p.77 / Chapter 4.1.5 --- Region for Serving “Weak´ح Vehicles --- p.78 / Chapter 4.2 --- System Model --- p.79 / Chapter 4.3 --- The Design of VECADS --- p.83 / Chapter 4.3.1 --- Using Vehicular Context to Help Scheduling --- p.83 / Chapter 4.3.2 --- Penalizing Slow Vehicles in the Coverage . --- p.88 / Chapter 4.3.3 --- "Round-Robin Scheduling for ""Weak"" Vehicles in the “Sweet Zone´ح" --- p.90 / Chapter 4.3.4 --- Rate Adaptation Algorithm in VEC ADS --- p.94 / Chapter 4.4 --- The Implementation of VECADS --- p.97 / Chapter 4.4.1 --- Overall System Architecture of VECADS --- p.97 / Chapter 4.4.2 --- Overall Scheduling Flow of VECADS --- p.100 / Chapter 4.4.3 --- Algorithms in VECADS --- p.102 / Chapter 5 --- Performance Evaluation of VECADS --- p.110 / Chapter 5.1 --- Simulation Setup --- p.110 / Chapter 5.2 --- Simulation Results and Discussion --- p.114 / Chapter 5.2.1 --- Evaluation of the Performance Impact of Different System Parameters --- p.114 / Chapter 5.2.2 --- Evaluation of Different Design Options --- p.119 / Chapter 6 --- Conclusions and Discussion --- p.142 / Chapter A --- Average Bytes Received by a Moving Vehicle from a Roadside AP --- p.146 / Chapter B --- Distribution of the Cumulative Bytes Received by Vehicles from a Roadside AP --- p.148 / Bibliography --- p.151

After that, the cooperative transmission scheme is extended for the scenario of more than two source-destination pairs. One objective is to investigate the relationship between the diversity order and the number of source-destination pairs. This is done by considering the sum power minimization problem. A pricing game is derived to provide a distributed implementation. At Nash Equilibrium of the game, the total transmission power is minimized. Simulation results show the rapid convergence of the game and its adaptation to channel fluctuations. It also shows that the cooperative transmission scheme achieves full diversity order. / Apart from replacing a superposition code based cooperative transmission scheme by a TDM based scheme, the implementation can be simplified by introducing a partner selection scheme to the nodes. In that network, the cooperative transmission code still uses superposition code as the building block. Instead of relaying the messages from all other nodes, in this new scheme, the source nodes only relay the messages for their assigned partners. A natural question is: How can we assign the partners to the source nodes such that the total transmission power is minimized. The problem is solved in two phases. Firstly, we solve the sum power minimization problem for each pair of nodes. In some cases, this problem has closed-form solutions while for the other cases, a simple iterative algorithm can solve this problem. / Firstly, cooperative orthogonal-division channel is defined and two cooperative transmission schemes based on dirty-paper coding and superposition code are proposed and compared through simulations. Simulation Results show the significant improvement over the pure direct transmission schemes. Although one cooperative transmission scheme achieves a slightly larger rate region, the other scheme has a much simpler implementation so the remaining parts of the thesis focus on this scheme. The outage performance of this scheme is also compared with a simplified Han-Kobayashi scheme through simulations. Simulation results illustrate the significant improvement in the diversity gain of this scheme over the Han-Kobayashi scheme. / However, it is noted that the complexity of implementing superposition code, which is a building block of the cooperative transmission code, is very high when there are many users in the network. Hence, another time-division multiplex (TDM) based cooperative transmission scheme is proposed. Similar to the superposition code based scheme, there is a pricing game which can provide a distributed sum power minimization. Simulation results also show that the game has high convergence rate and it can adapt to changes of channel conditions efficiently. In addition, this cooperative transmission scheme also achieves full diversity order. / In this thesis, different codes and resource allocation algorithms for cooperative transmissions are proposed. Briefly speaking, in cooperative transmission, a number of wireless nodes form a coalition in which they exchange and cooperatively transmit messages. As a result, the order of diversity can be increased without installing additional antennas. / Next, a weighted sum rate maximization algorithm is proposed. There are two purposes of this algorithm. Firstly, this algorithm is adopted to find the Pareto-optimal points of the boundary of the achievable rate region through simulations. Secondly, this algorithm can be extended to solve the max-min fairness problem and the joint utility maximization algorithm by the proposed framework. / This thesis is ended with some future research directions. / With this information, we can assign the partners by Gabow's algorithm, which solves the maximum weighted matching problem that is mapped from the original partner selection problem. Nonetheless, it is noted that when the number of users is very large, it involves a large amount of the communication and computational cost to solve the sum power minimization problem for each pair of nodes as well as the partner selection problem. Therefore, the Grouping Algorithm is proposed to reduce the aforementioned implementation cost. Simulation results show that the optimal algorithm and the Grouping Algorithm can achieve full diversity order. Moreover, although the Grouping Algorithm is sub-optimal in general, it costs only 1dB of the sum power more than the optimal algorithm. / Ng, Cho Yiu. / Adviser: Tal M. Lok. / Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 152-162). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Lin, Xingqin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 103-109). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Background --- p.3 / Chapter 1.2.1 --- Wireless Communication Schemes --- p.3 / Chapter 1.2.2 --- Mathematical Preliminaries --- p.8 / Chapter 1.3 --- Outline of the Thesis --- p.12 / Chapter 2 --- Learning for Parallel Gaussian Interference Channels --- p.14 / Chapter 2.1 --- System Model and Problem Formulation --- p.16 / Chapter 2.2 --- Stochastic Algorithm for Learning --- p.18 / Chapter 2.2.1 --- Algorithm Design --- p.18 / Chapter 2.2.2 --- Convergence Analysis --- p.21 / Chapter 2.3 --- Continuous Time Approximation --- p.26 / Chapter 2.4 --- Learning with Averaging --- p.28 / Chapter 2.5 --- Numerical Results --- p.29 / Chapter 3 --- Power Control for One-to-Many Transmissions --- p.34 / Chapter 3.1 --- System Model --- p.35 / Chapter 3.2 --- A GNEP Approach --- p.38 / Chapter 3.2.1 --- Problem Formulation --- p.38 / Chapter 3.2.2 --- Preliminary Results --- p.39 / Chapter 3.3 --- Algorithm Design --- p.42 / Chapter 3.4 --- Numerical Results --- p.46 / Chapter 4 --- Flow Allocation in Multiple Access Networks --- p.50 / Chapter 4.1 --- System Model and Problem Formulation --- p.52 / Chapter 4.1.1 --- System Model --- p.52 / Chapter 4.1.2 --- Problem Formulation --- p.53 / Chapter 4.2 --- Characterization of NE --- p.57 / Chapter 4.2.1 --- Feasibility Assumption --- p.57 / Chapter 4.2.2 --- Existence and Uniqueness of NE --- p.58 / Chapter 4.3 --- Distributed Algorithms Design --- p.60 / Chapter 4.3.1 --- D-SBRA --- p.60 / Chapter 4.3.2 --- P-SBRA --- p.61 / Chapter 4.3.3 --- Best Response and Layered Structure --- p.65 / Chapter 4.4 --- Performance Evaluation --- p.67 / Chapter 4.4.1 --- Protocol Evaluation --- p.67 / Chapter 4.4.2 --- Convergence and Performance --- p.69 / Chapter 4.4.3 --- Flow Distribution --- p.71 / Chapter 4.4.4 --- A Grid Network Simulation --- p.73 / Chapter 5 --- Relay Assignment in Cooperative Networks --- p.76 / Chapter 5.1 --- System Model and Problem Formulation --- p.77 / Chapter 5.1.1 --- Three-Node Relay Model --- p.77 / Chapter 5.1.2 --- Network Model --- p.78 / Chapter 5.1.3 --- Problem Formulation --- p.78 / Chapter 5.2 --- Centralized Scheme --- p.80 / Chapter 5.2.1 --- Generalized Relay Assignment --- p.80 / Chapter 5.2.2 --- Admission Control --- p.83 / Chapter 5.2.3 --- Iteration Algorithm and Some Remarks --- p.84 / Chapter 5.3 --- A Simple Distributed Algorithm --- p.84 / Chapter 5.4 --- Numerical Results --- p.86 / Chapter 6 --- Conclusions and Future Work --- p.88 / Chapter 6.1 --- Conclusions --- p.88 / Chapter 6.2 --- Future Work --- p.89 / Chapter A --- Proof of Theorem 21 --- p.93 / Chapter B --- Proof of Theorem 22 --- p.96 / Chapter C --- Proof of Proposition 31 --- p.98 / Chapter D --- Proof of Proposition 44 --- p.101 / Bibliography --- p.103