This thesis examines the case study of Jericho as an example of the unique challenges of intra-Palestinian water allocation. Over the past hundred years, Jericho has been under the control of five ruling governments: Ottoman, British, Jordanian, Israeli and Palestinian. This study begins with an investigation of local water allocation under foreign control. Throughout each period of rule, legislation about water was inherently connected with land control, and Jericho’s history as an agricultural city dictated how water was classified. Despite many of the nominal changes in law from one government to the next, local practice changed relatively little, as the community allocated resources in a fairly consistent way among community members. Jericho’s sustained level of agriculture has been possible because of the consistently high output of a large spring, Ein Sultan, just north of the contemporary city. The second chapter examines the transition from Israeli to Palestinian control of Jericho in 1994, which is now considered an Area A zone in the West Bank, and examines the relationship of nascent Palestinian water institutions with previous informal networks. The last section addresses the challenges facing Jericho today, referencing and analyzing the recently written Master Plan for Jericho’s water system undertaken by a Palestinian nongovernmental organization. The Plan effectively highlights problems within the system of allocation, including: poor water quality, inefficient domestic and irrigation networks, conspicuous local consumption, ineffective pricing systems, and lack of wastewater treatment. However, the plan does not provide long-term suggestions to address the underlying systematic problems with the allocation system. Although Jericho is theoretically a Palestinian controlled municipality, it faces serious obstacles to effective governance of its resources. The informal institutions dominated by the agricultural sector that sustained the community for such a long time, may not be able to adjust in the face of necessary water reform for the city. The local government may need to consider politically unpopular decisions, reform tariffs, and decrease reliance upon foreign aid if it hopes to continue maintain and manage Ein Sultan and other water sources for the growing city into the future. / text

Unlike traditional cellular networks, where voice calls dominate the network traffic, modern mobile traffic is created by of a mixture of both voice and broadband data services. The heterogeneous mixture of voice and data services in mobile broadband networks includes voice calls, web browsing, file transfers, video streaming, and social media applications. Consequently, network planning and radio resource management strategies must be aware of the quality of experience perceived by the users using various types of applications. In this report, we explore the traffic characteristics, scheduling and resource allocation strategies, and user experience models in mobile broadband networks. / text

The focus of this dissertation is the task of resource allocation in multi- server systems arising from two applications – multi-channel wireless com- munication networks and large-scale content delivery networks. The unifying theme behind all the problems studied in this dissertation is the large-scale nature of the underlying networks, which necessitate the design of algorithms which are simple/greedy and therefore scalable, and yet, have good perfor- mance guarantees. For the multi-channel multi-hop wireless communication networks we consider, the goal is to design scalable routing and scheduling policies which stabilize the system and perform well from a queue-length and end-to-end delay perspective. We first focus on relay assisted downlink networks where it is well understood that the BackPressure algorithm is stabilizing, but, its delay performance can be poor. We propose an alternative algorithm - an iterative MaxWeight algorithm and show that it stabilizes the system and outperforms the BackPressure algorithm. Next, we focus on wireless networks which serve mobile users via a wide-area base-station and multiple densely deployed short- range access nodes (e.g., small cells). We show that traditional algorithms that forward each packet at most once, either to a single access node or a mobile user, do not have good delay performance and propose an algorithm (a distributed scheduler - DIST) and show that it can stabilize the system and performs well from a queue-length/delay perspective. In content delivery networks, each arriving job can only be served by servers storing the requested content piece. Motivated by this, we consider two settings. In the first setting, each job, on arrival, reveals a deadline and a subset of servers that can serve it and the goal is to maximize the fraction of jobs that are served before their deadlines. We propose an online load balanc- ing algorithm which uses correlated randomness and prove its optimality. In the second setting, we study content placement in a content delivery network where a large number of servers, serve a correspondingly large volume of con- tent requests arriving according to an unknown stochastic process. The main takeaway from our results for this setting is that separating the estimation of demands and the subsequent use of the estimations to design optimal content placement policies (learn-and-optimize approach) is suboptimal. In addition, we study two simple adaptive content replication policies and show that they outperform all learning-based static storage policies. / text

This dissertation consists of three chapters. Chapter one estimates the capital adjustment costs at the plant level in a model entry and exit. We find that the estimated variance of plant-specific productivity shock is larger than obtained from balanced panel estimation. Estimation using the unbalanced panel generates a larger irreversibility cost, a smaller disruption cost, and a smaller convex cost, all compared with the estimates by Cooper and Haltiwanger (2006). In chapter two, we study how much of the aggregate productivity changes can be accounted for by the capital reallocation. We also study the impact of capital reallocation on the productivity dispersion across firms. We find that the capital reallocations accounts for roughly 12 percent of the labor productivity and capital productivity are reduced as the reallocation activity increases. When the economy-wide technology has a positive change, the reallocation increases temporarily then drops to its original level. After a short transition, the economy settles down with an increased labor productivity. Chapter three further studies the quantitative role of allocation, entry and exit in the growth of aggregate productivity. We find that, without including in the model the forces that drive the entry and exit changes, the model economy has a modest increase in the aggregate productivity as a result of decrease in the fixed reallocation cost.

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / In this work we evaluated the performance of different water filling algorithms. We have selected four power allocation algorithms: Conventional water-filling (CWF), Constant power water-filling, Inverse Water-filling (IWF), and Adaptive Iterative Water-Filling (AIWF) algorithms. Capacity is the performance metric we used to compare the above algorithms by taking the optimality of transmission power allocation to each sub-channel into account. The power allocation can be calculated with a reference of the water level value that has different approaches for different algorithms. The water level can either be fixed once it is found, or it may be adaptive or different for different sub-channels. Hence, the results show that the adaptive iterative water filling (AIWF) algorithm has a better effect on the performance of MIMO-OFDM system by allocating power adaptively.

M. Tech. Electrical Engineering / The radio frequency (RF) spectrum is a natural resource used by wireless network operators to provide radio communication and transmission systems. The availability of RF spectrum to various wireless network operators using large bandwidth and more than one channel, as demanded by their offered services, is challenging, due to RF spectrum scarcity and lack of optimal channel selection. On the other hand, the under-utilisation of licensed spectrum by some primary users' (PU's) networks may be improved by the use of dynamic spectrum access (DSA) techniques. Recent research has shown that a large portion of the assigned spectrum is used irregularly, leading to under-utilisation and waste of valuable frequency resources. The notion of cognitive radio (CR) was introduced to increase the effectiveness and efficiency of spectrum consumption. In a cognitive radio network (CRN), each secondary user (SU) is expected to select the best RF spectrum band for opportunistic use when the PUs have temporarily vacated the spectrum allocated to them. The literature has proposed many spectrum selection methods for selecting vacant spectra in CRNs. However, most of these methods do not consider the channel usage pattern over time by PUs and also do not adequately consider the effect that frequent channel-switching might have on the quality of service requirements of the SUs and the throughput of the CR system. Thus, the proposed scheme, which is heuristic-based spectrum selection scheme for minimal channel switching (HBSSS-MCS) and heuristic-based spectrum selection scheme for maximum throughput (HBSSS-MT) addresses these issues. The main study objectives were as follows: 1) Minimise the frequent switching of channels using HBSS-MCS; 2) Maximise the throughput of the CR system using HBSSS-MT. To achieve the objectives of this research work, the following sub-problems were addressed: 1) To investigate the spectrum selection schemes in cognitive radio network (CRN); 2) To design a learning mechanism to facilitate the selection of a suitable channel in the primary network.

The use of relay nodes to improve the performance of broadband wireless access (BWA) networks has been the subject of intense research activities in recent years. Relay enhanced BWA networks are anticipated to support multimedia traffic (i.e., voice, video, and data traffic). In order to guarantee service to network users, efficient resource distribution is imperative. Wireless multihop networks are characterized by two inherent dynamic characteristics: 1) the existence of wireless interference and 2) mobility of user nodes. Both mobility and interference greatly influence the ability of users to obtain the necessary resources for service. In this dissertation we conduct a comprehensive research study on the topic of resource allocation in the presence of interference and mobility. Specifically, this dissertation investigates the impact interference and mobility have on various aspects of resource allocation, ranging from fairness to spectrum utilization. We study four important resource allocation algorithms for relay enhanced BWA networks. The problems and our research achievements are briefly outlined as follows. First, we propose an interference aware rate adaptive subcarrier and power allocation algorithm using maximum multicommodity flow optimization. We consider the impact of the wireless interference constraints using Signal to Interference Noise Ratio (SINR). We exploit spatial reuse to allocate subcarriers in the network and show that an intelligent reuse of resources can improve throughput while mitigating the impact of interference. We provide a sub-optimal heuristic to solve the rate adaptive resource allocation problem. We demonstrate that aggressive spatial reuse and fine tuned-interference modeling garner advantages in terms of throughput, end-to-end delay and power distribution. Second, we investigate the benefits of decoupled optimization of interference aware routing and scheduling using SINR and spatial reuse to improve the overall achievable throughput. We model the routing optimization problem as a linear program using maximum concurrent flows. We develop an optimization formulation to schedule the link traffic such that interference is mitigated and time slots are reused appropriately based on spatial TDMA (STDMA). The scheduling problem is shown to be NP-hard and is solved using the column generation technique. We compare our formulations to conventional counterparts in the literature and show that our approach guarantees higher throughput by mitigating the effect of interference effectively. Third, we investigate the problem of multipath flow routing and fair bandwidth allocation under interference constraints for multihop wireless networks. We first develop a novel isotonic routing metric, RI3M, considering the influence of interflow and intraflow interference. Second, in order to ensure QoS, an interference-aware max-min fair bandwidth allocation algorithm, LMX:M3F, is proposed where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors while considering constraints of interference on the flows. We compare with various interference based routing metrics and interference aware bandwidth allocation algorithms established in the literature to show that RI3M and LMX:M3F succeed in improving network performance in terms of delay, packet loss ratio and bandwidth usage. Lastly, we develop a user mobility prediction model using the Hidden Markov Model(HMM) in which prediction control is transferred to the various fixed relay nodes in the network. Given the HMM prediction model, we develop a routing protocol which uses the location information of the mobile user to determine the interference level on links in its surrounding neighborhood. We use SINR as the routing metric to calculate the interference on a specific link (link cost). We minimize the total cost of routing as a cost function of SINR while guaranteeing that the load on each link does not exceed its capacity. The routing protocol is formulated and solved as a minimum cost flow optimization problem. We compare our SINR based routing algorithm with conventional counterparts in the literature and show that our algorithm reinforces routing paths with high link quality and low latency, therefore improving overall system throughput. The research solutions obtained in this dissertation improve the service reliability and QoS assurance of emerging BWA networks.

With a fixed number of terminals and a fixed system spectrum, it can be shown that increasing the number of BSs in a cellular network results in a better network performance. However, a regular organized deployment of a cellular network with small cells is both prohibitively expensive and complicated. A possible solution is to allow the network to grow in an organic fashion. This calls for self-configurable approaches for resource allocation which not only improve the performance but also decrease the network planning as well as maintenance costs. Over the past few years, a particular type of small BSs known as femto-BSs has attracted tremendous attention from the industry and academia alike. Femto BSs operate on the licensed spectrum allocated to the cellular network. They are small, inexpensive and transmit at low power levels. Although the deployment of these small BSs result in significant improvements in the indoor coverage for both voice and data services, they only satisfy the immediate needs of the present cellular networks — namely the improvement of the indoor coverage. They do not provide a fundamental solution for a scalable architecture which can organically grow and adapt to the short and long term changes in the network. In this thesis, we propose a practical unified framework for the system design and resource allocation of cellular networks with various types of base stations (with different power budgets) deployed in an irregular fashion. We refer to these networks as autonomous cellular networks. To this end, we first introduce an adaptive localized approach to resource allocation in traditional cellular networks. We show that localized resource assignment at the cost of partial loss of orthogonality at the cell edges can provide significant gains in the network performance. In the second part of the thesis, we propose an adaptive self-configurable resource allocation framework for cellular networks. A network clustering technique is proposed which enables coordinated resource allocation in a network with irregular deployment of BSs. The performance of the proposed framework is evaluated with the regular as well as the irregular deployment of BSs.

With a fixed number of terminals and a fixed system spectrum, it can be shown that increasing the number of BSs in a cellular network results in a better network performance. However, a regular organized deployment of a cellular network with small cells is both prohibitively expensive and complicated. A possible solution is to allow the network to grow in an organic fashion. This calls for self-configurable approaches for resource allocation which not only improve the performance but also decrease the network planning as well as maintenance costs. Over the past few years, a particular type of small BSs known as femto-BSs has attracted tremendous attention from the industry and academia alike. Femto BSs operate on the licensed spectrum allocated to the cellular network. They are small, inexpensive and transmit at low power levels. Although the deployment of these small BSs result in significant improvements in the indoor coverage for both voice and data services, they only satisfy the immediate needs of the present cellular networks — namely the improvement of the indoor coverage. They do not provide a fundamental solution for a scalable architecture which can organically grow and adapt to the short and long term changes in the network. In this thesis, we propose a practical unified framework for the system design and resource allocation of cellular networks with various types of base stations (with different power budgets) deployed in an irregular fashion. We refer to these networks as autonomous cellular networks. To this end, we first introduce an adaptive localized approach to resource allocation in traditional cellular networks. We show that localized resource assignment at the cost of partial loss of orthogonality at the cell edges can provide significant gains in the network performance. In the second part of the thesis, we propose an adaptive self-configurable resource allocation framework for cellular networks. A network clustering technique is proposed which enables coordinated resource allocation in a network with irregular deployment of BSs. The performance of the proposed framework is evaluated with the regular as well as the irregular deployment of BSs.

We introduce a new across-peer rate allocation algorithm with successive refinement to improve the video transmission performance in P2P networks, based on the combination of multiple description coding and network coding. Successive refinement is implemented through layered multiple description codes. The algorithm is developed to maximize the expected video quality at the receivers by partitioning video bitstream into different descriptions depending on different bandwidth conditions of each peer. Adaptive rate partition adjustment is applied to ensure the real reflection of the packet drop rate in the network. Also the granularity is changed to the scale of atomic blocks instead of stream rates in prior works. Through simulation results we show that the algorithm outperforms prior algorithms in terms of video playback quality at the peer ends, and helps the system adjust better to the peer dynamics.