Resource Allocation, User Association, and User Scheduling for OFDMA-based Cellular Networks

Current advances in wireless communication are driven by an increased demand for more data and bandwidth, mainly due to the development of new mobile platforms and applications. Ever since then the network operators are overwhelmed by the rapid increase in mobile data traffic, which is primarily fueled by the viewing of data-intensive content. In addition, according to the statistics, the ratio of downlink and uplink data traffic demands have changed drastically over the past decade and they are increasingly asymmetric even over small time periods. In recent years, different solutions, based on topological and architectural innovations of the conventional cellular networks, have been proposed to address the issues related to the increasing data requirements and uplink/downlink traffic asymmetries. The most trivial solution is to scale the network capacity through network densification, i.e., by bringing the network nodes closer to each other through efficient spectrum sharing techniques. The resulting dense networks, also known as heterogeneous networks, can address the growing need for capacity, coverage, and uplink/downlink traffic flexibility in wireless networks by deploying numerous low power base stations overlaying the existing macro cellular coverage. However, there is a need to analyze the interplay of different network processes in this context, since, it has not been studied in detail due to complex user dynamics and interference patterns, which are known to present difficulties in their design and performance evaluation under conventional heterogeneous networks. It is expected that by centralizing some of the network processes common to different network nodes in a heterogeneous network, such as coordination between multiple nodes, it will be easier to achieve significant performance gains. In this thesis, we aim at centralizing the control of the underlying network processes through Centralized Radio Access Networks (C-RAN), to deal with the high data requirements along with the asymmetric traffic demands. We analyze both large‐scale centralized solutions and the light‐weight distributed variants to obtain practical insights on how to design and operate future heterogeneous networks

Exploiting Wikipedia Semantics for Computing Word Associations

Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations

Exploiting Wikipedia Semantics for Computing Word Associations

Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations

Exploiting Wikipedia Semantics for Computing Word Associations

Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations.</p

انتظار حسین کی ناول نگاری میں ناسٹیلجیائی عناصر

Nostalgia is Psychiatric propensity that is associated to yearning for the past. For the first time, Johannes Hofer (1669-1752) introduced this word Nostalgia for home sickness especially for "Swiss homesickness". In Urdu literature, echoes of nostalgia is more prominent in fiction mainly in novel-writing like 'Aisi Bulandi Aisi Pasti' by Aziz Ahmad , Aag ka Darya' by Qurat-ul-Ain Haider, 'Angan' by Khadija Mastoor ,and 'Raja Gidh' by Bano Qudsia. During ninteenth century particularly in reference to 'Independence Movement', there is another seasoning of excessive sentimental yearning of past. To face quite different environment after separation develops distinctive feature of nostaligia in urdu literature. Intizar Hussain is widely recognised as a leading literary figure in Urdu, chiefly his three novel 'Basti,(1980), 'Tazkira' (1987). Aage Samandar Ha' (1995). Basti, recived global prize. Nostalgic feelings are more vived in Basti that apparently presents the separation of East Pakistan but it sheds light on tragic circumstances and its miserable results. While his novel Tazkira(1987) is an amalgamation of past, present and future confluence

Fractionation of acacia honey affects its antioxidant potential in vitro

Objective: To investigate the effects of fractionation of acacia honey on its antioxidant potential in contrast with the pure honey from whole blood, brain and liver in vitro. Methods: Honey was partitioned into three fractions (dichloromethane, ethyl acetate and aqueous). Their immuno-modulatory effect on whole blood was assayed using Luminol-amplified chemiluminescence technique. Their antioxidant activities on rat brain and hepatic tissues which covers for catalase, SOD activities and lipid peroxidation. Results: Fractions of the honey enhanced the production of radicals with no significant (P>0.05) antioxidant activity on whole blood where as pure honey does. Pure honey significantly (P0.05) effects on lipid peroxidation. Conclusions: Fractionation of acacia honey negatively affects its antioxidant potential thereby making it a radical generating agent in contrast with the unfractionated