1,484 research outputs found
Conflicting patterns of thought in the Russian debate on transition: 2003-2007
No full textThis article is a continuation of two essays by the same author on Soviet/Russian economic debates between 1987 and 2002 published in Europe-Asia Studies in 2006 and 2007, so now the series of articles covers 20 years of Soviet/Russian discussions on economic reforms. Should Russia strive to become a 'Western' country marked by democracy and a market economy serving the individual interests of its citizens, or was it more important to become a great power again? Are Western patterns of political and economic life suitable for Russia or is the attempt to import foreign institutional structures doomed for failure, making it necessary for Russia to find her own way? This type of question, going far beyond the realm of economics, was and still is at the heart of the debate among Russian economists, which shall be discussed here, on the basis of a qualitative content analysis of the most important economic journals and selected monographs. --
O-RAN for Energy-Efficient Serving Cluster Formulation in User-Centric Cell-Free MMIMO
No full textThe 6G Massive Multiple-Input Multiple-Output (MMIMO) networks can follow the
so-called User-Centric Cell-Free (UCCF) architecture, where a single user is
served by multiple Access Points (APs) coordinated by the Central Processing
Unit (CPU). In this paper, we propose how O-RAN functionalities, i.e.,
rApp-xApp pair, can be used for energy-efficient Serving Cluster Formulation
(SCF). Simulation studies show up to 37\% gain in Energy Efficiency (EE) of the
proposed solution over the state-of-the-art Network-Centric (NC) designs.Comment: Accepted for presentation during The 2nd Workshop on Next-generation
Open and Programmable Radio Access Networks (NG-OPERA), organized in
conjunction with IEEE International Conference on Computer Communications,
May 20, 202
A PRISMA review of research on music practice
No full textWe employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to systematically review research on music practice from 1928 until June 2020 and identified a total of 3,102 records using our inclusion criteria, of which a total of 296 were eventually selected for the final analysis. We tabulated percentages and frequencies of (a) publications in ten-year periods, (b) type of publications, (c) sampling by geographical location, (d) methodologies used, (e) the top tenth percentile of the most highly cited research, and (f) topics covered. Our analyses reveal that particularly strong growth occurred in the literature between 2000 and 2020. In the literature we retrieved, the most commonly sampled research participants were those in the United States, followed by the United Kingdom and Australia. Quantitative research designs were most prevalent, accounting for two thirds of all studies reviewed (66.2%), with questionnaires and recordings being the most common methods of data collection. Non-empirical papers (17.5%) as well as studies incorporating qualitative (13.5%) and mixed-methods designs (3.1%) were much less prevalent. Ericsson et al.’s (1993) seminal study of deliberate practice, Driskell et al.’s (1994) review of the research literature on mental practice, and Sloboda et al.’s (1996) study of young musicians were by far the most often cited. Overall, the most common topics addressed were deliberate practice, practice strategies, mental practice, the benefits of practice, metacognition, self-regulation, and self-efficacy, suggesting that music practice is a rich, multi-faceted, and complex activity. In light of the findings, recommendations for practice and implications for future research are provided.Accepted versio
How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
No full textAs the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics
How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
No full textAs the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics
A game theoretic approach for optimizing density of remote radio heads in user centric cloud-based radio access network
No full textIn this paper, we develop a game theoretic formulation for empowering cloud enabled HetNets with adaptive Self Organizing Network (SON) capabilities. SON capabilities for intelligent and efficient radio resource management is a fundamental design pillar for the emerging 5G cellular networks. The C-RAN system model investigated in this paper consists of ultra-dense remote radio heads (RRHs) overlaid by central baseband units that can be collocated with much less densely deployed overlaying macro base-stations (BSs). It has been recently demonstrated that under a user centric scheduling mechanism, C-RAN inherently manifests the trade-off between Energy Efficiency (EE) and Spectral Efficiency (SE) in terms of RRH density. The key objective of the game theoretic framework developed in this paper is to dynamically optimize the trade-off between the EE and the SE of the C- RAN. More specifically, for an ultra-dense C- RAN based HetNet, the density of active RRHs should be carefully dimensioned to maximize the SE. However, the density of RRHs which maximizes the SE may not necessarily be optimal in terms of the EE. In order to strike a balance between these two performance determinants, we develop a game theoretic formulation by employing a Nash bargaining framework. The two metrics of interest, SE and EE, are modeled as virtual players in a bargaining problem and the Nash bargaining solution for RRH density is determined. In the light of the optimization outcome we evaluate corresponding key performance indicators through numerical results. These results offer insights for a C-RAN designer on how to optimally design a SON mechanism to achieve a desired trade-off level between the SE and the EE in a dynamic fashion
The Union of Regeneration: the Anti-Bolshevik Underground in Revolutionary Russia, 1917-1919
No full textPhDThe Union of Regeneration has been chosen as the main focal point of this thesis, a
study of underground political organisations in revolutionary Russia who came about
as a result of fragmentation of Russia's major political parties in 1917 and sought to
oppose the Bolshevik takeover of power. The thesis traces the origins of the
underground in the political turmoil of 1917 before detailing how each group was
formed, and how a number of plans were made, most of which hinged on the
extensive involvement of Allied interventionist forces, to form an anti-Bolshevik and
anti-German front in the wake of the signature of the Treaty of Brest-Litovsk. The
efforts of the Union of Regeneration, the National Centre, and other groups such as
the Union for the Defence of the Fatherland and Freedom are presented as a series of
failures which took place mostly in 1918. By examining the reasons for each of these
failures, this thesis hopes to focus not on external factors, such as the lack of Allied
intervention to assist the underground groups or the machinations of reactionary
forces against them, in order to reveal the fundamental failings of the underground
movement as a whole. The underground lacked any organisational discipline or
coherence, its ranks were easily entered on a loose, `personal' basis and there was
little unity of purpose between its members, save the removal of Soviet power.
Consequently, plans made were too vague, agreements were too easily broken, and
alliances were too easily ruptured. This thesis, then, hopes to demonstrate that
although when considered together the anti-Bolshevik underground constituted a
genuine potential threat to the Bolshevik regime, that it failed to act as one
contributed greatly to it being easily marginalised by the extremes of left and right
Entrepreneurship education and entrepreneurial career option among polytechnic students in Northwestern Nigeria: the mediating effect of creativity
No full textThe study's primary objective is to examine the mediating effect of creativity on the relationship between components of entrepreneurship education (EE) and entrepreneurial career option (ECO) among polytechnic students in northwestern Nigeria. Data were collected from polytechnics through cross-sectional study design. The study adopted multistage stratified sampling to select six polytechnics and used proportionate random sampling to select 505 respondents, and questionnaires were self-administered. 348 usable responses were gathered to assess 11 direct and 5 indirect hypotheses and Partial Least Squares Structural Equation Modelling (PLSSEM) was used in the hypotheses testing. This study found that know-what (KWT), know-how (KHW), know-who (KWO), know-why (KWY), know-when (KWN), and creativity (CRT) are essential EE components of EE objectives that influence students to engage in ECO in northwestern Nigeria. The findings revealed that KWT, KHW, KWY, and KWN depends on the CRT disposition of the students. It is expected KWO and KWN would increase students' ECO, but the findings of this study ran contrary to this expectation, as the relationship between KWO, KWN and ECO was not significant. Interestingly, the findings further showed that creativity significantly mediate the relationship between KWT, KWO, KWY, KWN and ECO, but not the relationship between KHW and ECO. Higher educational institutions (HEIs) should emphasise KWT, KWO, KWY, and KWN, but it is imperative to note that overemphasis on KHW may result in lower creativity. The results of this study provides significant insights to EE stakeholders and researchers to recognise that the EE, CRT, and ECO relationships need to be examined further. The current study contributes mainly to the current literature on how CRT mediates the relationship between all of the components of EE and ECO, especially in the Nigerian context where such studies are sparse. The study recommends strategies and practical road map for effective delivery of EE courses in line with global best practice. Lastly, limitations of the current study and avenues for future research were discussed
ML-Based Optimization of Large-Scale Systems: Case Study in Smart Microgrids and 5G RAN
No full textThe recent advances in machine learning (ML) have brought revolutionary changes to every field. Many novel applications, such as face recognition and natural language processing, have demonstrated the great potential of ML techniques. Indeed, ML can significantly enhance the intelligence of many existing systems, including smart grid, wireless communications, mechanical engineering, and so on. For instance, microgrid (MG), a distribution-level power system, can exchange energy with the main grid or work in islanded mode, which enables higher flexibility for the smart grid. However, it suffers considerable management complexity by including multiple entities such as renewable energy resources, energy storage system (ESS), loads, etc. In addition, each entity may have unique observations and policies to make autonomous decisions.
Similarly, 5G networks are designed to provide lower latency, higher throughput and
reliability for a large number of user devices, but the evolving network architecture also leads to great complexity for network management. The 5G network management should jointly consider various user types and network resources in a dynamic wireless environment. In addition, the integration of new techniques, such as reconfigurable intelligent surfaces (RISs), requires more efficient algorithms for network optimization. Consequently, intelligent management schemes are crucial to schedule network resources. In this work, we aim to develop state-of-the-art ML techniques to improve the performance of large-scale systems. As case studies, we focus on MG energy management and 5G radio access network (RAN) management. Multi-agent reinforcement learning (MARL) is presumed to be an ideal solution for MG energy management by considering each entity as an independent agent. We further investigate how communication failures will affect MG energy trading by using Bayesian deep reinforcement learning (BA-DRL). On the 5G side, we use MARL, transfer reinforcement learning (TRL), and hierarchical reinforcement learning (HRL) to improve network performance. In particular, we study the performance of those algorithms under various scenarios, including radio resource allocation for network
slicing, joint radio and computation resource for mobile edge computing (MEC), joint radio and cache resource allocation for edge caching. Additionally, we further investigate how HRL can improve the energy efficiency (EE) of RIS-aided heterogeneous networks.
The findings of this research highlight the capabilities of various ML techniques under
different application domains. Firstly, different MG entities can be well coordinated by
applying MARL, enabling intelligent decision-making for each agent. Secondly, Bayesian theory can be used to solve partially observable Markov decision process (POMDP) problems caused by communication failures in MARL. Thirdly, MARL is capable of balancing the heterogeneous requirements of different slices in 5G networks, guaranteeing satisfactory overall network performance. Then, we find that TRL can significantly improve the convergence performance of conventional reinforcement learning or deep reinforcement learning by transferring the knowledge from experts to learners, which is demonstrated over a 5G network slicing case study. Finally, we find that long-term and short-term decisions are well coordinated by HRL, and the proposed cooperative hierarchical architecture achieves higher throughput and EE than conventional algorithms
NOVEL USER-CENTRIC ARCHITECTURES FOR FUTURE GENERATION CELLULAR NETWORKS: DESIGN, ANALYSIS AND PERFORMANCE OPTIMIZATION
No full textAmbitious targets for aggregate throughput, energy efficiency (EE) and ubiquitous user experience are propelling the advent of ultra-dense networks. Inter-cell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address this challenge, we investigate the idea of transforming network design from being base station-centric to user-centric. To this end, we develop mathematical framework and analyze multiple variants of the user-centric networks, with the help of advanced scientific tools such as stochastic geometry, game theory, optimization theory and deep neural networks. We first present a user-centric radio access network (RAN) design and then propose novel base station association mechanisms by forming virtual dedicated cells around users scheduled for downlink. The design question that arises is what should the ideal size of the dedicated regions around scheduled users be? To answer this question, we follow a stochastic geometry based approach to quantify the area spectral efficiency (ASE) and energy efficiency (EE) of a user-centric Cloud RAN architecture. Observing that the two efficiency metrics have conflicting optimal user-centric cell sizes, we propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. The designed model is shown to outperform base-station centric design in terms of both ASE and EE in dense deployment scenarios. Taking this user-centric approach as a baseline, we improve the ASE and EE performance by introducing flexibility in the dimensions of the user-centric regions as a function of data requirement for each device. So instead of optimizing the network-wide ASE or EE, each user device competes for a user-centric region based on its data requirements. This competition is modeled via an evolutionary game and a Vickrey-Clarke-Groves auction. The data requirement based flexibility in the user-centric RAN architecture not only improves the ASE and EE, but also reduces the scheduling wait time per user.
Offloading dense user hotspots to low range mmWave cells promises to meet the enhance mobile broadband requirement of 5G and beyond. To investigate how the three key enablers; i.e. user-centric virtual cell design, ultra-dense deployments and mmWave communication; are integrated in a multi-tier Stienen geometry based user-centric architecture. Taking into account the characteristics of mmWave propagation channel such as blockage and fading, we develop a statistical framework for deriving the coverage probability of an arbitrary user equipment scheduled within the proposed architecture. A key advantage observed through this architecture is significant reduction in the scheduling latency as compared to the baseline user-centric model. Furthermore, the interplay between certain system design parameters was found to orchestrate the ASE-EE tradeoff within the proposed network design. We extend this work by framing a stochastic optimization problem over the design parameters for a Pareto optimal ASE-EE tradeoff with random placements of mobile users, macro base stations and mmWave cells within the network. To solve this optimization problem, we follow a deep learning approach to estimate optimal design parameters in real-time complexity. Our results show that if the deep learning model is trained with sufficient data and tuned appropriately, it yields near-optimal performance while eliminating the issue of long processing times needed for system-wide optimization.
The contributions of this dissertation have the potential to cause a paradigm shift from the reactive cell-centric network design to an agile user-centric design that enables real-time optimization capabilities, ubiquitous user experience, higher system capacity and improved network-wide energy efficiency
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