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Scientific Study from the year 2016 in the subject Engineering - Communication Technology, Mahalingam College of Engineering and Technology, language: English, abstract: The future Wireless Communication Systems (WCS) are supposed to provide high data rate to support personal and multimedia communications irrespective of the users' mobility and location. These services include heterogeneous classes of traffics such as voice, file transfer, web browsing, wireless multimedia, teleconferencing, and interactive games. In recent years, data and multimedia services have become important in wireless communications. As a result, bandwidth requirement and number of users become delicate problems. To support high data rate requirement for future WCS, it is essential to efficiently allocate the limited resources. The major challenges are the dynamic nature of wireless channel, limited resources such as power, frequency spectrum, and diversified Quality of Service (QoS) requirements. Orthogonal Frequency Division Multiplexing (OFDM) is a special case of multicarrier transmission that supports high data rate operation. OFDM is a modulation and multiplexing technique appropriate for current and future wireless networks. OFDM divides the available bandwidth into a number of parallel independent orthogonal subchannels and their bandwidth is much less than the coherence bandwidth of the channel. The wide band frequency selective fading channel is converted into several narrow band flat fading channels. OFDM is an excellent method to overcome multipath fading effects. One of the goals of WCS is to enhance the capacity of the channel. Multiple Access Technique (MAT) permits several mobile users to share the given bandwidth in an effective way. Basically there are four multiple access techniques available namely, Time Division Multiple Access (TDMA), Frequency Division Multiple access (FDMA), Code Division Multiple Access (CDMA) and Space Division Multiple Access (SDMA). MAT is employ
This book presents state-of-the-art research on robust resource allocation in current and future wireless networks. The authors describe the nominal resource allocation problems in wireless networks and explain why introducing robustness in such networks is desirable. Then, depending on the objectives of the problem, namely maximizing the social utility or the per-user utility, cooperative or competitive approaches are explained and their corresponding robust problems are considered in detail. For each approach, the costs and benefits of robust schemes are discussed and the algorithms for reducing their costs and improving their benefits are presented. Considering the fact that such problems are inherently non-convex and intractable, a taxonomy of different relaxation techniques is presented, and applications of such techniques are shown via several examples throughout the book. Finally, the authors argue that resource allocation continues to be an important issue in future wireless networks, and propose specific problems for future research.
A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
In June 2000, GTEL (Wireless Telecommunications Research Group) at the F- eral University of Ceara ́ was founded by Professor Rodrigo Cavalcanti and his c- leagues with the mission of developing wireless communications technology and impact the development of the Brazilian telecommunications sector. From the start, this research effort has been supported by Ericsson Research providing a dynamic environment where academia and industry together can address timely and relevant research challenges. This book summarized much of the research output that has resulted from GTEL’s efforts. It provides a comprehensive treatment of the physical and multiple access layers in mobile communication systems describing different generations of systems but with a focus on 3G systems. The team of Professor C- alcanti has contributed scienti cally to the development of this eld and built up an impressive expertise. In the chapters that follow, they share their views and kno- edge on the underlying principles and technical trade-offs when designing the air interface of 3G systems. The complexity of 3G systems and the interaction between the physical and m- tiple access layers present a tremendous challenge when modeling, designing, and analyzing the mobile communication system. Herein, the authors tackle this pr- lem in an impressive manner. Their work is very much in line with the developments in 3GPP providing a deeper understanding of the evolution of 3G and also future enhancements.
The purpose of this book is to provide tools for a better understanding of the fundamental tradeo?s and interdependencies in wireless networks, with the goal of designing resource allocation strategies that exploit these int- dependencies to achieve signi?cant performance gains. Two facts prompted us to write it: First, future wireless applications will require a fundamental understanding of the design principles and control mechanisms in wireless networks. Second, the complexity of the network problems simply precludes the use of engineering common sense alone to identify good solutions, and so mathematics becomes the key avenue to cope with central technical problems in the design of wireless networks. In this book, two ?elds of mathematics play a central role: Perron-Frobenius theory for non-negative matrices and optimization theory. This book is a revised and expanded version of the research monograph “Resource Allocation in Wireless Networks” that was published as Lecture Notes in Computer Sciences (LNCS 4000) in 2006. Although the general structure has remained unchanged to a large extent, the book contains - merous additional results and more detailed discussion. For instance, there is a more extensive treatment of general nonnegative matrices and interf- ence functions that are described by an axiomatic model. Additional material on max-min fairness, proportional fairness, utility-based power control with QoS (quality of service) support and stochastic power control has been added.
Merging the fundamental principles of resource allocation with the state-of-the-art in research and application examples, Han and Liu present a novel and comprehensive perspective for improving wireless systems performance. Cross-layer multiuser optimization in wireless networks is described systematically. Starting from the basic principles, such as power control and multiple access, coverage moves to the optimization techniques for resource allocation, including formulation and analysis, and game theory. Advanced topics such as dynamic resource allocation and resource allocation in antenna array processing, and in cooperative, sensor, personal area, and ultrawideband networks, are then discussed. Unique in its scope, timeliness, and innovative author insights, this invaluable work will help graduate students and researchers to understand the basics of wireless resource allocation whilst highlighting modern research topics, and will help industrial engineers to improve system optimization.
Tackling problems from the least complicated to the most, Resource Allocation in Uplink OFDMA Wireless Systems provides readers with a comprehensive look at resource allocation and scheduling techniques (for both single and multi-cell deployments) in uplink OFDMA wireless networks relying on convex optimization and game theory to thoroughly analyze performance. Inside, readers will find topics and discussions on: Formulating and solving the uplink ergodic sum-rate maximization problem Proposing suboptimal algorithms that achieve a close performance to the optimal case at a considerably reduced complexity and lead to fairness when the appropriate utility is used Investigating the performance and extensions of the proposed suboptimal algorithms in a distributed base station scenario Studying distributed resource allocation where users take part in the scheduling process, and considering scenarios with and without user collaboration Formulating the sum-rate maximization problem in a multi-cell scenario, and proposing efficient centralized and distributed algorithms for intercell interference mitigation Discussing the applicability of the proposed techniques to state-of-the-art wireless technologies, LTE and WiMAX, and proposing relevant extensions Along with schematics and figures featuring simulation results, Resource Allocation in Uplink OFDMA Wireless Systems is a valuable book for?wireless communications and cellular systems professionals and students.