Resource Allocation And Mimo For 4g And Beyond

This book will be a comprehensive collection of advanced concepts related to 4th generation wireless communication systems. It will be divided into two main parts: resource allocation and transceiver architectures. These two research areas are at the core of the recent advances experimented by wireless communication systems. Each chapter will cover a relevant, timely, topic with two focuses: a first part which is of tutorial and survey nature, reviews the state of the art in that topic, followed by a more deep treatment including current research topics, case studies and performance analysis.

This book comprises select proceedings of the International Conference on VLSI, Communication and Signal processing (VCAS 2018). It looks at latest research findings in VLSI design and applications. The book covers a wide range of topics in electronics and communication engineering, especially in the area of microelectronics and VLSI design, communication systems and networks, and image and signal processing. The contents of this book will be useful to researchers and professionals alike.

MIMO Processing for 4G and Beyond: Fundamentals and Evolution offers a cutting-edge look at multiple-input multiple-output (MIMO) signal processing, namely its detection (in both time and frequency domains) and precoding. It examines its integration with OFDM, UWB, and CDMA, along with the impact of these combinations at the system level. Massive M

Massive MIMO (multiple-input multiple-output) is considered as an heir of the multi-user MIMO technology and it has gained lots of attention from both academia and industry since the last decade. By equipping base stations (BSs) with hundreds of antennas in a compact array or a distributed manner, this new technology can provide very large multiplexing gains by serving many users on the same time-frequency resources and thereby bring significant improvements in spectral efficiency (SE) and energy efficiency (EE) over the current wireless networks. The transmit power, pilot training, and spatial transmission resources need to be allocated properly to the users to achieve the highest possible performance. This is called resource allocation and can be formulated as design utility optimization problems. If the resource allocation in Massive MIMO is optimized, the technology can handle the exponential growth in both wireless data traffic and number of wireless devices, which cannot be done by the current cellular network technology. In this thesis, we focus on the five different resource allocation aspects in Massive MIMO communications: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. More specifically, we consider a network topology with no cell boundary where the BSs can collaborate to serve the users in the considered coverage area. We focus on a downlink (DL) scenario in which each BS transmits different data signals to each user. This scenario does not require phase synchronization between BSs and therefore has the same backhaul requirements as conventional Massive MIMO systems, where each user is preassigned to only one BS. The scenario where all BSs are phase synchronized to send the same data is also included for comparison. We solve a total transmit power minimization problem in order to observe how much power Massive MIMO BSs consume to provide the requested quality of service (QoS) of each user. A max-min fairness optimization is also solved to provide every user with the same maximum QoS regardless of the propagation conditions. The second part of the thesis considers a joint pilot design and uplink (UL) power control problem in multi-cell Massive MIMO. The main motivation for this work is that the pilot assignment and pilot power allocation is momentous in Massive MIMO since the BSs are supposed to construct linear detection and precoding vectors from the channel estimates. Pilot contamination between pilot-sharing users leads to more interference during data transmission. The pilot design is more difficult if the pilot signals are reused frequently in space, as in Massive MIMO, which leads to greater pilot contamination effects. Related works have only studied either the pilot assignment or the pilot power control, but not the joint optimization. Furthermore, the pilot assignment is usually formulated as a combinatorial problem leading to prohibitive computational complexity. Therefore, in the second part of this thesis, a new pilot design is proposed to overcome such challenges by treating the pilot signals as continuous optimization variables. We use those pilot signals to solve different max-min fairness optimization problems with either ideal hardware or hardware impairments. The third part of this thesis studies a two-layer decoding method that mitigates inter-cell interference in multi-cell Massive MIMO systems. In layer one, each BS estimates the channels to intra-cell users and uses the estimates for local decoding within the cell. This is followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An UL achievable SE expression is computed for arbitrary two-layer decoding schemes, while a closed form expression is obtained for correlated Rayleigh fading channels, maximum-ratio combining (MRC), and largescale fading decoding (LSFD) in the second layer. We formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since the problem is non-convex, we develop an algorithm based on the weighted minimum mean square error (MMSE) approach to obtain a stationary point with low computational complexity. Motivated by recent successes of deep learning in predicting the solution to an optimization problem with low runtime, the fourth part of this thesis investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization. Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardware consumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a nonconvex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.

Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints

EVOLVING NETWORKING TECHNOLOGIES This book discusses in a practical manner some of the critical security challenges facing the ever-evolving networking technologies of today. In an age of explosive worldwide growth of electronic data storage and communications, effective protection of information has become a critical requirement, especially when used in coordination with other tools for information security and cryptography in all of its applications, including data confidentiality, data integrity, and user authentication. While the importance of cryptographic technique, i.e., encryption, in protecting sensitive and critical information and resources cannot be overemphasized, an examination of the technical evolution within several industries reveals an approaching precipice of scientific change. The glacially paced but inevitable convergence of quantum mechanics, nanotechnology, computer science, and applied mathematics will revolutionize modern technology. The implications of such changes will be far-reaching, with one of its greatest impacts affecting information security and, more specifically, modern cryptography. The book takes the reader through these issues. As the security systems design becomes more and more complex to meet these challenges, a mistake that is committed most often by security specialists is not making a comprehensive analysis of the system to be secured before choosing which security mechanism to deploy. Often, the security mechanism chosen turns out to be either incompatible with, or inadequate for, handling the complexities of the system. In addition, the book also discusses three main points: Configuration management is a critical issue, and as networks are increasing in size, their configuration needs to be managed. Devices may conflict with each other in terms of configuration. Therefore, it becomes challenging for firewalls to be up-to-date according to network policies. Scalability of the network is another big challenge, it would be easier to address if the network stays the same, but the network is ever expanding with a constant increase in the number of devices devoted to the network. Vendor lock-in: Business decisions that are taken today are revolving around the assumptions and capabilities of the current vendor and environment scenario. Buying the best solutions from today’s vendors involves how to interoperate, integrate, and support multiple solutions. It may involve tearing out all of the longstanding kits without tearing down the entire network at the same time. Audience This book specifically appeals to industry practitioners, IT researchers, and students regarding network technological management.

By 2020, if not before, mobile computing and wireless systems are expected to enter the fifth generation (5G), which promises evolutionary if not revolutionary services. What those advanced services will look like, sound like, and feel like is the theme of the book Advances in Mobile Computing and Communications: Perspectives and Emerging Trends in 5G Networks. The book explores futuristic and compelling ideas in latest developments of communication and networking aspects of 5G. As such, it serves as an excellent guide for advanced developers, communication network scientists, researchers, academicians, and graduate students. The authors address computing models, communication architecture, and protocols based on 3G, LTE, LTE-A, 4G, and beyond. Topics include advances in 4G, radio propagation and channel modeling aspects of 4G networks, limited feedback for 4G, and game theory application for power control and subcarrier allocation in OFDMA cellular networks. Additionally, the book covers millimeter-wave technology for 5G networks, multicellular heterogeneous networks, and energy-efficient mobile wireless network operations for 4G and beyond using HetNets. Finally, the authors delve into opportunistic multiconnect networks with P2P WiFi and cellular providers and video streaming over wireless channels for 4G and beyond.

A guide to the physical and mathematical-statistical approaches to personal and mobile wireless communication networks Wireless Networks Technologies offers an authoritative account of several current and modern wireless networks and the corresponding novel technologies and techniques. The text explores the main aspects of the "physical layer" of the technology. The authors—noted experts on the topic—examine the well-known networks (from 2-G to 3-G) in a historical perspective. They also illuminate the "physical layer" of networks while presenting polarization diversity analysis and positioning of any subscriber located in areas of service both for land-to-land and land-to-atmosphere communication links. The book includes clear descriptions of planning techniques for different integrated femto/pico/micro/macrocell deployments. The authors also examine new technologies of time and frequency dispersy and multiple-input and multiple-output (MIMO) modern network design in space and time domains. In addition, the text contains a discussion of a MIMO network based on multi-beam adaptive antennas. This important book: Provides an examination of current and modern wireless networks Describes various techniques of signal data capacity and spectral efficiency based on the universal stochastic approach Explains how usage of MIMO systems with adaptive multi-beam antennas increase the grade of service and quality of service of modern networks beyond 4-G Provides comparative analysis of depolarization effects and the corresponding path loss factor for rural, mixed residential, suburban, and urban land areas Written for students and instructors as well as designers and engineers of wireless communications systems, Wireless Networks Technologies offers a combination of physical and mathematical-statistical approaches to predict operational parameters of land-to-land and land-to-atmosphere personal and mobile wireless communication networks.