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3527 results in Wireless Communications

Page 35 of 177

  • 6 - Probability and random processes

  • Samuel O. Agbo, California Polytechnic State University, Matthew N. O. Sadiku, Prairie View A & M University, Texas
  • Book:
    Principles of Modern Communication Systems
    Published online:
    01 February 2019
    Print publication:
    06 February 2017, pp 270-322

  • Summary

    Nothing moves without a mover.

    ISAAC NEWTON

    TECHNICAL NOTES - Ethics

    The electrical engineering curriculum in most accredited engineering schools in this country is so crowded that there is no room for courses such as engineering ethics, economics, or law. Part of the problem is that most engineering professors are not prepared to introduce engineering ethics into their classrooms.

    The days when an engineer's only ethical commitment was loyalty to his or her employer have long passed. Society in general tends to hold the engineering profession to an elevated standard and expects practicing engineers to perform on a higher ethical plane. Society holds engineers accountable for their actions. And for engineers, the implications are inescapable.

    As members of the engineering profession, engineers are expected to exhibit the highest standards of honesty and integrity. Engineering has a direct and vital impact on the quality of life for all people. Engineers must perform under a standard of professional behavior that requires adherence to the highest principles of ethical conduct. The ability to discern right from wrong in cases of apparent ethical dilemma is important. With ethics, there is frequently no absolute right answer, just a personal best answer. Ethics poses dilemmas, which force hard moral choices and cause us to deal with values.

    Each professional engineering society has some form of ethical code. Here is a combination of some.

    • Accept personal responsibility consistent with the safety, health, and welfare of the public.

    • Conduct themselves responsibly, ethically, and lawfully so as to enhance the honor, reputation, and usefulness of the profession.

    • Be guided in all relations by the highest standards of honesty and integrity.

    • Reject bribery in all its forms.

    • Perform professional services only in areas of your competence.

    • Conform with state registration laws in the practice of engineering.

    • Treat fairly all colleagues and co-workers regardless of their race, religion, gender, age, or national origin.

    INTRODUCTION

    So far in this book, we have been dealing with deterministic signals, i.e. signals which can be expressed explicitly so that they can be determined at all times. However, most of the signals we deal with in practice are random (unpredictable or erratic) and not deterministic. Random signals are encountered in one form or another in every practical communication system.

  • B - MATLAB

  • from Appendices
  • Samuel O. Agbo, California Polytechnic State University, Matthew N. O. Sadiku, Prairie View A & M University, Texas
  • Book:
    Principles of Modern Communication Systems
    Published online:
    01 February 2019
    Print publication:
    06 February 2017, pp 407-420

  • Summary

    MATLAB has become a powerful tool of technical professionals worldwide. The term MATLAB is an abbreviation for MATrix LABoratory, implying that MATLAB is a computational tool that employes matrices and vectors/arrays to carry out numerical analysis, signal processing, and scientific visualization tasks. Because MATLAB uses matrices as its fundamental building blocks, one can write mathematical expressions involving matrices just as easily as one would on paper. MATLAB is available for Macintosh, Unix, and Windows operating systems. A student version of MATLAB is available for PCs. A copy of MATLAB can be obtained from:

    The Mathworks, Inc.

    3 Apple Hill Drive

    Natick, MA 01760–2098, USA

    Phone:(508) 647–7000

    Website: http://www.mathworks.com

    A brief introduction on MATLAB is presented in this Appendix. What is presented is sufficient for solving problems in this book. Other information on MATLAB required in this book is provided on a chapter-by-chapter basis as needed. Additional information about MATLAB can be found in MATLAB books and from online help. The best way to learn MATLAB is to work with it after one has learned the basics.

    MATLAB FUNDAMENTALS

    The Command window is the primary area where you interact with MATLAB. A little later, we will learn how to use the text editor to create M-files, which allow one to execute sequences of commands. For now, we focus on how to work in the Command window. We will first learn how to use MATLAB as a calculator. We do so by using the algebraic operators in Table B.1.

    To begin to use MATLAB, we use these operators. Type commands at the MATLAB prompt “>>” in the Command window (correct any mistakes by backspacing) and press the <Enter> key. For example,

    The first command assigns the values 2, 4, and −6 to the variables a, b, and c respectively. MATLAB does not respond because this line ends with a semicolon. The second command sets dat to b 2 − 4ac and MATLAB returns the answer as 64. Finally, the third line sets e equal to the square root of dat and divides by 10. MATLAB prints the answer as 0.8. The function sqrt is used here; other mathematical functions listed in Table B.2 can be used.

  • Preface

  • Samuel O. Agbo, California Polytechnic State University, Matthew N. O. Sadiku, Prairie View A & M University, Texas
  • Book:
    Principles of Modern Communication Systems
    Published online:
    01 February 2019
    Print publication:
    06 February 2017, pp xiii-xiv

  • Summary

    We live in the information age – news, weather, sports, shopping, financial, business inventory, and other sources make information available to us almost instantly via communication systems. The field of communication is perhaps the fastest growing area in electrical engineering. This is why a course on communication systems is an important part of most engineering curricula.

    Most books on communication systems are designed for a two-semester course sequence. Unfortunately, electrical engineering has grown considerably, and its curriculum is so crowded that there is no room for a two-semester course on communication systems. This book is designed for a three-hour semester course on communication systems. It is intended as a textbook for senior-level students in electrical and computer engineering. The prerequisites for a course based on this book are standard engineering mathematics (including calculus and differential equations), electronics, and electric circuit analysis.

    This book is intended to present communication systems to electrical and computer engineering students in a manner that is clearer, more interesting, and easier to understand than other texts. This objective is achieved in the following ways:

  • • We have included several features to help students feel at home with the subject. Each chapter opens with a historical profile or technical note. This is followed by an introduction that links the chapter with the previous chapters and states the chapter objectives. The chapter ends with a summary of key points and formulas.

  • • All principles are presented in a lucid, logical, step-by-step manner. As much as possible, we avoid wordiness and giving too much detail that could hide concepts and impede overall understanding of the material.

  • • Important formulas are boxed as a means of helping students sort out what is essential from what is not. Also, to ensure that students clearly get the gist of the matter, key terms are defined and highlighted.

  • • Thoroughly worked examples are liberally given at the end of every section. The examples are regarded as a part of the text and are clearly explained without asking the reader to fill in missing steps. Thoroughly worked examples give students a good understanding of the material and the confidence to solve problems themselves.

  • 9 - Adaptive Compression in C-RANs

  • from Part II - Physical-Layer Design in C-RANs
  • Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
  • Book:
    Cloud Radio Access Networks
    Published online:
    23 February 2017
    Print publication:
    02 February 2017, pp 200-224

  • Summary

    Cloud radio access networks (C-RANs) provide a promising architecture for the future mobile networks needed to sustain the exponential growth of the data rate. In C-RAN, one data processing center or baseband unit communicates with users through distributed remote radio heads, which are connected to the baseband unit (BBU) via high-capacity low-latency so-called fronthaul links. The architecture of C-RAN, however, imposes a burden of fronthaul bandwidth because raw I/Q samples are exchanged between the RRHs and the BBU. Therefore, signal compression is required on fronthaul links owing to their limited capacity. This chapter exploits the advance of joint signal processing to reduce the transmission rate on fronthaul uplinks. In particular, we first propose a joint decompression and detection (JDD) algorithm which exploits the correlation among RRHs and jointly performs decompressing and detecting. The JDD algorithm takes into consideration both fading and quantization effects in a single decoding step. Second, the block error rate of the JDD algorithm is analyzed in a closed form by using pairwise error probability analysis under both deterministic and Rayleigh fading channel models. Third, on the basis of the analyzed block error rate (BLER), we introduce adaptive compression schemes subject to quality of service constraints to minimize the fronthaul transmission rate while satisfying the predefined target QoS. The premise of the proposed compression methods originates from practical scenarios, where most applications tolerate a non-zero BLER. As a dual problem, we also develop a scheme to minimize the signal distortion subject to the fronthaul rate constraint. We finally consider the counterparts of these two adaptive compression schemes for Rayleigh-fading channels and analyze their asymptotic behavior as the constraints approach extremes.

    Introduction

    Cloud radio access networks have been widely accepted as a new architecture for future mobile networks to sustain the ever increasing demand in the data rate [1]. In a C-RAN, one centralized processor or BBU communicates with users distributed in a graphical area via a number of remote radio heads (RRHs), which act as “soft” relaying nodes and are connected to the BBU via high-capacity and low-latency fronthaul links. By moving all baseband processing functions from RRHs to a centralized processor, the C-RAN enables adaptive load balancing via a virtual base station pool [2] and effective network-wide inter-cell interference management thanks to multi-cell processing [3, 4].

  • 19 - Field Trials and Testbed Design for C-RAN

  • from Part IV - Networking in C-RANs
  • Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
  • Book:
    Cloud Radio Access Networks
    Published online:
    23 February 2017
    Print publication:
    02 February 2017, pp 451-471

  • Summary

    Introduction

    Since the proposal of C-RAN [1-3] in 2009, China Mobile (CMCC) has been committed to developing various kinds of proof-of-concept (PoC), test-beds, and field trials to demonstrate C-RAN's benefits and verify the key enabling technologies. This chapter gives a comprehensive introduction to these activities. In particular, we will demonstrate not only the feasibility and reliability of wavelength-division-multiplexing (WDM)-based fronthaul (FH) solutions but also how a noticeable coordinated multiple-points (CoMP) gain can be achieved with the C-RAN architecture. In addition, a virtualized C-RAN system is elaborated, including the design principles, the architecture, and the field trial results.

    Field-Trial Verification of FH Solutions

    19.2.1 Centralization Field Trials in 2G and 3G Networks

    The first step toward C-RAN was baseband unit (BBU) centralization which is relatively easy to implement and can be tested with the existing 2G, 3G, and 4G systems. In the past few years, extensive field trials have been carried out in more than 10 cities in China using commercial 2G, 3G, and pre-commercial TD-LTE networks with different centralization scales. The main objective of C-RAN deployment in 2G and 3G is to demonstrate the deployment benefits of centralization, including accelerated site construction and reduced power consumption. For example, one trial took place in the city of Changchun where 506 2G BSs in five counties were upgraded to a C-RAN-type architecture centralized in several sites. In the largest of these, 21 BSs were aggregated to support 101 RRUs with a total of 312 carriers. It was observed that power consumption was reduced by 41% owing to shared air-conditioning. In addition, system performance in terms of the call-drop rate as well as the downlink data rate was enhanced using multiple RRU-co-cell technologies. For the results and benefits from using centralization in 2G and 3G trials, the reader is referred to [4]. When it comes to TD-LTE, centralization becomes more challenging owing to the high data rate in the FH connection. For example, the data rate of the most widely used FH interface in the industry, the common public radio interface (CPRI), could be as high as 9.8 Gb/s for an TD-LTE carrier with a 20 MHz bandwidth and eight antennas.

  • 3 - The Tradeoff of Computational Complexity and Achievable Rates in C-RANs

  • from Part II - Physical-Layer Design in C-RANs
  • Edited by Tony Q. S. Quek, Singapore University of Technology and Design, Mugen Peng, Osvaldo Simeone, New Jersey Institute of Technology, Wei Yu, University of Toronto
  • Book:
    Cloud Radio Access Networks
    Published online:
    23 February 2017
    Print publication:
    02 February 2017, pp 35-53

  • Summary

    Introduction

    The trend of increased centralization holds the potential to transform mobile networks in two ways. First, centralization enables the exploitation of common channel knowledge, which in turn allows for significant improvements in the performance of a communication channel by, for instance, performing the joint transmission and reception of signals or allocating resources jointly amongst adjacent cells [1]. Second, centralized processing leverages the trend towards deploying mobile networks on low-cost commodity hardware that is running commodity or open-source software solutions. Deploying software-based implementations increases implementation flexibility, reduces service-creation time, and enables the flexible usage of processing resources through virtualization. In this chapter we use the term Cloud-RAN (C-RAN) to refer to a flexible use of commodity solutions that combines gains in both the telecommunication and information technology domains.

    Before implementing the protocol stack of a RAN on a cloud-computing platform, we must also take the required effort into account, e.g., commodity hardware is considered to be less performant and energy efficient than dedicated hardware such as ASIC, DSP, or FPGA. Furthermore, resource virtualization implies an overbooking of resources while satisfying joint resource requirements of all processed base stations (BSs), which is in contrast with fulfilling individual processing constraints at each BS. Centralized signal processing may further impose stringent requirements on the fronthaul network between a radio access point (RAP) and the data center.

    So far, research in the area of Cloud-RAN has focused on the telecommunication domain, e.g., the applicability of joint processing approaches, gains from centralization, and optimal degrees of centralization under different side constraints. In this chapter the focus is on the impact of limiting and virtualizing the data processing resources on the communication rate, i.e., the quantitative coupling of the required computational resources and communication rates [2]. After introducing basic notation and definitions, we consider metrics and an analytical framework that allows one to determine the data processing demand Interestingly, the data processing requirements depend not only on the number of information bits but also to a large extent on the quality of a user's communication channel. In this chapter we discuss and quantify multi-user gains, which lower the requirements on the data processing resources to be provided.

Page 35 of 177