Enhanced Orthogonal Frequency-Division Multiplexing
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Performance Comparisons of MIMO Techniques with Application to WCDMA Systems
EURASIP Journal on Applied Signal Processing 2004:5, 649–661 c 2004 Hindawi Publishing Corporation Performance Comparisons of MIMO Techniques with Application to WCDMA Systems Chuxiang Li Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Xiaodong Wang Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Received 11 December 2002; Revised 1 August 2003 Multiple-input multiple-output (MIMO) communication techniques have received great attention and gained significant devel- opment in recent years. In this paper, we analyze and compare the performances of different MIMO techniques. In particular, we compare the performance of three MIMO methods, namely, BLAST, STBC, and linear precoding/decoding. We provide both an analytical performance analysis in terms of the average receiver SNR and simulation results in terms of the BER. Moreover, the applications of MIMO techniques in WCDMA systems are also considered in this study. Specifically, a subspace tracking algo- rithm and a quantized feedback scheme are introduced into the system to simplify implementation of the beamforming scheme. It is seen that the BLAST scheme can achieve the best performance in the high data rate transmission scenario; the beamforming scheme has better performance than the STBC strategies in the diversity transmission scenario; and the beamforming scheme can be effectively realized in WCDMA systems employing the subspace tracking and the quantized feedback approach. Keywords and phrases: BLAST, space-time block coding, linear precoding/decoding, subspace tracking, WCDMA. 1. INTRODUCTION ing power and/or rate over multiple transmit antennas, with partially or perfectly known channel state information [7]. -
UNIT V- SPREAD SPECTRUM MODULATION Introduction
UNIT V- SPREAD SPECTRUM MODULATION Introduction: Initially developed for military applications during II world war, that was less sensitive to intentional interference or jamming by third parties. Spread spectrum technology has blossomed into one of the fundamental building blocks in current and next-generation wireless systems. Problem of radio transmission Narrow band can be wiped out due to interference. To disrupt the communication, the adversary needs to do two things, (a) to detect that a transmission is taking place and (b) to transmit a jamming signal which is designed to confuse the receiver. Solution A spread spectrum system is therefore designed to make these tasks as difficult as possible. Firstly, the transmitted signal should be difficult to detect by an adversary/jammer, i.e., the signal should have a low probability of intercept (LPI). Secondly, the signal should be difficult to disturb with a jamming signal, i.e., the transmitted signal should possess an anti-jamming (AJ) property Remedy spread the narrow band signal into a broad band to protect against interference In a digital communication system the primary resources are Bandwidth and Power. The study of digital communication system deals with efficient utilization of these two resources, but there are situations where it is necessary to sacrifice their efficient utilization in order to meet certain other design objectives. For example to provide a form of secure communication (i.e. the transmitted signal is not easily detected or recognized by unwanted listeners) the bandwidth of the transmitted signal is increased in excess of the minimum bandwidth necessary to transmit it. -
CS647: Advanced Topics in Wireless Networks Basics
CS647: Advanced Topics in Wireless Networks Basics of Wireless Transmission Part II Drs. Baruch Awerbuch & Amitabh Mishra Computer Science Department Johns Hopkins University CS 647 2.1 Antenna Gain For a circular reflector antenna G = η ( π D / λ )2 η = net efficiency (depends on the electric field distribution over the antenna aperture, losses such as ohmic heating , typically 0.55) D = diameter, thus, G = η (π D f /c )2, c = λ f (c is speed of light) Example: Antenna with diameter = 2 m, frequency = 6 GHz, wavelength = 0.05 m G = 39.4 dB Frequency = 14 GHz, same diameter, wavelength = 0.021 m G = 46.9 dB * Higher the frequency, higher the gain for the same size antenna CS 647 2.2 Path Loss (Free-space) Definition of path loss LP : Pt LP = , Pr Path Loss in Free-space: 2 2 Lf =(4π d/λ) = (4π f cd/c ) LPF (dB) = 32.45+ 20log10 fc (MHz) + 20log10 d(km), where fc is the carrier frequency This shows greater the fc, more is the loss. CS 647 2.3 Example of Path Loss (Free-space) Path Loss in Free-space 130 120 fc=150MHz (dB) f f =200MHz 110 c f =400MHz 100 c fc=800MHz 90 fc=1000MHz 80 Path Loss L fc=1500MHz 70 0 5 10 15 20 25 30 Distance d (km) CS 647 2.4 Land Propagation The received signal power: G G P P = t r t r L L is the propagation loss in the channel, i.e., L = LP LS LF Fast fading Slow fading (Shadowing) Path loss CS 647 2.5 Propagation Loss Fast Fading (Short-term fading) Slow Fading (Long-term fading) Signal Strength (dB) Path Loss Distance CS 647 2.6 Path Loss (Land Propagation) Simplest Formula: -α Lp = A d where A and -
Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication
sensors Article Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication Chao Li 1,2,*, Franck Marzani 3 and Fan Yang 3 1 State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China 2 University of Chinese Academy of Sciences, Beijing 100190, China 3 LE2I EA7508, Université Bourgogne Franche-Comté, 21078 Dijon, France; [email protected] (F.M.); [email protected] (F.Y.) * Correspondence: [email protected] Received: 25 September 2018; Accepted: 28 November 2018; Published: 1 December 2018 Abstract: The chaos phase modulation sequences consist of complex sequences with a constant envelope, which has recently been used for direct-sequence spread spectrum underwater acoustic communication. It is considered an ideal spreading code for its benefits in terms of large code resource quantity, nice correlation characteristics and high security. However, demodulating this underwater communication signal is a challenging job due to complex underwater environments. This paper addresses this problem as a target classification task and conceives a machine learning-based demodulation scheme. The proposed solution is implemented and optimized on a multi-core center processing unit (CPU) platform, then evaluated with replay simulation datasets. In the experiments, time variation, multi-path effect, propagation loss and random noise were considered as distortions. According to the results, compared to the reference algorithms, our method has greater reliability with better temporal efficiency performance. Keywords: underwater acoustic communication; direct sequence spread spectrum; chaos phase modulation sequence; partial least square regression; machine learning 1. Introduction The underwater acoustic communication has always been a crucial research topic [1–6]. -
MIMO Systems and Transmit Diversity 1 Introduction 2 MIMO Capacity Analysis
MIMO Systems and Transmit Diversity 1 Introduction So far we have investigated the use of antenna arrays in interference cancellation and for receive diversity. This final chapter takes a broad view of the use of antenna arrays in wireless communi- cations. In particular, we will investigate the capacity of systems using multiple transmit and/or multiple receive antennas. This provides a fundamental limit on the data throughput in multiple- input multiple-output (MIMO) systems. We will also develop the use of transmit diversity, i.e., the use of multiple transmit antennas to achieve reliability (just as earlier we used multiple receive antennas to achieve reliability via receive diversity). The basis for receive diversity is that each element in the receive array receives an independent copy of the same signal. The probability that all signals are in deep fade simultaneously is then significantly reduced. In modelling a wireless communication system one can imagine that this capability would be very useful on transmit as well. This is especially true because, at least in the near term, the growth in wireless communications will be asymmetric internet traffic. A lot more data would be flowing from the base station to the mobile device that is, say, asking for a webpage, but is receiving all the multimedia in that webpage. Due to space considerations, it is more likely that the base station antenna comprises multiple elements while the mobile device has only one or two. In addition to providing diversity, intuitively having multiple transmit/receive antennas should allow us to transmit data faster, i.e., increase data throughput. -
Multiple Antenna Technologies
Multiple Antenna Technologies Manar Mohaisen | YuPeng Wang | KyungHi Chang The Graduate School of Information Technology and Telecommunications INHA University ABSTRACT the receiver. Alamouti code is considered as the simplest transmit diversity scheme while the receive diversity includes maximum ratio, equal gain and selection combining Multiple antenna technologies have methods. Recently, cooperative received high attention in the last few communication was deeply investigated as a decades for their capabilities to improve the mean of increasing the communication overall system performance. Multiple-input reliability by not only considering the multiple-output systems include a variety of mobile station as user but also as a base techniques capable of not only increase the station (or relay station). The idea behind reliability of the communication but also multiple antenna diversity is to supply the impressively boost the channel capacity. In receiver by multiple versions of the same addition, smart antenna systems can increase signal transmitted via independent channels. the link quality and lead to appreciable On the other hand, multiple antenna interference reduction. systems can tremendously increase the channel capacity by sending independent signals from different transmit antennas. I. Introduction BLAST spatial multiplexing schemes are a good example of such category of multiple Multiple antennas technologies proposed antenna technologies that boost the channel for communications systems have gained capacity. much attention in the last few years because In addition, smart antenna technique can of the huge gain they can introduce in the significantly increase the data rate and communication reliability and the channel improve the quality of wireless transmission, capacity levels. Furthermore, multiple which is limited by interference, local antenna systems can have a big contribution scattering and multipath propagation. -
Continuous Phase Modulation with Faster-Than-Nyquist Signaling for Channels with 1-Bit Quantization and Oversampling at the Receiver Rodrigo R
1 Continuous Phase Modulation With Faster-than-Nyquist Signaling for Channels With 1-bit Quantization and Oversampling at the Receiver Rodrigo R. M. de Alencar, Student Member, IEEE, and Lukas T. N. Landau, Member, IEEE, Abstract—Continuous phase modulation (CPM) with 1-bit construction of zero-crossings. The proposed CPM waveform quantization at the receiver is promising in terms of energy conveys the same information per time interval as the common and spectral efficiency. In this study, CPM waveforms with CPFSK while its bandwidth can be the same and even lower. symbol durations significantly shorter than the inverse of the signal bandwidth are proposed, termed faster-than-Nyquist CPM. Referring to the high signaling rate, like it is typical for faster- This configuration provides a better steering of zero-crossings than-Nyquist signaling [9], the novel waveform is termed as compared to conventional CPM. Numerical results confirm a faster-than-Nyquist continuous phase modulation (FTN-CPM). superior performance in terms of BER in comparison with state- Numerical results confirm that the proposed waveform yields of-the-art methods, while having the same spectral efficiency and a significantly reduced bit error rate (BER) as compared to a lower oversampling factor. Moreover, the new waveform can be detected with low-complexity, which yields almost the same the existing methods [7], [6] with at least the same spectral performance as using the BCJR algorithm. efficiency. In addition, FTN-CPM can be detected with low- complexity and with a lower effective oversampling factor in Index Terms—1-bit quantization, oversampling, continuous phase modulation, faster-than-Nyquist signaling. -
Performance Analysis of Diversity Techniques for Wireless Communication System
1 Performance Analysis of Diversity Techniques for Wireless Communication System Md. Jaherul Islam [email protected] This Thesis is a part (30 ECTS) of Master of Science degree (120 ECTS) in Electrical Engineering emphasis on Telecommunication Blekinge Institute of Technology February 12 Blekinge Institute of Technology School of Engineering Department of Telecommunication Supervisor: Magnus G Nilsson Examiner: Magnus G Nilsson Contact: [email protected] 2 Abstract Different diversity techniques such as Maximal-Ratio Combining (MRC), Equal-Gain Combining (EGC) and Selection Combining (SC) are described and analyzed. Two branches (N=2) diversity systems that are used for pre-detection combining have been investigated and computed. The statistics of carrier to noise ratio (CNR) and carrier to interference ratio (CIR) without diversity assuming Rayleigh fading model have been examined and then measured for diversity systems. The probability of error ( ) vs CNR and ( ) versus CIR have also been obtained. The fading dynamic range of the instantaneous CNR and CIR is reduced remarkably when diversity systems are used [1]. For a certain average probability of error, a higher valued average CNR and CIR is in need for non-diversity systems [1]. But a smaller valued of CNR and CIR are compared to diversity systems. The overall conclusion is that maximal-ratio combining (MRC) achieves the best performance improvement compared to other combining methods. Diversity techniques are very useful to improve the performance of high speed wireless channel to transmit data and information. The problems which considered in this thesis are not new but I have tried to organize, prove and analyze in new ways. -
Great Expectations: the Value of Spatial Diversity in Wireless Networks
Great Expectations: The Value of Spatial Diversity in Wireless Networks SUHAS N. DIGGAVI, MEMBER, IEEE, NAOFAL AL-DHAHIR, SENIOR MEMBER, IEEE, A. STAMOULIS, MEMBER, IEEE, AND A. R. CALDERBANK, FELLOW, IEEE Invited Paper In this paper, the effect of spatial diversity on the throughput The challenge here is that Moore’s Law does not seem to and reliability of wireless networks is examined. Spatial diversity apply to rechargeable battery capacity, and though the den- is realized through multiple independently fading transmit/re- sity of transistors on a chip has consistently doubled every ceive antenna paths in single-user communication and through independently fading links in multiuser communication. Adopting 18 mo, the energy density of batteries only seems to double spatial diversity as a central theme, we start by studying its every 10 years. This need to conserve energy (see [2] and ref- information-theoretic foundations, then we illustrate its benefits erences therein) leads us to focus on what is possible when across the physical (signal transmission/coding and receiver signal signal processing at the terminal is limited. Throughout this processing) and networking (resource allocation, routing, and paper, we use the cost and complexity of the receiver to applications) layers. Throughout the paper, we discuss engineering intuition and tradeoffs, emphasizing the strong interactions be- bound the resources available for signal processing. Wireless tween the various network functionalities. spectrum itself is a valuable resource that also needs to be conserved given the economic imperative of return on multi- Keywords—Ad hoc networks, channel estimation, diversity, fading channels, hybrid networks, information theory for wireless billion-dollar investments by wireless carriers [1]. -
Digital Phase Modulation: a Review of Basic Concepts
Digital Phase Modulation: A Review of Basic Concepts James E. Gilley Chief Scientist Transcrypt International, Inc. [email protected] August , Introduction The fundamental concept of digital communication is to move digital information from one point to another over an analog channel. More specifically, passband dig- ital communication involves modulating the amplitude, phase or frequency of an analog carrier signal with a baseband information-bearing signal. By definition, fre- quency is the time derivative of phase; therefore, we may generalize phase modula- tion to include frequency modulation. Ordinarily, the carrier frequency is much greater than the symbol rate of the modulation, though this is not always so. In many digital communications systems, the analog carrier is at a radio frequency (RF), hundreds or thousands of MHz, with information symbol rates of many megabaud. In other systems, the carrier may be at an audio frequency, with symbol rates of a few hundred to a few thousand baud. Although this paper primarily relies on examples from the latter case, the concepts are applicable to the former case as well. Given a sinusoidal carrier with frequency: fc , we may express a digitally-modulated passband signal, S(t), as: S(t) A(t)cos(2πf t θ(t)), () = c + where A(t) is a time-varying amplitude modulation and θ(t) is a time-varying phase modulation. For digital phase modulation, we only modulate the phase of the car- rier, θ(t), leaving the amplitude, A(t), constant. BPSK We will begin our discussion of digital phase modulation with a review of the fun- damentals of binary phase shift keying (BPSK), the simplest form of digital phase modulation. -
Bandwidth Scaling of a Phase-Modulated CW Comb Through Four-Wave Mixing in a Silicon Nano-Waveguide
Chalmers Publication Library Bandwidth scaling of a phase-modulated CW comb through four-wave mixing in a silicon nano-waveguide This document has been downloaded from Chalmers Publication Library (CPL). It is the author´s version of a work that was accepted for publication in: Optics Letters (ISSN: 0146-9592) Citation for the published paper: Liu, Y. ; Metcalf, A. ; Torres Company, V. (2014) "Bandwidth scaling of a phase-modulated CW comb through four-wave mixing in a silicon nano-waveguide". Optics Letters, vol. 39(22), pp. 6478-6481. Downloaded from: http://publications.lib.chalmers.se/publication/208731 Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published source. Please note that access to the published version might require a subscription. Chalmers Publication Library (CPL) offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all types of publications: articles, dissertations, licentiate theses, masters theses, conference papers, reports etc. Since 2006 it is the official tool for Chalmers official publication statistics. To ensure that Chalmers research results are disseminated as widely as possible, an Open Access Policy has been adopted. The CPL service is administrated and maintained by Chalmers Library. (article starts on next page) Bandwidth scaling of a phase-modulated CW comb through four-wave mixing in a silicon nano-waveguide Yang Liu,1* Andrew J. Metcalf,1 Victor Torres Company,1,2 Rui Wu,1,3 Li Fan,1,4 Leo T. -
Module 3: Physical Layer
Module 3: Physical Layer Dr. Natarajan Meghanathan Associate Professor of Computer Science Jackson StateMeghanathan University Jackson, MS 39217 Copyrights Phone: 601-979-3661 E-mail: [email protected] Natarajan 1 Topics • 3.1 Signal Levels: Baud rate and Bit rate • 3.2 Channel Encoding Standards – RS-232 and Manchester Encoding – Delay during transmission • 3.3 Transmission Order of Bits and Bytes • 3.4 Modulation Techniques – Amplitude, Frequency and PhaseMeghanathan modulation • 3.5 Multiplexing Techniques – TDMA, FDMA, StatisticalCopyrights Multiplexing and CDMA All Natarajan 2 Meghanathan Copyrights All Natarajan 3.1 Signal Levels: Baud Rate and Bit Rate Analog and Digital Signals • Data communications deals with two types of information: – analog – digital • An analog signal is characterized by a continuous mathematical function – when the input changes from one value to the next, it does so by movingMeghanathan through all possible intermediate values Copyrights • A digital signal has a fixed set of All valid levels Natarajan – each change consists of an instantaneous move from one valid level to another 4 Digital Signals and Signal Levels • Some systems use voltage to represent digital values – by making a positive voltage correspond to a logical one – and zero voltage correspond to a logical zero • For example, +5 volts can be used for a logical one and 0 volts for a logical zero • If only two levels of voltage are used – each level corresponds to one data bit (0 or 1). • Some physical transmission mechanismsMeghanathan