An Introduction to Orthogonal Frequency-Division Multiplexing
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Chapter 1 Experiment-8
Chapter 1 Experiment-8 1.1 Single Sideband Suppressed Carrier Mod- ulation 1.1.1 Objective This experiment deals with the basic of Single Side Band Suppressed Carrier (SSB SC) modulation, and demodulation techniques for analog commu- nication.− The student will learn the basic concepts of SSB modulation and using the theoretical knowledge of courses. Upon completion of the experi- ment, the student will: *UnderstandSSB modulation and the difference between SSB and DSB modulation * Learn how to construct SSB modulators * Learn how to construct SSB demodulators * Examine the I Q modulator as SSB modulator. * Possess the necessary tools to evaluate and compare the SSB SC modulation to DSB SC and DSB_TC performance of systems. − − 1.1.2 Prelab Exercise 1.Using Matlab or equivalent mathematics software, show graphically the frequency domain of SSB SC modulated signal (see equation-2) . Which of the sign is given for USB− and which for LSB. 1 2 CHAPTER 1. EXPERIMENT-8 2. Draw a block diagram and explain two method to generate SSB SC signal. − 3. Draw a block diagram and explain two method to demodulate SSB SC signal. − 4. According to the shape of the low pass filter (see appendix-1), choose a carrier frequency and modulation frequency in order to implement LSB SSB modulator, with minimun of 30 dB attenuation of the USB component. 1.1.3 Background Theory SSB Modulation DEFINITION: An upper single sideband (USSB) signal has a zero-valued spectrum for f <fcwhere fc, is the carrier frequency. A lower single| | sideband (LSSB) signal has a zero-valued spectrum for f >fc where fc, is the carrier frequency. -
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 -
3 Characterization of Communication Signals and Systems
63 3 Characterization of Communication Signals and Systems 3.1 Representation of Bandpass Signals and Systems Narrowband communication signals are often transmitted using some type of carrier modulation. The resulting transmit signal s(t) has passband character, i.e., the bandwidth B of its spectrum S(f) = s(t) is much smaller F{ } than the carrier frequency fc. S(f) B f f f − c c We are interested in a representation for s(t) that is independent of the carrier frequency fc. This will lead us to the so–called equiv- alent (complex) baseband representation of signals and systems. Schober: Signal Detection and Estimation 64 3.1.1 Equivalent Complex Baseband Representation of Band- pass Signals Given: Real–valued bandpass signal s(t) with spectrum S(f) = s(t) F{ } Analytic Signal s+(t) In our quest to find the equivalent baseband representation of s(t), we first suppress all negative frequencies in S(f), since S(f) = S( f) is valid. − The spectrum S+(f) of the resulting so–called analytic signal s+(t) is defined as S (f) = s (t) =2 u(f)S(f), + F{ + } where u(f) is the unit step function 0, f < 0 u(f) = 1/2, f =0 . 1, f > 0 u(f) 1 1/2 f Schober: Signal Detection and Estimation 65 The analytic signal can be expressed as 1 s+(t) = − S+(f) F 1{ } = − 2 u(f)S(f) F 1{ } 1 = − 2 u(f) − S(f) F { } ∗ F { } 1 The inverse Fourier transform of − 2 u(f) is given by F { } 1 j − 2 u(f) = δ(t) + . -
Protection of Data Transmission Systems from the Influence of Intersymbol Interference of Signals
59 Protection of data transmission systems from the influence of intersymbol interference of signals © Volodymyr Yartsev 1 and © Dmitry Gololobov 2 1 State University of Telecommunications, Kyiv, Ukraine 2 Taras Shevchenko National University of Kyiv, Ukraine [email protected], [email protected] Abstract. The problems of creating a regenerator of an optical system for trans- mitting discrete messages with the aim of protecting against the influence of in- tersymbol interference of signals using the methods of statistical theory for a mixture of probability distributions are considered. A demodulator adaptation model that calculates the weighted sum of 4 samples of the input signal and com- pares the resulting sum with a threshold value has been created and studied. The main operations for adaptation of the regenerator during demodulation of signals distorted by intersymbol interference are assigned under certain transmission conditions, which are formulated as the task of evaluating some parameters of the probability distribution of the mixture. A block diagram of a regenerating optical signal in which an algorithm for digital processing of a mixture is imple- mented is proposed. The analysis is carried out and the requirements for the pa- rameters of the analog-to-digital converter used in the regenerator are deter- mined. The results of modeling digital processing of a discrete test message to verify the process of adaptation of the device to the influence of intersymbol dis- tortions of signals are presented. The block diagram of a digital processing device is substantiated, in which high-speed nodes that perform real-time processing are combined with a microprocessor, which implements an algorithm for adapting the regenerator to specific message transmission conditions. -
Revision of Wireless Channel
ELEC6214 Advanced Wireless Communications Networks and Systems S Chen Revision of Wireless Channel • Quick recap system block diagram Wireless CODEC MODEM Channel • Previous three lectures looked into wireless mobile channels – To understand mobile communication technologies, one needs a deep understand of mobile communication media – Two main sources of hostility in mobile media are Doppler spread and multipath – Many techniques developed are counter measures for fading and frequency selective – How spatial/angular dimension fits into wireless mobile landscape • Front end of transceiver is Modem, which faces hostile (fading and frequency selective) communication media – Next eight lectures we will have a close look into Modem 57 ELEC6214 Advanced Wireless Communications Networks and Systems S Chen Digital Modulation Overview In Digital Coding and Transmission, we learn schematic of MODEM (modulation and demodulation) • with its basic components: clock carrier pulse b(k) x(k) x(t) s(t) pulse Tx filter bits to modulat. symbols generator GT (f) clock carrier channel recovery recovery GC (f) ^ b(k) x(k)^x(t) ^s(t) ^ symbols sampler/ Rx filter AWGN demodul. + to bits decision GR (f) n(t) The purpose of MODEM: transfer the bit stream at required rate over the communication medium • reliably – Given system bandwidth and power resource 58 ELEC6214 Advanced Wireless Communications Networks and Systems S Chen Constellation Diagram • Digital modulation signal has finite states. This manifests in symbol (message) set: M = {m1, m2, ··· , mM }, where each symbol contains log2 M bits • Or in modulation signal set: S = {s1(t),s2(t), ··· sM (t)}. There is one-to-one relationship between two sets: modulation scheme M S ←→ Q • Example: BPSK, M = 2. -
Bit & Baud Rate
What’s The Difference Between Bit Rate And Baud Rate? Apr. 27, 2012 Lou Frenzel | Electronic Design Serial-data speed is usually stated in terms of bit rate. However, another oft- quoted measure of speed is baud rate. Though the two aren’t the same, similarities exist under some circumstances. This tutorial will make the difference clear. Table Of Contents Background Bit Rate Overhead Baud Rate Multilevel Modulation Why Multiple Bits Per Baud? Baud Rate Examples References Background Most data communications over networks occurs via serial-data transmission. Data bits transmit one at a time over some communications channel, such as a cable or a wireless path. Figure 1 typifies the digital-bit pattern from a computer or some other digital circuit. This data signal is often called the baseband signal. The data switches between two voltage levels, such as +3 V for a binary 1 and +0.2 V for a binary 0. Other binary levels are also used. In the non-return-to-zero (NRZ) format (Fig. 1, again), the signal never goes to zero as like that of return- to-zero (RZ) formatted signals. 1. Non-return to zero (NRZ) is the most common binary data format. Data rate is indicated in bits per second (bits/s). Bit Rate The speed of the data is expressed in bits per second (bits/s or bps). The data rate R is a function of the duration of the bit or bit time (TB) (Fig. 1, again): R = 1/TB Rate is also called channel capacity C. If the bit time is 10 ns, the data rate equals: R = 1/10 x 10–9 = 100 million bits/s This is usually expressed as 100 Mbits/s. -
VHF and UHF Signal Characteristics Observed on a Long Knife-Edge Diffraction Path A
JOURNAL OF RESEARCH of the National Bureau of Standards- D. Radio Propagatior. Vol. 65D, No. 5, September- Odober 1961 VHF and UHF Signal Characteristics Observed on a Long Knife-Edge Diffraction Path A. P. Barsis and R. S. Kirby (R eceived April 6, 1961) Contribution from Central Radio Propagation Laboratory, National Bureau of Standards, Boulder, Colo. During 1959 and 1960 long-term transmission loss measurements were performed over a 223 kilom eter path in Eastern Colorado using frequencies of 100 and 751 megacycles per second. This path intersects P ikes P eak which forms a knife-edge type obstacle visible from both terminals. The transmission loss measurements have been analyzed in terms of diurnal a nd seaso nal variations in hourly medians and in instan taneous levels. As expected, results show t hat the long-term fading range is substantially less t han expected for t ropospheric seatter paths of comparable length. T ransmission loss levels were in general agreem ent wi t h predicted k ni fe-edge d iffraction propagation when a ll owance is made for rounding of t he knife edge. A technique for est imatin g long-term fading ra nges is presented a nd t he res ults are in good agreement with observations. Short-term variations in some case resemble t he space-wave fadeo uts commonly observed on within- the-horizon paths, a lthough other phe nomena may contribute to t he fadin g. Since t he foreground telTain was rough there was no evidence of direct a nd grou nd-refl ected lobe structure. -
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]. -
Pulse Shape Filtering in Wireless Communication-A Critical Analysis
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No.3, March 2011 Pulse Shape Filtering in Wireless Communication-A Critical Analysis A. S Kang Vishal Sharma Assistant Professor, Deptt of Electronics and Assistant Professor, Deptt of Electronics and Communication Engg Communication Engg SSG Panjab University Regional Centre University Institute of Engineering and Technology Hoshiarpur, Punjab, INDIA Panjab University Chandigarh - 160014, INDIA [email protected] [email protected] Abstract—The goal for the Third Generation (3G) of mobile networks. The standard that has emerged is based on ETSI's communications system is to seamlessly integrate a wide variety Universal Mobile Telecommunication System (UMTS) and is of communication services. The rapidly increasing popularity of commonly known as UMTS Terrestrial Radio Access (UTRA) mobile radio services has created a series of technological [1]. The access scheme for UTRA is Direct Sequence Code challenges. One of this is the need for power and spectrally Division Multiple Access (DSCDMA). The information is efficient modulation schemes to meet the spectral requirements of spread over a band of approximately 5 MHz. This wide mobile communications. Pulse shaping plays a crucial role in bandwidth has given rise to the name Wideband CDMA or spectral shaping in the modern wireless communication to reduce WCDMA.[8-9] the spectral bandwidth. Pulse shaping is a spectral processing technique by which fractional out of band power is reduced for The future mobile systems should support multimedia low cost, reliable , power and spectrally efficient mobile radio services. WCDMA systems have higher capacity, better communication systems. It is clear that the pulse shaping filter properties for combating multipath fading, and greater not only reduces inter-symbol interference (ISI), but it also flexibility in providing multimedia services with defferent reduces adjacent channel interference. -
Capacity of Flat and Freq.-Selective Fading Channels Linear Digital Modulation Review
Lecture 8 - EE 359: Wireless Communications - Winter 2020 Capacity of Flat and Freq.-Selective Fading Channels Linear Digital Modulation Review Lecture Outline • Channel Inversion with Fixed Rate Transmission • Comparison of Fading Channel Capacity under Different Schemes • Capacity of Frequency Selective Fading Channels • Review of Linear Digital Modulation • Performance of Linear Modulation in AWGN 1. Channel Inversion with Fixed Rate Transmission • Suboptimal transmission strategy where fading is inverted to maintain constant re- ceived SNR. • Simplifies system design and is used in CDMA systems for power control. • Capacity with channel inversion greatly reduced over that with optimal adaptation (capacity equals zero in Rayleigh fading). • Truncated inversion: performance greatly improved by inverting above a cutoff γ0. 2. Comparison of Fading Channel Capacity under Different Schemes: • Fading generally decreases channel capacity. • Rate/power adaptation have similar capacity as rate adaptation alone. • Rate adaptation alone has same capacity as no adaptation (RX CSI only) but rate adaptation is more practical due to complexity of ML decoding over long blocklengths that experience all fading values. This ML decoding is necesssary to achieve capacity in the RX CSI only case. • Truncated channel inversion is more practical than rate adaptation, with significant capacity gain over full channel inversion, which has zero capacity in Rayleigh fading. 3. Capacity of Frequency Selective Fading Channels • Capacity for time-invariant frequency-selective fading channels is a \water-filling” of power over frequency. • For time-varying ISI channels, capacity is unknown in general. Approximate by di- viding up the bandwidth subbands of width equal to the coherence bandwidth (same premise as multicarrier modulation) with independent fading in each subband. -
7.3.7 Video Cassette Recorders (VCR) 7.3.8 Video Disk Recorders
/7 7.3.5 Fiber-Optic Cables (FO) 7.3.6 Telephone Company Unes (TELCO) 7.3.7 Video Cassette Recorders (VCR) 7.3.8 Video Disk Recorders 7.4 Transmission Security 8. Consumer Equipment Issues 8.1 Complexity of Receivers 8.2 Receiver Input/Output Characteristics 8.2.1 RF Interface 8.2.2 Baseband Video Interface 8.2.3 Baseband Audio Interface 8.2.4 Interfacing with Ancillary Signals 8.2.5 Receiver Antenna Systems Requirements 8.3 Compatibility with Existing NTSC Consumer Equipment 8.3.1 RF Compatibility 8.3.2 Baseband Video Compatibility 8.3.3 Baseband Audio Compatibility 8.3.4 IDTV Receiver Compatibility 8.4 Allows Multi-Standard Display Devices 9. Other Considerations 9.1 Practicality of Near-Term Technological Implementation 9.2 Long-Term Viability/Rate of Obsolescence 9.3 Upgradability/Extendability 9.4 Studio/Plant Compatibility Section B: EXPLANATORY NOTES OF ATTRIBUTES/SYSTEMS MATRIX Items on the Attributes/System Matrix for which no explanatory note is provided were deemed to be self-explanatory. I. General Description (Proponent) section I shall be used by a system proponent to define the features of the system being proposed. The features shall be defined and organized under the headings ot the following subsections 1 through 4. section I. General Description (Proponent) shall consist of a description of the proponent system in narrative form, which covers all of the features and characteris tics of the system which the proponent wishe. to be included in the public record, and which will be used by various groups to analyze and understand the system proposed, and to compare with other propo.ed systems.