Emergency Radio Identification by Supervised Learning based Automatic Modulation Recognition

Size: px
Start display at page:

Download "Emergency Radio Identification by Supervised Learning based Automatic Modulation Recognition"

Transcription

1 Emergency Radio Identification by Supervised Learning based Automatic Modulation Recognition M. A. Rahman, M. Kim and J. Takada Department of International Development Engineering, Tokyo Institute of Technology, S6-4, -1-1, O-okayama,Meguro-ku, Tokyo , s:{abdur, mskim, Abstract Post-disaster scenario requires quick and effective rescue efforts. Lack of collaboration among the rescue teams creates severe interference among the emergency emitters. A sensor network in terms of architecture and technology has been proposed in this study in order to create a collaborative environment in such cases. Possible architecture and technology of an emergency sensor network to collect the emitter information is discussed in the first segment of this study. Remaining part discussed a supervised learning based automatic modulation classification system to indentify the emitters. Modulation classification is necessary to recognize the identity of the emergency emitters. To detect the modulation schemes of emergency radios automatically in a disaster scenario, the algorithms are needed to be quick and precise. Processing speed of the recognition system can be controlled by appropriate selection of parameters with proper signal processing techniques. This study investigated some well known techniques and proposed a supervised learning based scheme for automatic modulation classification. The performance of the proposed algorithm is quite satisfactory on analog modulations whereas there are still some provisions of improvement on digital modulations classification. 1. Introduction Automatic Modulation Recognition (AMR) is the first step to the identification process of an unknown emitter. Research on AMR is going on for almost three decades. The applications include but are not limited to military and civilian applications such as interference identifications, surveillance, spectrum monitoring, security, cognitive radio etc. This study uses the AMR to identify the unknown emergency emitters used by the rescue teams in a post disaster scenario. After big natural or manmade disasters generally rescue teams come to the site in the first hour. In case of developed countries emergency frequencies are allocated among the first responders for better managements, on the other hand the allocation policies are yet to be implemented in most of the least developed countries. For example in Japan the emergency frequencies are allocated by different states to the emergency responders. But after a big disaster rescue teams come from all over the world, emergency channels are normally fixed by each rescue team beforehand, and two or more rescue teams that are configured to use the same wireless channels may come to the scene and try to establish their own networks. This may create severe interference among the corresponding emitters and hinder the rescue efforts. A spectrum management system for emergency situations is necessary to perform the 1

2 emergency operations efficiently [1]. This study discussed about the architecture and components of an emergency spectrum management system that will identify the spectrum usage as well as the emergency emitters in the first segment. In the remaining part a supervised learning based algorithm has been proposed. The proposed system will maintain a database of the active emitters in the disaster area. The database is updated frequently by collecting the spectrum sensing information from onsite sensors. Identification of the emitter can be done by comparing the extracted parameters with some prior information about the rescue teams. In this paper a possible architecture of a sensor network to collect and extract some physical parameters of active emergency radio in a disaster area has been discussed briefly. Afterwards the concentration was given mostly on an automatic modulation recognition system that can recognize both analog and digital modulation signals. Parameter extraction and classification algorithm description was followed by some simulation and measurements results.. Proposed network architecture This section describes the architecture of the sensor network used to collect the information of the disaster radios. The whole system is subdivided into 5 subsystems. Fig. 1 represents the functional blocks of the system. It is assumed that the sensor nodes will not have any communications with the emergency emitters. Sensors will only scan the spectrum to detect the presence of emitters. After a disaster generally lots of rescue teams come to help the victims. The rescue team members start moving in the disaster area as shown in Fig.. Only a spectrum sensing system can identify the active frequency in the designated area. Sensors shown in the figure will forward the spectrum information toward the central database server. For better management of the sensors a hierarchical network architecture has been proposed. Fig. 3 shows our proposed network architecture which incorporates cooperative spectrum sensing to combat multipath and shadowing effects. We will use a cluster based sensing network in order to provide spectrum sensing coverage over a wide area [1]. Each cluster consists of one head node and several sensor nodes. The sensor nodes are strategically placed apart depending on the physical environment. Cooperative spectrum sensing schemes achieve maximum cooperative gains only when the sensor nodes experience Emergency Emitter Channel Interferences Input Signal Modulation Receiver Noise Update Database Identification Waveform Detection Modulation Recognition Geolocation Carrier Detection Figure 1: Functional overview of the system

3 Multi-hop Ad-hoc link Emergency Radio Central Database Server AP Ad-hoc (HN) Infrastructure link Ad-hoc link AP Ad-hoc (HN) Spectrum Sensor Disaster Area Figure : Classification performance Ad-hoc (HN) AP Central database server Figure 3: Hierarchical network framework independent multipath fading and are not blocked by the same obstacle. Sensor nodes sense the spectra individually, and the received power data in the frequency domain is sent to the head node. The head node then determines which frequency bands are currently being used in that particular cluster area. For each frequency band whose power is above a predetermined threshold, the head node chooses the sensor node with the strongest power, and sends a command to that sensor node to obtain the time domain data. Then, the head node collects the time domain data received by array antenna from certain sensor nodes, and from this data, the head node will estimate the modulation type and geolocation via angle of arrival. After the calculation has finished, the head node sends a text file containing all the necessary information needed to the database. The IEEE 8.11 wireless LAN standard in infrastructure mode is used to transfer information from the sensor nodes to the head nodes. Maximum sensing coverage provided by a single cluster will depend mainly on the Wi-Fi range, the sensitivity of each sensor node and also the cooperative sensing scheme employed. Head nodes of each cluster are connected via a multihop ad-hoc network to forward the sensed and extracted information to the central database server. 3. Automatic modulation recognition Modulation can be defined as varying the amplitude, frequency and phase of a relatively higher frequency carrier according to a lower frequency modulating signal. Modulation can mainly be classified as two different categories. A continuous modulating signal is classified as analogue while a discrete modulating signal is referred as digital modulations. Each type of modulations can further be divided into Linear and Angle modulations []. Linear modulations i.e. in amplitude modulations the sum of the carrier and the modulating signal is shifted in frequency by the carrier frequency. However in case of angle modulations 3

4 the phase/frequency of the carrier is altered according to the modulating signal. Digital modulations use different amplitude, frequency/phase for every symbol of the modulating signal. To identify these different modulation schemes the selected parameters should be able to differentiate among the unique signatures of each modulation type. The classification algorithms are basically divided into two steps [3]. 1) Unique parameter extraction and ) Classification of modulations. Unique parameter extraction can be termed as preprocessing of the received signal where some physical properties of the received signal are extracted. The accuracy of the preprocessing mainly depends on the classification algorithms used in the second step. Most of the cases accuracy of the classification depends on the precision and SNR of the received signal. Classification algorithms can primarily be divided into two categories. 1) Likelihood based and ) Feature based approach. For the first category decisions are made by a likelihood function where the likelihood ratio is compared with a predetermined threshold. Feature based approaches extract the values of some specific parameters and make the decision by comparing it with some previously observed values [4]. Feature based approaches are relatively simple and easy to implement whereas likelihood based classifications can achieve near optimal solution with higher computation complexity. For the proposed scenario AMR algorithm should combat with some hostile issues like non-cooperative environment, multipath, shadowing, no prior knowledge about the transmitted signals. 3.1 Statistical signal characterization Statistical Signal Characterization (SSC) is a little unconventional way to perform frequencydomain analysis of a waveform without using the Fourier transform or the Fast Fourier transform (FFT) [5]. FFT is used extensively for its accuracy and effectiveness, but limited by procession speed and intense computation. SSC allows characterization for frequency domain similarities and differences among waveforms from time-domain samples. In SSC waveforms are characterized as a function of their relative amplitudes and phases. SSC exploits the fact that a signal consists of a set of consecutive segments. Each segment has amplitude and period characteristics that are unique for particular frequency combinations and statistically well-behaved [5]. i.e. the statistical measures like mean and variance of the segments are consistent. One pre condition of such analysis is the proper, adequate Figure 4: SSC amplitude and period 4

5 and accurate sampling of the received signal. As mean and variance of the segments are consistent for a certain type of waveform these characterization can be used to classify the received signal. Four SSC parameters are Amplitude mean, Period mean, Amplitude deviation and Period deviation. These parameters can be observed by Fig. 4. The waveform is a combination of multiple frequencies represented by a series of extrema. SSC segments are defined by the area bounded by two consecutive extrema (maxima and minima). A waveform with N extrema (N/ Maxima + N/ Minima) has N 1 SSC segments. In the figure six extrema produces five SSC segments. Extrema amplitude and periods are denoted as (a a 5 ) and (t t 5 ) respectively. However, the detection of the extrema is sensitive to the carrier frequency estimation that is beyond the scope of this study. Mathematically, the segment amplitudes and periods are calculated Equations (1),() respectively. A i = a i a i 1 (1) T i = t i t i 1 () where, A i, T i = Amplitude and period of the i-th segment, a i, t i = Amplitude and period at the concluding extremum of the segment, and a i 1, t i 1 = Amplitude and period at the beginning extremum of the segment respectively. These two values are used to calculate four SSC parameters by the following Equations (3)-(6). ( NS A M = A i )/N S (3) here, A M = Amplitude mean, T M = Period mean, A D = Amplitude deviation, T D = Period Deviation, N S = Number of SSC segments. 3. Classification method A D = T D = T M = ( NS i=1 ( NS T i )/N S (4) i=1 ) A i A M /N S (5) i=1 ( NS i=1 ) T i T M /N S (6) For the proposed system the target modulation classes are assumed to be known in advance. Rescue teams use both analog and digital modulation schemes for communication. As the 5

6 modulation classes are finite a supervised learning based classification algorithm can be used here. A brief review of the supervised learning based algorithms is available in [6]. Supervised learning is a very popular and useful technique used in the machine learning field for classifications. A function is deduced from a provided training set. Training set contains some input objects and with corresponding outputs. The algorithm may produce continuous (regression) value or a class label (classification) of desired outputs. The performance of the algorithm can be measured by its ability to identify a class for an input object after observing a number of training set. The learning algorithm should be reasonable to the unseen input objects also. Decision tree based classification algorithms are widely used in the machine learning field for its simplicity and processing speeds. The decision tree is termed as a predictive model reference that subdivides the observations into a hierarchical structure towards the target class. The decision tree consists of a root, leaves and branches that lead toward target class. Decision tree has the ability to visually represent the decision making process. Fig. 5 is showing the example of a decision tree. The classification algorithm used here is divided into three parts. Unique parameters extraction Training set generation with known objects and classes Classification performance analysis for unknown object values SSC parameters discussed earlier have been identified as potential objects to classify different modulations. A set of known objects and classes are used to generate the training set for different SNR values. In this study a decision tree algorithm based on C4.5 [7] is used for training set generation and classification purposes. The algorithm is developed in JAVA environment and called as J48. This algorithm is implemented in Java and included in WEKA (Waikato Environment for Knowledge Analysis) data mining tool [8]. 4. Simulation System A recorded continuous voice signal modulated by AM, FM and PM schemes has been generated as the input signal for the classification system. The carrier and bandwidth of the signal is assumed to be known by the parameter extractor. However, in real implementation the estimation error of the carrier frequency will have some significant impact. This is identified as one of the future studies of this work. A 64-bit pseudo random sequence have been modulated by ASK, FSK, BPSK and QPSK schemes as the input signal for digital classification system. The symbol rate is taken as 6.4 Ksym/sec. Roll-off factors are not considered for simplicity. The modulated signal is then added with a noise signal simulated by a zero mean Gaussian random sequence with a variance dependent on the SNR (-15dB). Fig. 6 shows the simulation steps for both analog and digital modulations. Table 1 represents the simulation parameters used to generate the input signals. 6

7 Analog Digital Amp_Dev Voice PN Seq. <=.5638 Amp_Mean >.5638 Amp_Mean Modulator <= > <= > AWGN Time_Mean FM Amp_Dev AM SSC Extraction Training <=.5679 BPSK >.5679 <=.7793 >.7793 QPSK FSK ASK Figure 5: Decision tree example Classification Waveform Figure 6: Simulation flowchart Table 1: Simulation parameters. Parameters Analog Digital Input Voice PN Seq. Rate 8KHz., 16-bits 6.4 Ksym/s Noise AWGN AWGN -15dB -15dB Carrier Freq. 5KHz 5KHz Freq. Dev. 1KHz 4.1 Feature extraction The generated waveforms have been sampled properly to extract four SSC parameters. Feature extraction steps are as follows: 1. Signal streams are generated for each type of modulation with different SNR. Signal is sampled according to the Nyquist theorem 3. SSC parameters have been extracted for each segment At first 5 segments of 1 samples each were generated to produce the training set, hence the decision tree. Later another 1 segments were generated to test the performance of the classifier. However, in this simulation the training set is limited only for some specific modulation parameters. 7

8 4. Classification Decision tree structures are a common way to organize classification schemes. In classifying tasks, decision trees visualize what steps are taken to arrive at a classification. Every decision tree begins with what is termed a root node, considered to be the parent of every other node. Each node in the tree evaluates an attribute in the data and determines which path it should follow. Typically, the decision test is based on comparing a value against some previously calculated constant. Classification in a decision tree is performed by routing from the root node until arriving at a leaf node. The constant value for each node is calculated based on well known Shanon s information theory. If S = s is a dataset and p and n are elements of classes P and N respectively, then the amount of information bits needed to decide whether a sample in S belongs to P or N I(p, n) = p p + n Log p p + n n p + n Log n p + n If S is partitioned into {S 1, S,..., S v } where v is number of values of attribute A. When S i contains p i and n i samples of class P and N The entropy for attribute A is calculated by the following Equation. v p i + n i E(A) = p + n I(p i, n i ) (8) i=1 Finally, the information gain can be calculated by: Gain(A) = I(p, n) E(A) (9) In the decision tree algorithm the attribute with maximum information gain is selected as the root. Later, rests of the attributes follow the same technique to build the decision tree. In this study the decision tree is generated by using 5 instances of each of the four attribute and saved a a model in WEKA. Afterwards this model is used to classify the unknown data segments. 4.3 Simulation performance Voice signal, PN sequences and different modulations with AWGN were generated in MAT- LAB. The extrema and SSC parameters of the generated signals were also calculated by Matlab. These parameters are used in the WEKA tool to generate the decision tree. Fig. 7 shows the Range of SSC parameters for seven basic modulation classes on 5dB SNR. These parameters have been saved into.arff format (the filetype to import data to WEKA). The performance of the classifier to recognize the target modulation classes is shown in Fig. 8. Performance of the digital modulation in the lower SNR is not so satisfactory. Because the SNR damages the original extrema information as well as the bit information. Performance of the classifier can be increased by improving the input attributes. A threshold based approach to calculate the SSC parameters can be employed for better performance. Finding the appropriate variables and proper algorithm for threshold is one of the future studies. Results of an arbitrarily chosen threshold for digital modulations is shown in Fig (7)

9 Amplitude Amplitude Mean x AM, FM, 3 PM Amplitude deviation Period Period Mean AM, FM, 3 PM Period deviation Amplitude Amplitude Mean Period Period Mean ASK, FSK, 3 BPSK, 4 QPSK 1 ASK, FSK, 3 BPSK, 4 QPSK Amplitude deviation Period deviation 1 15 Deviation 6 4 Deviation 6 4 Deviation.5 Deviation AM, FM, 3 PM 1 AM, FM, 3 PM (a)analog Modulations ASK, FSK, 3 BPSK, 4 QPSK 1 ASK, FSK, 3 BPSK, 4 QPSK (b)digital Modulations Figure 7: Extracted SSC parameters Figure 8: Classification performance 5. Conclusions In this study a sensor network to collect the information of emergency radios in a post disaster scenario has been discussed. The network design is still in the primary stage. Sensed data will be available publicly through a database containing the position and physical attributes of the emergency emitters. Automatic modulation recognition is one of the important tasks for the proposed system. A SSC parameter based decision tree algorithm has been used to recognize the target modulations. The recognition system has shown good performance for analog modulations with lower SNR and in case of digital modulations the performance on lower SNR is still not so satisfactory. However, in this study modulation parameters such as maximum frequency deviation or symbol rates for both training and classification signals were remained unchanged. Accuracy of the algorithm for varying input signals with different modulation parameters are still under investigations. Effects of carrier frequency estimation error with threshold based parameter extraction are also identified as future studies. 9

10 Acknowledgements Figure 9: Classification performance with threshold This study is a part of an ongoing project carried out by a group of 5 students in TokyoTech. The project was started in April 9 to take part in the Smart Radio Challenge 1 [9] organized by the Wireless Innovation forum (SDR forum version.). We acknowledge the support of the forum as well as sponsorship of Mathworks. To uphold the condition of the SPC only the contributions of the first author are presented in this study. We also acknowledge other members who are working on different subsystems, Mr. Azril Haniz (Spectrum sensing), Mr. Iswandi and Mr. Santosh Khadka (Channel modeling) and Mr. Mutsawashe Gahadza (Geolocation). References [1] M. A. Rahman, S. Khadka, Iswandi, G. Mutsawashe, A. Haniz, M. Kim, and J. Takada, Design and implementation of a Cognitive Radio Based Emergency Sensor Network in Disaster Area, IEICE Technical Report, SR9-7, 9. [] A. Hossen, F. Al-Wadahi, J. A. Jervase, Classification of modulation signals using statistical signal characterization and artificial neural networks, Engineering Applications of Artificial Intelligence,, 7, pp [3] E. E. Azzouz, A. K. Nandi, Automatic Modulation Recognition of Communication Signals, Kluwer Academic Publishers, Dordrecht, [4] O. A. Dobre, A. Abdi, Y. Bar-Ness and W. Su, A Survey of Automatic Modulation Classification Techniques: Classical Approaches and New Trends, IET Comunications, 1,, 7, pp [5] H.L. Hirsch, Statistical signal characterization-new help for real-timeprocessing, IEEE Aerospace and Electronics Conference, 1, 199, pp [6] S. B. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques, Informatica, 31, 7, pp [7] R. J. Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Pub. Inc., [8] WEKA Documentation, Available on [9] Smart Radio Challenge 1, Available on 1

3rd Smart Radio Challenge 2009

3rd Smart Radio Challenge 2009 3rd Smart Radio Challenge 2009 Emergency Radio Information System Geolocation Based Cooperative Sensing System to Mitigate Interference in Emergency Communications TokyoTech Team Takada Lab International

More information

Chapter 2 Channel Equalization

Chapter 2 Channel Equalization Chapter 2 Channel Equalization 2.1 Introduction In wireless communication systems signal experiences distortion due to fading [17]. As signal propagates, it follows multiple paths between transmitter and

More information

International Journal of Advance Research in Engineering, Science & Technology. An Automatic Modulation Classifier for signals based on Fuzzy System

International Journal of Advance Research in Engineering, Science & Technology. An Automatic Modulation Classifier for signals based on Fuzzy System Impact Factor (SJIF): 3.632 International Journal of Advance Research in Engineering, Science & Technology e-issn: 2393-9877, p-issn: 2394-2444 Volume 3, Issue 5, May-2016 An Automatic Modulation Classifier

More information

AUTOMATIC MODULATION RECOGNITION OF COMMUNICATION SIGNALS

AUTOMATIC MODULATION RECOGNITION OF COMMUNICATION SIGNALS エシアンゾロナルオフネチュラルアンドアプライヅサエニセズ ISSN: 2186-8476, ISSN: 2186-8468 Print AUTOMATIC MODULATION RECOGNITION OF COMMUNICATION SIGNALS Muazzam Ali Khan 1, Maqsood Muhammad Khan 2, Muhammad Saad Khan 3 1 Blekinge

More information

Online Large Margin Semi-supervised Algorithm for Automatic Classification of Digital Modulations

Online Large Margin Semi-supervised Algorithm for Automatic Classification of Digital Modulations Online Large Margin Semi-supervised Algorithm for Automatic Classification of Digital Modulations Hamidreza Hosseinzadeh*, Farbod Razzazi**, and Afrooz Haghbin*** Department of Electrical and Computer

More information

Frequency Hopping Spread Spectrum Recognition Based on Discrete Fourier Transform and Skewness and Kurtosis

Frequency Hopping Spread Spectrum Recognition Based on Discrete Fourier Transform and Skewness and Kurtosis Frequency Hopping Spread Spectrum Recognition Based on Discrete Fourier Transform and Skewness and Kurtosis Hadi Athab Hamed 1, Ahmed Kareem Abdullah 2 and Sara Al-waisawy 3 1,2,3 Al-Furat Al-Awsat Technical

More information

A Novel Technique for Automatic Modulation Classification and Time-Frequency Analysis of Digitally Modulated Signals

A Novel Technique for Automatic Modulation Classification and Time-Frequency Analysis of Digitally Modulated Signals Vol. 6, No., April, 013 A Novel Technique for Automatic Modulation Classification and Time-Frequency Analysis of Digitally Modulated Signals M. V. Subbarao, N. S. Khasim, T. Jagadeesh, M. H. H. Sastry

More information

Digital Modulation Recognition Based on Feature, Spectrum and Phase Analysis and its Testing with Disturbed Signals

Digital Modulation Recognition Based on Feature, Spectrum and Phase Analysis and its Testing with Disturbed Signals Digital Modulation Recognition Based on Feature, Spectrum and Phase Analysis and its Testing with Disturbed Signals A. KUBANKOVA AND D. KUBANEK Department of Telecommunications Brno University of Technology

More information

DESIGN AND IMPLEMENTATION OF AN ALGORITHM FOR MODULATION IDENTIFICATION OF ANALOG AND DIGITAL SIGNALS

DESIGN AND IMPLEMENTATION OF AN ALGORITHM FOR MODULATION IDENTIFICATION OF ANALOG AND DIGITAL SIGNALS DESIGN AND IMPLEMENTATION OF AN ALGORITHM FOR MODULATION IDENTIFICATION OF ANALOG AND DIGITAL SIGNALS John Yong Jia Chen (Department of Electrical Engineering, San José State University, San José, California,

More information

Modulation Classification based on Modified Kolmogorov-Smirnov Test

Modulation Classification based on Modified Kolmogorov-Smirnov Test Modulation Classification based on Modified Kolmogorov-Smirnov Test Ali Waqar Azim, Syed Safwan Khalid, Shafayat Abrar ENSIMAG, Institut Polytechnique de Grenoble, 38406, Grenoble, France Email: ali-waqar.azim@ensimag.grenoble-inp.fr

More information

Geolocation Based Cooperative Sensing System to Mitigate Interference in Emergency Communications Smart Radio Challenge. Md.

Geolocation Based Cooperative Sensing System to Mitigate Interference in Emergency Communications Smart Radio Challenge. Md. 1 / 31 Based Cooperative Sensing System to Mitigate Interference in Emergency Communications -07-02 2 / 31 SDR Forum overview problems Our Targets SDR Forum 3 / 31 Established in 1996 A non-profit international

More information

A Novel Technique for Automatic Modulation Classification and Time- Frequency Analysis of Digitally Modulated Signals

A Novel Technique for Automatic Modulation Classification and Time- Frequency Analysis of Digitally Modulated Signals A Novel Technique for Automatic Modulation Classification and Time- Frequency Analysis of Digitally Modulated Signals M. Venkata Subbarao, Sayedu Khasim Noorbasha, Jagadeesh Thati 3,,3 Asst. Professor,

More information

Novel Automatic Modulation Classification using Correntropy Coefficient

Novel Automatic Modulation Classification using Correntropy Coefficient Novel Automatic Modulation Classification using Correntropy Coefficient Aluisio I. R. Fontes, Lucas C. P. Cavalcante and Luiz F. Q. Silveira Abstract This paper deals with automatic modulation classification

More information

Overview. Cognitive Radio: Definitions. Cognitive Radio. Multidimensional Spectrum Awareness: Radio Space

Overview. Cognitive Radio: Definitions. Cognitive Radio. Multidimensional Spectrum Awareness: Radio Space Overview A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications Tevfik Yucek and Huseyin Arslan Cognitive Radio Multidimensional Spectrum Awareness Challenges Spectrum Sensing Methods

More information

Tejashri Kuber ALL RIGHTS RESERVED

Tejashri Kuber ALL RIGHTS RESERVED 2013 Tejashri Kuber ALL RIGHTS RESERVED AUTOMATIC MODULATION RECOGNITION USING THE DISCRETE WAVELET TRANSFORM By TEJASHRI KUBER A thesis submitted to the Graduate School-New Brunswick Rutgers, The State

More information

Performance Evaluation of STBC-OFDM System for Wireless Communication

Performance Evaluation of STBC-OFDM System for Wireless Communication Performance Evaluation of STBC-OFDM System for Wireless Communication Apeksha Deshmukh, Prof. Dr. M. D. Kokate Department of E&TC, K.K.W.I.E.R. College, Nasik, apeksha19may@gmail.com Abstract In this paper

More information

OFDM MODULATED SIGNALS BASED ON STATISTICAL PARAMETERS

OFDM MODULATED SIGNALS BASED ON STATISTICAL PARAMETERS OFDM MODULATED SIGNALS BASED ON STATISTICAL PARAMETERS 1 S SUBRAHMANYA SASTRY, 2 K.RAJU, 3 DR.M.CHANDRASEKHAR 1 Ph.D student in Rayalaseema University-Kurnool & Assoc Prof in Malla Reddy Engineering College

More information

CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES

CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES Proceedings of the SDR 11 Technical Conference and Product Exposition, Copyright 2011 Wireless Innovation Forum All Rights Reserved CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY

More information

Effect of Time Bandwidth Product on Cooperative Communication

Effect of Time Bandwidth Product on Cooperative Communication Surendra Kumar Singh & Rekha Gupta Department of Electronics and communication Engineering, MITS Gwalior E-mail : surendra886@gmail.com, rekha652003@yahoo.com Abstract Cognitive radios are proposed to

More information

Msc Engineering Physics (6th academic year) Royal Institute of Technology, Stockholm August December 2003

Msc Engineering Physics (6th academic year) Royal Institute of Technology, Stockholm August December 2003 Msc Engineering Physics (6th academic year) Royal Institute of Technology, Stockholm August 2002 - December 2003 1 2E1511 - Radio Communication (6 ECTS) The course provides basic knowledge about models

More information

RESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS

RESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS Abstract of Doctorate Thesis RESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS PhD Coordinator: Prof. Dr. Eng. Radu MUNTEANU Author: Radu MITRAN

More information

NEW METHODS FOR CLASSIFICATION OF CPM AND SPREAD SPECTRUM COMMUNICATIONS SIGNALS

NEW METHODS FOR CLASSIFICATION OF CPM AND SPREAD SPECTRUM COMMUNICATIONS SIGNALS NEW METHODS FOR CLASSIFICATION OF CPM AND SPREAD SPECTRUM COMMUNICATIONS SIGNALS VIS RAMAKONAR, DARYOUSH HABIBI, ABDESSELAM BOUZERDOUM School of Engineering and Mathematics Edith Cowan University 100 Joondalup

More information

Design and FPGA Implementation of an Adaptive Demodulator. Design and FPGA Implementation of an Adaptive Demodulator

Design and FPGA Implementation of an Adaptive Demodulator. Design and FPGA Implementation of an Adaptive Demodulator Design and FPGA Implementation of an Adaptive Demodulator Sandeep Mukthavaram August 23, 1999 Thesis Defense for the Degree of Master of Science in Electrical Engineering Department of Electrical Engineering

More information

Automatic Digital Modulation Classification Using Genetic Programming with K-Nearest Neighbor

Automatic Digital Modulation Classification Using Genetic Programming with K-Nearest Neighbor The 21 Military Communications Conference - Unclassified Program - Waveforms and Signal Processing Track Automatic Digital Modulation Classification Using Genetic Programming with K-Nearest Neighbor Muhammad

More information

CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES

CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES CLASSIFICATION OF MULTIPLE SIGNALS USING 2D MATCHING OF MAGNITUDE-FREQUENCY DENSITY FEATURES Aaron Roof (Vanteon Corporation, Fairport, NY; aroof@vanteon.com); Adly Fam (Dept. of Electrical Engineering,

More information

Cognitive Radio: Smart Use of Radio Spectrum

Cognitive Radio: Smart Use of Radio Spectrum Cognitive Radio: Smart Use of Radio Spectrum Miguel López-Benítez Department of Electrical Engineering and Electronics University of Liverpool, United Kingdom M.Lopez-Benitez@liverpool.ac.uk www.lopezbenitez.es,

More information

Year : TYEJ Sub: Digital Communication (17535) Assignment No. 1. Introduction of Digital Communication. Question Exam Marks

Year : TYEJ Sub: Digital Communication (17535) Assignment No. 1. Introduction of Digital Communication. Question Exam Marks Assignment 1 Introduction of Digital Communication Sr. Question Exam Marks 1 Draw the block diagram of the basic digital communication system. State the function of each block in detail. W 2015 6 2 State

More information

Nonlinear Companding Transform Algorithm for Suppression of PAPR in OFDM Systems

Nonlinear Companding Transform Algorithm for Suppression of PAPR in OFDM Systems Nonlinear Companding Transform Algorithm for Suppression of PAPR in OFDM Systems P. Guru Vamsikrishna Reddy 1, Dr. C. Subhas 2 1 Student, Department of ECE, Sree Vidyanikethan Engineering College, Andhra

More information

AIR FORCE INSTITUTE OF TECHNOLOGY

AIR FORCE INSTITUTE OF TECHNOLOGY MODIFICATION OF A MODULATION RECOGNITION ALGORITHM TO ENABLE MULTI-CARRIER RECOGNITION THESIS Angela M. Waters, Second Lieutenant, USAF AFIT/GE/ENG/5-23 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE

More information

Lab 3.0. Pulse Shaping and Rayleigh Channel. Faculty of Information Engineering & Technology. The Communications Department

Lab 3.0. Pulse Shaping and Rayleigh Channel. Faculty of Information Engineering & Technology. The Communications Department Faculty of Information Engineering & Technology The Communications Department Course: Advanced Communication Lab [COMM 1005] Lab 3.0 Pulse Shaping and Rayleigh Channel 1 TABLE OF CONTENTS 2 Summary...

More information

S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY

S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY VISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY A seminar report on Orthogonal Frequency Division Multiplexing (OFDM) Submitted by Sandeep Katakol 2SD06CS085 8th semester

More information

A Novel Technique or Blind Bandwidth Estimation of the Radio Communication Signal

A Novel Technique or Blind Bandwidth Estimation of the Radio Communication Signal International Journal of ISSN 0974-2107 Systems and Technologies IJST Vol.3, No.1, pp 11-16 KLEF 2010 A Novel Technique or Blind Bandwidth Estimation of the Radio Communication Signal Gaurav Lohiya 1,

More information

CHAPTER 5 DIVERSITY. Xijun Wang

CHAPTER 5 DIVERSITY. Xijun Wang CHAPTER 5 DIVERSITY Xijun Wang WEEKLY READING 1. Goldsmith, Wireless Communications, Chapters 7 2. Tse, Fundamentals of Wireless Communication, Chapter 3 2 FADING HURTS THE RELIABILITY n The detection

More information

OFDM Transmission Corrupted by Impulsive Noise

OFDM Transmission Corrupted by Impulsive Noise OFDM Transmission Corrupted by Impulsive Noise Jiirgen Haring, Han Vinck University of Essen Institute for Experimental Mathematics Ellernstr. 29 45326 Essen, Germany,. e-mail: haering@exp-math.uni-essen.de

More information

Key words: OFDM, FDM, BPSK, QPSK.

Key words: OFDM, FDM, BPSK, QPSK. Volume 4, Issue 3, March 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analyse the Performance

More information

UNIT- 7. Frequencies above 30Mhz tend to travel in straight lines they are limited in their propagation by the curvature of the earth.

UNIT- 7. Frequencies above 30Mhz tend to travel in straight lines they are limited in their propagation by the curvature of the earth. UNIT- 7 Radio wave propagation and propagation models EM waves below 2Mhz tend to travel as ground waves, These wave tend to follow the curvature of the earth and lose strength rapidly as they travel away

More information

Mel Spectrum Analysis of Speech Recognition using Single Microphone

Mel Spectrum Analysis of Speech Recognition using Single Microphone International Journal of Engineering Research in Electronics and Communication Mel Spectrum Analysis of Speech Recognition using Single Microphone [1] Lakshmi S.A, [2] Cholavendan M [1] PG Scholar, Sree

More information

Design and Analysis of New Digital Modulation classification method

Design and Analysis of New Digital Modulation classification method Design and Analysis of New Digital Modulation classification method ANNA KUBANKOVA Department of Telecommunications Brno University of Technology Purkynova 118, 612 00 Brno CZECH REPUBLIC shklya@feec.vutbr.cz

More information

Study of Performance Evaluation of Quasi Orthogonal Space Time Block Code MIMO-OFDM System in Rician Channel for Different Modulation Schemes

Study of Performance Evaluation of Quasi Orthogonal Space Time Block Code MIMO-OFDM System in Rician Channel for Different Modulation Schemes Volume 4, Issue 6, June (016) Study of Performance Evaluation of Quasi Orthogonal Space Time Block Code MIMO-OFDM System in Rician Channel for Different Modulation Schemes Pranil S Mengane D. Y. Patil

More information

Higher Order Cummulants based Digital Modulation Recognition Scheme

Higher Order Cummulants based Digital Modulation Recognition Scheme Research Journal of Applied Sciences, Engineering and Technology 6(20): 3910-3915, 2013 ISSN: 2040-7459; e-issn: 2040-7467 Maxwell Scientific Organization, 2013 Submitted: April 04, 2013 Accepted: April

More information

ON FEATURE BASED AUTOMATIC CLASSIFICATION OF SINGLE AND MULTITONE SIGNALS

ON FEATURE BASED AUTOMATIC CLASSIFICATION OF SINGLE AND MULTITONE SIGNALS ON FEATURE BASED AUTOMATIC CLASSIFICATION OF SINGLE AND MULTITONE SIGNALS Arindam K. Das, Payman Arabshahi, Tim Wen Applied Physics Laboratory University of Washington, Box 355640, Seattle, WA 9895, USA.

More information

Classification of Analog Modulated Communication Signals using Clustering Techniques: A Comparative Study

Classification of Analog Modulated Communication Signals using Clustering Techniques: A Comparative Study F. Ü. Fen ve Mühendislik Bilimleri Dergisi, 7 (), 47-56, 005 Classification of Analog Modulated Communication Signals using Clustering Techniques: A Comparative Study Hanifi GULDEMIR Abdulkadir SENGUR

More information

An Indoor Localization System Based on DTDOA for Different Wireless LAN Systems. 1 Principles of differential time difference of arrival (DTDOA)

An Indoor Localization System Based on DTDOA for Different Wireless LAN Systems. 1 Principles of differential time difference of arrival (DTDOA) An Indoor Localization System Based on DTDOA for Different Wireless LAN Systems F. WINKLER 1, E. FISCHER 2, E. GRASS 3, P. LANGENDÖRFER 3 1 Humboldt University Berlin, Germany, e-mail: fwinkler@informatik.hu-berlin.de

More information

Outline. Communications Engineering 1

Outline. Communications Engineering 1 Outline Introduction Signal, random variable, random process and spectra Analog modulation Analog to digital conversion Digital transmission through baseband channels Signal space representation Optimal

More information

QUESTION BANK SUBJECT: DIGITAL COMMUNICATION (15EC61)

QUESTION BANK SUBJECT: DIGITAL COMMUNICATION (15EC61) QUESTION BANK SUBJECT: DIGITAL COMMUNICATION (15EC61) Module 1 1. Explain Digital communication system with a neat block diagram. 2. What are the differences between digital and analog communication systems?

More information

Performance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels

Performance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels Performance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels Abstract A Orthogonal Frequency Division Multiplexing (OFDM) scheme offers high spectral efficiency and better resistance to

More information

EITN85, FREDRIK TUFVESSON, JOHAN KÅREDAL ELECTRICAL AND INFORMATION TECHNOLOGY. Why do we need UWB channel models?

EITN85, FREDRIK TUFVESSON, JOHAN KÅREDAL ELECTRICAL AND INFORMATION TECHNOLOGY. Why do we need UWB channel models? Wireless Communication Channels Lecture 9:UWB Channel Modeling EITN85, FREDRIK TUFVESSON, JOHAN KÅREDAL ELECTRICAL AND INFORMATION TECHNOLOGY Overview What is Ultra-Wideband (UWB)? Why do we need UWB channel

More information

(i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods

(i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods Tools and Applications Chapter Intended Learning Outcomes: (i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods

More information

Digital Communication Systems Engineering with

Digital Communication Systems Engineering with Digital Communication Systems Engineering with Software-Defined Radio Di Pu Alexander M. Wyglinski ARTECH HOUSE BOSTON LONDON artechhouse.com Contents Preface xiii What Is an SDR? 1 1.1 Historical Perspective

More information

IT S A COMPLEX WORLD RADAR DEINTERLEAVING. Philip Wilson. Slipstream Engineering Design Ltd.

IT S A COMPLEX WORLD RADAR DEINTERLEAVING. Philip Wilson. Slipstream Engineering Design Ltd. IT S A COMPLEX WORLD RADAR DEINTERLEAVING Philip Wilson pwilson@slipstream-design.co.uk Abstract In this paper, we will look at how digital radar streams of pulse descriptor words are sorted by deinterleaving

More information

Performance Analysis of n Wireless LAN Physical Layer

Performance Analysis of n Wireless LAN Physical Layer 120 1 Performance Analysis of 802.11n Wireless LAN Physical Layer Amr M. Otefa, Namat M. ElBoghdadly, and Essam A. Sourour Abstract In the last few years, we have seen an explosive growth of wireless LAN

More information

Performance Improvement of Wireless Communications Using Frequency Hopping Spread Spectrum

Performance Improvement of Wireless Communications Using Frequency Hopping Spread Spectrum Int. J. Communications, Network and System Sciences, 010, 3, 805-810 doi:10.436/ijcns.010.310108 Published Online October 010 (http://www.scirp.org/journal/ijcns) Performance Improvement of Wireless Communications

More information

AN INVESTIGATION INTO SALIENCY-BASED MARS ROI DETECTION

AN INVESTIGATION INTO SALIENCY-BASED MARS ROI DETECTION AN INVESTIGATION INTO SALIENCY-BASED MARS ROI DETECTION Lilan Pan and Dave Barnes Department of Computer Science, Aberystwyth University, UK ABSTRACT This paper reviews several bottom-up saliency algorithms.

More information

OFDM AS AN ACCESS TECHNIQUE FOR NEXT GENERATION NETWORK

OFDM AS AN ACCESS TECHNIQUE FOR NEXT GENERATION NETWORK OFDM AS AN ACCESS TECHNIQUE FOR NEXT GENERATION NETWORK Akshita Abrol Department of Electronics & Communication, GCET, Jammu, J&K, India ABSTRACT With the rapid growth of digital wireless communication

More information

BLIND SIGNAL PARAMETER ESTIMATION FOR THE RAPID RADIO FRAMEWORK

BLIND SIGNAL PARAMETER ESTIMATION FOR THE RAPID RADIO FRAMEWORK BLIND SIGNAL PARAMETER ESTIMATION FOR THE RAPID RADIO FRAMEWORK Adolfo Recio, Jorge Surís, and Peter Athanas {recio; jasuris; athanas}@vt.edu Virginia Tech Bradley Department of Electrical and Computer

More information

Cognitive Radio Techniques

Cognitive Radio Techniques Cognitive Radio Techniques Spectrum Sensing, Interference Mitigation, and Localization Kandeepan Sithamparanathan Andrea Giorgetti ARTECH HOUSE BOSTON LONDON artechhouse.com Contents Preface xxi 1 Introduction

More information

DESIGN, IMPLEMENTATION AND OPTIMISATION OF 4X4 MIMO-OFDM TRANSMITTER FOR

DESIGN, IMPLEMENTATION AND OPTIMISATION OF 4X4 MIMO-OFDM TRANSMITTER FOR DESIGN, IMPLEMENTATION AND OPTIMISATION OF 4X4 MIMO-OFDM TRANSMITTER FOR COMMUNICATION SYSTEMS Abstract M. Chethan Kumar, *Sanket Dessai Department of Computer Engineering, M.S. Ramaiah School of Advanced

More information

MODELLING AND SIMULATION OF LOCAL AREA WIRELESS CHANNELS FOR WLAN PERFORMANCE ANALYSIS

MODELLING AND SIMULATION OF LOCAL AREA WIRELESS CHANNELS FOR WLAN PERFORMANCE ANALYSIS MODELLING AND SIMULATION OF LOCAL AREA WIRELESS CHANNELS FOR WLAN PERFORMANCE ANALYSIS Simmi Dutta, Assistant Professor Computer Engineering Deptt., Govt. College of Engg. & Tech., Jammu. Email: simmi_dutta@rediffmail.com;

More information

Implementation of Cognitive Radio Networks Based on Cooperative Spectrum Sensing Optimization

Implementation of Cognitive Radio Networks Based on Cooperative Spectrum Sensing Optimization www.semargroups.org, www.ijsetr.com ISSN 2319-8885 Vol.02,Issue.11, September-2013, Pages:1085-1091 Implementation of Cognitive Radio Networks Based on Cooperative Spectrum Sensing Optimization D.TARJAN

More information

TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY

TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY 2 Basic Definitions Time and Frequency db conversion Power and dbm Filter Basics 3 Filter Filter is a component with frequency

More information

Objectives. Presentation Outline. Digital Modulation Revision

Objectives. Presentation Outline. Digital Modulation Revision Digital Modulation Revision Professor Richard Harris Objectives To identify the key points from the lecture material presented in the Digital Modulation section of this paper. What is in the examination

More information

Chapter- 5. Performance Evaluation of Conventional Handoff

Chapter- 5. Performance Evaluation of Conventional Handoff Chapter- 5 Performance Evaluation of Conventional Handoff Chapter Overview This chapter immensely compares the different mobile phone technologies (GSM, UMTS and CDMA). It also presents the related results

More information

1 Interference Cancellation

1 Interference Cancellation Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.829 Fall 2017 Problem Set 1 September 19, 2017 This problem set has 7 questions, each with several parts.

More information

COMMUNICATION SYSTEMS

COMMUNICATION SYSTEMS COMMUNICATION SYSTEMS 4TH EDITION Simon Hayhin McMaster University JOHN WILEY & SONS, INC. Ш.! [ BACKGROUND AND PREVIEW 1. The Communication Process 1 2. Primary Communication Resources 3 3. Sources of

More information

OFDM Systems For Different Modulation Technique

OFDM Systems For Different Modulation Technique Computing For Nation Development, February 08 09, 2008 Bharati Vidyapeeth s Institute of Computer Applications and Management, New Delhi OFDM Systems For Different Modulation Technique Mrs. Pranita N.

More information

Performance Evaluation of a UWB Channel Model with Antipodal, Orthogonal and DPSK Modulation Scheme

Performance Evaluation of a UWB Channel Model with Antipodal, Orthogonal and DPSK Modulation Scheme International Journal of Wired and Wireless Communications Vol 4, Issue April 016 Performance Evaluation of 80.15.3a UWB Channel Model with Antipodal, Orthogonal and DPSK Modulation Scheme Sachin Taran

More information

Doppler Frequency Effect on Network Throughput Using Transmit Diversity

Doppler Frequency Effect on Network Throughput Using Transmit Diversity International Journal of Sciences: Basic and Applied Research (IJSBAR) ISSN 2307-4531 (Print & Online) http://gssrr.org/index.php?journal=journalofbasicandapplied ---------------------------------------------------------------------------------------------------------------------------

More information

Part 3. Multiple Access Methods. p. 1 ELEC6040 Mobile Radio Communications, Dept. of E.E.E., HKU

Part 3. Multiple Access Methods. p. 1 ELEC6040 Mobile Radio Communications, Dept. of E.E.E., HKU Part 3. Multiple Access Methods p. 1 ELEC6040 Mobile Radio Communications, Dept. of E.E.E., HKU Review of Multiple Access Methods Aim of multiple access To simultaneously support communications between

More information

UWB Channel Modeling

UWB Channel Modeling Channel Modeling ETIN10 Lecture no: 9 UWB Channel Modeling Fredrik Tufvesson & Johan Kåredal, Department of Electrical and Information Technology fredrik.tufvesson@eit.lth.se 2011-02-21 Fredrik Tufvesson

More information

Lecture 3: Wireless Physical Layer: Modulation Techniques. Mythili Vutukuru CS 653 Spring 2014 Jan 13, Monday

Lecture 3: Wireless Physical Layer: Modulation Techniques. Mythili Vutukuru CS 653 Spring 2014 Jan 13, Monday Lecture 3: Wireless Physical Layer: Modulation Techniques Mythili Vutukuru CS 653 Spring 2014 Jan 13, Monday Modulation We saw a simple example of amplitude modulation in the last lecture Modulation how

More information

Estimation of speed, average received power and received signal in wireless systems using wavelets

Estimation of speed, average received power and received signal in wireless systems using wavelets Estimation of speed, average received power and received signal in wireless systems using wavelets Rajat Bansal Sumit Laad Group Members rajat@ee.iitb.ac.in laad@ee.iitb.ac.in 01D07010 01D07011 Abstract

More information

Mobile & Wireless Networking. Lecture 2: Wireless Transmission (2/2)

Mobile & Wireless Networking. Lecture 2: Wireless Transmission (2/2) 192620010 Mobile & Wireless Networking Lecture 2: Wireless Transmission (2/2) [Schiller, Section 2.6 & 2.7] [Reader Part 1: OFDM: An architecture for the fourth generation] Geert Heijenk Outline of Lecture

More information

NEURAL NETWORK DEMODULATOR FOR QUADRATURE AMPLITUDE MODULATION (QAM)

NEURAL NETWORK DEMODULATOR FOR QUADRATURE AMPLITUDE MODULATION (QAM) NEURAL NETWORK DEMODULATOR FOR QUADRATURE AMPLITUDE MODULATION (QAM) Ahmed Nasraden Milad M. Aziz M Rahmadwati Artificial neural network (ANN) is one of the most advanced technology fields, which allows

More information

Channel Modeling ETI 085

Channel Modeling ETI 085 Channel Modeling ETI 085 Overview Lecture no: 9 What is Ultra-Wideband (UWB)? Why do we need UWB channel models? UWB Channel Modeling UWB channel modeling Standardized UWB channel models Fredrik Tufvesson

More information

Downloaded from 1

Downloaded from  1 VII SEMESTER FINAL EXAMINATION-2004 Attempt ALL questions. Q. [1] How does Digital communication System differ from Analog systems? Draw functional block diagram of DCS and explain the significance of

More information

CHAPTER 3 ADAPTIVE MODULATION TECHNIQUE WITH CFO CORRECTION FOR OFDM SYSTEMS

CHAPTER 3 ADAPTIVE MODULATION TECHNIQUE WITH CFO CORRECTION FOR OFDM SYSTEMS 44 CHAPTER 3 ADAPTIVE MODULATION TECHNIQUE WITH CFO CORRECTION FOR OFDM SYSTEMS 3.1 INTRODUCTION A unique feature of the OFDM communication scheme is that, due to the IFFT at the transmitter and the FFT

More information

Satellite Communications: Part 4 Signal Distortions & Errors and their Relation to Communication Channel Specifications. Howard Hausman April 1, 2010

Satellite Communications: Part 4 Signal Distortions & Errors and their Relation to Communication Channel Specifications. Howard Hausman April 1, 2010 Satellite Communications: Part 4 Signal Distortions & Errors and their Relation to Communication Channel Specifications Howard Hausman April 1, 2010 Satellite Communications: Part 4 Signal Distortions

More information

ETSI Standards and the Measurement of RF Conducted Output Power of Wi-Fi ac Signals

ETSI Standards and the Measurement of RF Conducted Output Power of Wi-Fi ac Signals ETSI Standards and the Measurement of RF Conducted Output Power of Wi-Fi 802.11ac Signals Introduction The European Telecommunications Standards Institute (ETSI) have recently introduced a revised set

More information

BER ANALYSIS OF WiMAX IN MULTIPATH FADING CHANNELS

BER ANALYSIS OF WiMAX IN MULTIPATH FADING CHANNELS BER ANALYSIS OF WiMAX IN MULTIPATH FADING CHANNELS Navgeet Singh 1, Amita Soni 2 1 P.G. Scholar, Department of Electronics and Electrical Engineering, PEC University of Technology, Chandigarh, India 2

More information

Wireless Networks (PHY): Design for Diversity

Wireless Networks (PHY): Design for Diversity Wireless Networks (PHY): Design for Diversity Y. Richard Yang 9/20/2012 Outline Admin and recap Design for diversity 2 Admin Assignment 1 questions Assignment 1 office hours Thursday 3-4 @ AKW 307A 3 Recap:

More information

Goriparthi Venkateswara Rao, K.Rushendra Babu, Sumit Kumar

Goriparthi Venkateswara Rao, K.Rushendra Babu, Sumit Kumar International Journal of Scientific & Engineering Research, Volume 5, Issue 10, October-2014 935 Performance comparison of IEEE802.11a Standard in Mobile Environment Goriparthi Venkateswara Rao, K.Rushendra

More information

Combining techniques graphical representation of bit error rate performance used in mitigating fading in global system for mobile communication (GSM)

Combining techniques graphical representation of bit error rate performance used in mitigating fading in global system for mobile communication (GSM) JEMT 5 (2017) 1-7 ISSN 2053-3535 Combining techniques graphical representation of bit error rate performance used in mitigating fading in global system for mobile communication (GSM) Awofolaju T. T.* and

More information

Performance Evaluation of Wireless Communication System Employing DWT-OFDM using Simulink Model

Performance Evaluation of Wireless Communication System Employing DWT-OFDM using Simulink Model Performance Evaluation of Wireless Communication System Employing DWT-OFDM using Simulink Model M. Prem Anand 1 Rudrashish Roy 2 1 Assistant Professor 2 M.E Student 1,2 Department of Electronics & Communication

More information

Performance analysis of OFDM with QPSK using AWGN and Rayleigh Fading Channel

Performance analysis of OFDM with QPSK using AWGN and Rayleigh Fading Channel Performance analysis of OFDM with QPSK using AWGN and Rayleigh Fading Channel 1 V.R.Prakash* (A.P) Department of ECE Hindustan university Chennai 2 P.Kumaraguru**(A.P) Department of ECE Hindustan university

More information

MIMO-Based Vehicle Positioning System for Vehicular Networks

MIMO-Based Vehicle Positioning System for Vehicular Networks MIMO-Based Vehicle Positioning System for Vehicular Networks Abduladhim Ashtaiwi* Computer Networks Department College of Information and Technology University of Tripoli Libya. * Corresponding author.

More information

Presented at IEICE TR (AP )

Presented at IEICE TR (AP ) Sounding Presented at IEICE TR (AP 2007-02) MIMO Radio Seminar, Mobile Communications Research Group 07 June 2007 Takada Laboratory Department of International Development Engineering Graduate School of

More information

Bit Error Rate Assessment of Digital Modulation Schemes on Additive White Gaussian Noise, Line of Sight and Non Line of Sight Fading Channels

Bit Error Rate Assessment of Digital Modulation Schemes on Additive White Gaussian Noise, Line of Sight and Non Line of Sight Fading Channels International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 3 Issue 8 ǁ August 2014 ǁ PP.06-10 Bit Error Rate Assessment of Digital Modulation Schemes

More information

Cooperative Spectrum Sensing and Decision Making Rules for Cognitive Radio

Cooperative Spectrum Sensing and Decision Making Rules for Cognitive Radio ISSN (Online) : 2319-8753 ISSN (Print) : 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014 2014 International Conference

More information

Cognitive multi-mode and multi-standard base stations: architecture and system analysis

Cognitive multi-mode and multi-standard base stations: architecture and system analysis Cognitive multi-mode and multi-standard base stations: architecture and system analysis C. Armani Selex Elsag, Italy; claudio.armani@selexelsag.com R. Giuliano University of Rome Tor Vergata, Italy; romeo.giuliano@uniroma2.it

More information

Amplitude and Phase Distortions in MIMO and Diversity Systems

Amplitude and Phase Distortions in MIMO and Diversity Systems Amplitude and Phase Distortions in MIMO and Diversity Systems Christiane Kuhnert, Gerd Saala, Christian Waldschmidt, Werner Wiesbeck Institut für Höchstfrequenztechnik und Elektronik (IHE) Universität

More information

TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY

TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY TSEK02: Radio Electronics Lecture 2: Modulation (I) Ted Johansson, EKS, ISY An Overview of Modulation Techniques: chapter 3.1 3.3.1 2 Introduction (3.1) Analog Modulation Amplitude Modulation Phase and

More information

Simulation Study and Performance Comparison of OFDM System with QPSK and BPSK

Simulation Study and Performance Comparison of OFDM System with QPSK and BPSK Simulation Study and Performance Comparison of OFDM System with QPSK and BPSK 1 Mr. Adesh Kumar, 2 Mr. Sudeep Singh, 3 Mr. Shashank, 4 Asst. Prof. Mr. Kuldeep Sharma (Guide) M. Tech (EC), Monad University,

More information

UNIFIED DIGITAL AUDIO AND DIGITAL VIDEO BROADCASTING SYSTEM USING ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING (OFDM) SYSTEM

UNIFIED DIGITAL AUDIO AND DIGITAL VIDEO BROADCASTING SYSTEM USING ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING (OFDM) SYSTEM UNIFIED DIGITAL AUDIO AND DIGITAL VIDEO BROADCASTING SYSTEM USING ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING (OFDM) SYSTEM 1 Drakshayini M N, 2 Dr. Arun Vikas Singh 1 drakshayini@tjohngroup.com, 2 arunsingh@tjohngroup.com

More information

Optimized BPSK and QAM Techniques for OFDM Systems

Optimized BPSK and QAM Techniques for OFDM Systems I J C T A, 9(6), 2016, pp. 2759-2766 International Science Press ISSN: 0974-5572 Optimized BPSK and QAM Techniques for OFDM Systems Manikandan J.* and M. Manikandan** ABSTRACT A modulation is a process

More information

Physical Layer: Outline

Physical Layer: Outline 18-345: Introduction to Telecommunication Networks Lectures 3: Physical Layer Peter Steenkiste Spring 2015 www.cs.cmu.edu/~prs/nets-ece Physical Layer: Outline Digital networking Modulation Characterization

More information

2. LITERATURE REVIEW

2. LITERATURE REVIEW 2. LITERATURE REVIEW In this section, a brief review of literature on Performance of Antenna Diversity Techniques, Alamouti Coding Scheme, WiMAX Broadband Wireless Access Technology, Mobile WiMAX Technology,

More information

Performance Study of MIMO-OFDM System in Rayleigh Fading Channel with QO-STB Coding Technique

Performance Study of MIMO-OFDM System in Rayleigh Fading Channel with QO-STB Coding Technique e-issn 2455 1392 Volume 2 Issue 6, June 2016 pp. 190 197 Scientific Journal Impact Factor : 3.468 http://www.ijcter.com Performance Study of MIMO-OFDM System in Rayleigh Fading Channel with QO-STB Coding

More information

Proceedings Statistical Evaluation of the Positioning Error in Sequential Localization Techniques for Sensor Networks

Proceedings Statistical Evaluation of the Positioning Error in Sequential Localization Techniques for Sensor Networks Proceedings Statistical Evaluation of the Positioning Error in Sequential Localization Techniques for Sensor Networks Cesar Vargas-Rosales *, Yasuo Maidana, Rafaela Villalpando-Hernandez and Leyre Azpilicueta

More information

Orthogonal Frequency Division Multiplexing (OFDM) based Uplink Multiple Access Method over AWGN and Fading Channels

Orthogonal Frequency Division Multiplexing (OFDM) based Uplink Multiple Access Method over AWGN and Fading Channels Orthogonal Frequency Division Multiplexing (OFDM) based Uplink Multiple Access Method over AWGN and Fading Channels Prashanth G S 1 1Department of ECE, JNNCE, Shivamogga ---------------------------------------------------------------------***----------------------------------------------------------------------

More information

Evaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel

Evaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel ISSN (Online): 2409-4285 www.ijcsse.org Page: 1-7 Evaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel Lien Pham Hong 1, Quang Nguyen Duc 2, Dung

More information