scholarly journals Autocorrelation Method for Cyclic Prefix OFDM Estimation

2019 ◽  
Vol 3 (3) ◽  
pp. 91
Author(s):  
Desti Madya Saputri
Keyword(s):  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Correlation Method ◽  
Cyclic Prefix ◽  
Service Needs ◽  
Method Performance ◽  
Radio System ◽  
Signal Peak ◽  
Symbol Duration ◽  
Autocorrelation Method

A radio system design providing various data service needs becomes one of the Software Defined Radio (SDR) system advantages. SDR technology applies software functions further to be run in hardware platforms. The need for services with greater data rates can be resolved by using multi-carrier transmission techniques, one of which is the Orthogonal Frequency Division Multiplexing (OFDM) technique. This paper discusses the detection of OFDM signals and their parameters. Multi-carrier transmission can prevent Inter-Symbol Interference (ISI) occurrence due to multi-path fading effect. The recognition can classify the correctly received signals, including the signal conditions mixed with AWGN noise. The autocorrelation method was used to estimate the OFDM parameters, namely the one symbol duration and the cyclic prefix duration. The detected cyclic prefix durations were 1/2, 1/4, 1/8, and 1/16. This method is very simple, because with the cyclic prefix presence, a different signal peak will be detected to further estimate the cyclic prefix duration. The results show the correlation method performance can detect one symbol duration with 100%, accuracy, starting at SNR 0 dB, whereas the cyclic prefix duration accuracy rate is getting more accurate by using a less cyclic prefix duration, which is 1/16 of the total symbol duration.

scholarly journals Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 626 ◽  
Author(s):  
Ernesto Cadena Muñoz ◽  
Luis Fernando Pedraza Martínez ◽  
Cesar Augusto Hernandez
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Radio Networks ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Phone Call ◽  
Radio System

A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Rényi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Rényi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Rényi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.

Performance of Chaotic CDMA System implemented on Software-Defined Radio System

2014 ◽  
Vol 1 ◽  
pp. 662-665
Author(s):  
Hisashi Watanabe ◽  
Yuichi Omori ◽  
Mikio Hasegawa ◽  
Kazuyuki Aihara
Keyword(s):  
Software Defined Radio ◽  
Cdma System ◽  
Radio System

scholarly journals Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1558
Author(s):  
Muhammad Bilal Khan ◽  
Mubashir Rehman ◽  
Ali Mustafa ◽  
Raza Ali Shah ◽  
Xiaodong Yang
Keyword(s):  
Psychological Health ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Vital Signs ◽  
Wireless Channel ◽  
Machine Learning Algorithms ◽  
Breathing Patterns ◽  
Connected Health ◽  
Innovative Solution

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

scholarly journals Deep neural network for orthogonal frequency division multiplexing systems without cyclic prefix transmission

2018 ◽  
Vol 189 ◽  
pp. 04016
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Orthogonal Frequency Division Multiplexing ◽  
Deep Neural Network ◽  
Cyclic Prefix ◽  
Wireless Transmission ◽  
Frequency Division Multiplexing ◽  
Frequency Division ◽  
Ofdm System ◽  
New Approach ◽  
Multiplexing Systems

Orthogonal frequency division multiplexing (OFDM) is widely used in wired or wireless transmission systems. In the structure of OFDM, a cycle prefix (CP) has been exploited to avoid the effects of inter-symbol interference (ISI) and inter-carrier interference (ICI). This paper proposes a new approach to transmit the signals without CP transmission. Using the deep neural network, the proposed OFDM system transmits data without the CP. Simulation results show that the proposed scheme can estimate the CP at the receiver and overcome the effect of ISI.

DEVELOPMENT OF A SOFTWARE COMPLEX FOR INTELLIGENT TUNING OF OPERATING FREQUENCIES FOR COGNITIVE RADIO SYSTEMS

2021 ◽  
Author(s):  
Е.О. КАНДАУРОВА ◽  
Д.С. ЧИРОВ
Keyword(s):  
Cognitive Radio ◽  
Software Defined Radio ◽  
Experimental Studies ◽  
Spectrum Utilization ◽  
Radio System ◽  
Cognitive Radio System ◽  
Software Complex ◽  
Cognitive Radio Systems ◽  
Software Modules ◽  
Radio Systems

Представлено описание разработанных программных модулей интеллектуальной перестройки рабочих частот для системы когнитивного радио, в которых применяется ранее предложенный алгоритм анализа использования радиочастотного спектра. Также разработаны программные модули для взаимодействия с программно-определяемыми радиосистемами, такими как LimeSDR. Экспериментально показано, что использование алгоритма предсказания занятости частотных каналов позволяет сократить время оперативного сканирования спектра. A description of the developed software modules for intelligent tuning of operating frequencies for the cognitive radio system is presented. These software modules use the previously proposed algorithm of RF spectrum utilization analysis. Also, software modules have been developed for interacting with software-defined radio such as LimeSDR. Experimental studies have shown that the use of an algorithm for predicting the occupancy of frequency channels allows reducing the time of operational scanning of the spectrum.

scholarly journals Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence

2020 ◽  
Vol 10 (14) ◽  
pp. 4886 ◽  
Author(s):  
Mohammed Ali Mohammed Al-hababi ◽  
Muhammad Bilal Khan ◽  
Fadi Al-Turjman ◽  
Nan Zhao ◽  
Xiaodong Yang
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Nearest Neighbor ◽  
Wireless Channel ◽  
Weight Lifting ◽  
K Nearest Neighbor ◽  
Post Surgery ◽  
Contact Sensing

Non-contact health care monitoring is a unique feature in the emerging 5G networks that is achieved by exploiting artificial intelligence (AI). The ratio of the number of health care problems and patients is increasing exponentially and creating burgeoning data. The integration of AI and Internet of things (IoT) systems enables us to increase the huge volume of data to be generated. The approach by which AI is applied to the IoT systems enhances the intelligence of the health care system. In post-surgery monitoring of the patient, timely consultation is essential before further loss. Unfortunately, even after the advice of the doctor to the patient, he/she may forget to perform the activity in the correct way, which may lead to complications in recovery. In this research, the idea is to design a non-contact sensing testbed using AI for the classification of post-surgery activities. Universal software-defined radio peripheral (USRP) is utilized to collect the data of spinal cord operated patients during weight lifting activity. The wireless channel state information (WCSI) is extracted by using orthogonal frequency division multiplexing (OFDM) technique. AI applies machine learning to classify the correct and wrong way of weight lifting activity that was considered for experimental analysis. The accuracy achieved by the proposed testbed by using a fine K-nearest neighbor (FKNN) algorithm is 99.6%.

scholarly journals Hardware Platform Design for Baseband Data Transmission of WCDMA Based on USB3.0

2018 ◽  
Vol 246 ◽  
pp. 03005
Author(s):  
Fu Xiao ◽  
Li-ming Xiao
Keyword(s):  
High Throughput ◽  
Data Transmission ◽  
Delay Time ◽  
Software Defined Radio ◽  
Experimental Results ◽  
Hardware Platform ◽  
Platform Design ◽  
Radio System ◽  
Throughput Rate ◽  
Low Delay

This paper proposes a hardware platform for WCDMA baseband data transmission, which consists of USB3.0 interface, general purposes processor (GPP), and software defined radio (SDR) system. In view of the requirements of WCDMA system, the hardware platform consisting of USB3.0 controller, FPGA and DDRII was selected, which finally realized the high throughput rate and low delay transmission of baseband data of WCDMA system. The experimental results show that in this GPP software defined radio system, the interface speed of USB3.0 can reach 200MBps, and the loopback delay time of the system is about 0.7ms, which can meet the requirements of WCDMA system.

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