Cooperative Networks

Non

A Compact Beam-Scanning Leaky-Wave Antenna With Improved Performance

A compact microstrip leaky-wave antenna (MLWA) with reduced sidelobe level and increased linear frequency-scanning capability is proposed in this letter. Symmetric Yagi-like elements are introduced, which reduce the sidelobe level by radiating the remaining power at the physical end of MLWA, and make the radiation plane (xz plane) symmetric. Defected ground plane is used to optimize the working of Yagi-like elements. Measured results show that the sidelobe is suppressed about 16 dB at 4.8 GHz. To further reduce the sidelobe level, improve frequency-scanning capability, and increase the gain, the leaky section of the antenna is tapered, and two slots of equal dimensions are introduced. The frequency beam scanning is improved compared with the conventional MLWAs by achieving a total beam scan of 78° (from broadside [12°] to endfire [90°]). The measurements performed on the fabricated prototype exhibit good agreement with simulations

An Experimental Channel Capacity Analysis of Cooperative Networks Using Universal Software Radio Peripheral (USRP)

Cooperative communication (CC) is one of the best solutions to overcome channel fading and to improve channel capacity. However, most of the researchers evaluate its performance based on mathematical modeling or by simulations. These approaches are often unable to successfully capture many real-world radio signal propagation problems. Hardware based wireless communication test-bed provides reliable and accurate measurements, which are not attainable through other means. This research work investigates experimental performance analysis of CC over direct communication (DC) in the lab environment. The experimental setup is built using Universal Software Radio Peripheral (USRP) and Laboratory Virtual Instrument Engineering Workbench (LabVIEW). A text message is transmitted by using Phase Shift Keying (PSK) modulation schemes. The setup uses amplify and forward (AF) relaying mode and two time slot transmission protocols. The maximum ratio combining (MRC) technique is used for combining SNR at the receiver. Channel capacity analysis is performed in order to evaluate the performance of CC over DC with and without obstacle. Moreover, optimal position of the relay is also analyzed by varying the position of the relay. Extensive experiments are carried out in the lab environment to evaluate the performance of the system for different hardware setups. The results reveal that cooperative communication attains significant improvement in terms of channel capacity of the system

Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications

Wireless communication using existing coding models poses several challenges for RF signals due tomultipath scattering, rapid fluctuations in signal strength and path loss effect. Unlike existing works, thisstudy presents a novel coding technique based on Analogue Network Coding (ANC) in conjunction withSpace Time Block Coding (STBC), termed as Space Time Analogue Network Coding (STANC). STANCachieves the transmitting diversity (virtual MIMO) and supports big data networks under low transmittingpower conditions. Furthermore, this study evaluates the impact of relay location on smart devices networkperformance in increasing interfering and scattering environments. The performance of STANC is analyzedfor Internet of Things (IoT) applications in terms of Symbol Error Rate (SER) and the outage probabilitythat are calculated using analytical derivation of expression for Moment Generating Function (MGF).In addition, the ergodic capacity is analyzed using mean and second moment. These expressions enableeffective evaluation of the performance and capacity under different relay location scenario. Differentfading models are used to evaluate the effect of multipath scattering and strong signal reflection. Undersuch unfavourable environments, the performance of STANC outperforms the conventional methods suchas physical layer network coding (PNC) and ANC adopted for two way transmission

Utilizing a 5G spectrum for health care to detect the tremors and breathing activity for multiple sclerosis

Utilizing fifth‐generation (5G) sensing in the health care sector with increased capacity and massive spectrum range increases the quality of health care monitoring systems. In this paper, 5G C‐band sensing operating at 4.8 GHz is used to monitor a particular body motion of multiple sclerosis patients, especially the tremors and breathing patterns. The breathing pattern obtained using 5G C‐band technology is compared with the invasive breathing sensor to monitor the subtle chest movements caused due to respiration. The 5G C‐band has a huge spectrum from 1 to 100 GHz, which enhances the capacity and performance of wireless communication by increasing the data rate from 20 Gb/s to 1 Tb/s. The system captures and monitors the wireless channel information of different body motions and efficiently identifies the tremors experienced since each body motion induces a unique imprint that is used for a particular purpose. Different machine learning algorithms such as support vector machine, k‐nearest neighbors, and random forest are used to classify the wireless channel information data obtained for various human activities. The values obtained using different machine learning algorithms for various performance metrics such as accuracy, precision, recall, specificity, Kappa, and F‐measure indicate that the proposed method can efficiently identify the particular conditions experienced by multiple sclerosis patients

Seizure episodes detection via smart medical sensing system

Cyber-physical systems (CPS) consist of seamless network of sensors and actuators integrated with physical processes related to human activities. The CPS exploits sensors and actuators to monitor and control different physical process that can affect the computations of the devices. This paper presents the monitoring of physical activities exploiting wireless devices as sensors used in medical cyber-physical systems. Patients undergoing epileptic seizures experience involuntary body movements such as jerking, muscle twitching, falling, and convulsions. The proposed method exploits S-Band sensing used in medical CPS that leverage wireless devices such as omni-directional antenna at the transmitter side, four-beam patch antenna at the receiver side, RF signal generator and vector signal analyzer that perform signal conditioning by providing amplitude and raw phase data. The method uses wireless monitoring and recording system for measurement and classification of a clinical condition (epileptic seizures) versus normal daily routine activities. The data acquired that are perturbations of the radio signal is analyzed as amplitude, phase information, and statistical models. Extracting the statistical features, we leverage various machine learning algorithms such as support vector machine, random forest, and K-nearest neighbor that classify the data to differentiate patient’s various activities such as press-ups, walking, sitting, squatting, and seizure episodes. The performance parameters used in three machine learning algorithms are accuracy, precision, recall, Cohen’s Kappa coefficient, and F-measure. The values obtained using five performance parameters provide the accuracy of more than 90%

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

We assume Bistatic Phase Multiple Input Multiple Output radar having passive Centrosymmetric Cross Shape Sensor Array (CSCA) on its receiver. Let the transmitter of this Bistatic radar send coherent signals using a subarray that gives a fairly wide beam with a large solid angle so as to cover up any potential relevant target in the near field. We developed Heuristic Computational Intelligence (HCI) based techniques to jointly estimate the range, amplitude, and elevation and azimuth angles of these multiple targets impinging on the CSCA. In this connection, first the global search optimizers, that is,are developed separately Particle Swarm Optimization (PSO) and Differential Evolution (DE) are developed separately, and, to enhance the performances further, both of them are hybridized with a local search optimizer called Active Set Algorithm (ASA). Initially, the performance of PSO, DE, PSO hybridized with ASA, and DE hybridized with ASA are compared with each other and then with some traditional techniques available in literature using root mean square error (RMSE) as figure of merit

Next-Generation Security: Detecting Suspicious Liquids through Radio Frequency Sensing and Machine Learning

Hazardous liquids like nitroglycerin are replacing conventional flammable explosives in modern terrorist attacks. The majority of these hazardous liquids are colorless, oily, and cannot be judged as suspicious by the naked eye. Security inspections of hazardous liquids on a large scale are urgently required to prevent terrorist activities in public areas. However, traditional liquid detection and identification techniques face issues such as cost, accuracy, and scalability, which hinder their widespread adoption. This article introduces a platform that combines radio frequency sensing based on software-defined radio technology and state-of-the-art machine learning (ML) algorithms to detect and classify suspicious and non-suspicious liquids without compromising individuals' privacy. Specifically, fine-grained samples of orthogonal frequency division multiplexing are utilized to acquire channel state information to detect suspicious liquids (glycerin, spirit, and mustard oil) by utilizing radio signals at 900 MHz and 2.45 GHz bands. ML algorithms are employed for classification purposes based on liquids dielectric constant, and their effectiveness is evaluated based on accuracy, prediction speed, and training time. The outcomes of the performance evaluation confirm the platform's effectiveness in accurately identifying and classifying suspicious and non-suspicious liquids with up to 98.3% accuracy with support vector machine.</p