Designing an optical frequency comb generator for visible light communication applications

<p>The optical frequency comb generator (OFCG) is an efficient optoelectronic device that is included in many important applications over a various field such as microwave and optical communication. A novel scheme of OFCG presented in this work for visible light communication application based on amplitude modulation, radio frequency (RF) signal, phase shift and two Mach-Zehnder modulators (MZMs), our design features are simple with more efficient power and premium flatness of comb lines, the number of generating frequencies lines was 64 with a power stronger than -2 dBm over a 340 GHz bandwidth from a single continuous laser diode. Different chirping factor (α) of MZMs are implemented (3, 5, 7), as the results the best results related to α=5 with extra flatness, the system was designed and simulated by VPI design suite 9.8.</p>

AN OPTIMIZED DESIGN MODELLING OF A NEURAL NETWORK BASED GREEN HOUSE MANAGEMENT SYSTEM USING SOLAR AND RECTIFYING ANTENNA

The renewable energy resources are widely used in various real time applications, which utilized the solar, wind, fuel cell, etc. From this, the energy management and controlling strategy improves the results. The conventional approach uses Quantum Tunneling PSO for optimization and it is managed with various utility on power grid system. The work utilized the solar and EM waves for energy management scheme and it utilized the controlling parameter by optimization algorithm. The drawback of conventional method is that, the hybrid system utilization and switching is performed with random selection and it not capable for hybrid resources of multiple array functioning. The proposed research work performed with the solar with MPPT tracking and EM with rectenna are utilized and with the help of neural network model, the PV and RF signal generations are stored as array and based on the switching duty cycle from the function of proposed particle swarm optimization, the boost converter act to provide the supply to grid. Through the inverter control, the model fed with the grid, which uses PI controlling with PWM signal generation. Based on the demand and grid utility the LC compensation improves the boost converter performance. The PV and RF signal generation utilized on the continuous utility and obtains the demand free grid circuit. By comparing with the proposed and existing approach, the proposed greenhouse management model obtains the better result. Overall simulink model is done with MATLAB 2018a. Keywords- PV module; EM waves; Rectenna; Proposed PSO; Feed Forward neural network; PI controller and grid utility;

Throughput Performance of A Delay Constraint Transmission For Half-Duplex And Full-Duplex Cognitive Relay Networks

The performance of a relay based Half-Duplex (HD) and Full-Duplex (FD) cooperative cognitive radio (CR) network with a RF energy harvesting (EH) is studied in this paper. Co-operative environment includes a network with multiple primary users (PUs), and CRs. The relay node is considered as an EH node which harvests energy (HE) from RF signal (RFS) of source and loop-back interference. The network performance is studied for instantaneous transmission and delay constraint transmission for decode and forward (DF) relaying protocol. The performance is investigated under a relay energy outage constraint and the expression of throughput is redesigned. Expressions of energy outage, data outage and throughput for HD and FD are developed. The impact of several parameters such as transmitting SNR, fractional harvesting time parameter, fractional transmission time parameter, and loop-back interference on the system throughput has been investigated.

Understanding of RF Cloud Interference Measurement and Modeling

Importance of spectrum regulation and management was first revealed on May of 1985 after the release of unlicensed ISM bands resulting in emergence of Wi-Fi, Bluetooth and many other wireless technologies that has affected our daily lives by enabling the emergence of the smart world and IoT era. Today, the idea of a liberated spectrum is circulating around, which can potentially direct wireless networking industry into another revolution by enabling a new paradigm in intelligent spectrum regulation and management. The RF signal radiated from IoT devices as well as other wireless technologies create an RF cloud causing co- and cross-interference to each other. Lack of a science and technology for understanding, measurement, and modeling of the RF cloud interference in near real-time results in inefficient utilization of the precious spectrum, a unique natural resource shared among all wireless devices of the universe in frequency, time, and space. Near real time forecasting of the RF cloud interference is essential to pursue the path to the optimal utilization of spectrum and a liberated spectrum management. This paper presents a historical perspective on the evolution of spectrum regulation and management, explains the diversified meanings of interference for different sectors of the wireless industry, and presents a path for implementing a theoretical foundation for interference monitoring and forecasting to enable the emergence of a liberated spectrum industry and a new paradigm in spectrum management and regulations.

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

A Beacon-based Approach for RF Source localization in Outdoor NLOS Environment for Search and Rescue Missions

Due to its low implementation cost, the combination of the Received Signal Strength (RSS) with the Angle of Arrival (AOA) measurements is one of the solutions for Radio Frequency (RF) source localization, especially in a Non-Line of Sight (NLOS) environment. It is critical to determine the search space for a person who is lost in rural areas where the mobile network is unavailable due to a lack of Base Tower Stations (BTS) in order to reduce search time. In this paper, we introduce a new beacon-based approach for RF source localization, where the RF signal is received in NLOS after 1-bounce reflection, by combining the information coming from both the RSS-AOA sensors and the beacons, which are used as helpers- that move along a determined path. The proposed approach relies on determining the reflector’s pose first, after which the RF source is localized. The work has been verified in simulation and the Root Mean Square Error (RMSE) is used as a performance metric for RF source localization. Results show that our proposed approach has the lowest RMSE among localization methods mentioned in the literature under the same conditions. HIGHLIGHTS A new beacon-based approach for RF source localization in Non-Line Of Sight (NLOS) condition A reflector’s pose is determined based on the signal received from beacons The reflector’s pose is used to determine the location of the RF source One bounce reflection is considered since the chance of receiving RF signal with more reflections is very low GRAPHICAL ABSTRACT

Generation of Switchable Chirp Waveforms in the Photonic Domain with Immunity to Dispersion-Induced Power Fading

A novel photonic approach to generating switchable multi-format chirp waveforms using a dual-drive dual-parallel Mach–Zehnder modulator (DD-DPMZM) is proposed and experimentally demonstrated. By properly controlling the bias voltage on the DD-DPMZM, different chirp RF waveforms, including the dual-, down-, and up-chirp waveforms, can be obtained when an RF signal and a chirp RF signal are injected into the modulator. A main feature of this approach is that it can eliminate chromatic dispersion-induced RF power fading, which is highly desired in distributed multi-functional radars based on radio over fiber. There is no polarization control and optical filtering in the given scheme, which also improves the stability and feasibility of the approach. An experiment successfully demonstrated the generation and switching of the multi-format chirp waveforms and the capability of immunity to dispersion-induced power fading.