FPGA based technical solutions for high throughput data processing and encryption for 5G communication: A review

The field programmable gate array (FPGA) devices are ideal solutions for high-speed processing applications, given their flexibility, parallel processing capability, and power efficiency. In this review paper, at first, an overview of the key applications of FPGA-based platforms in 5G networks/systems is presented, exploiting the improved performances offered by such devices. FPGA-based implementations of cloud radio access network (C-RAN) accelerators, network function virtualization (NFV)-based network slicers, cognitive radio systems, and multiple input multiple output (MIMO) channel characterizers are the main considered applications that can benefit from the high processing rate, power efficiency and flexibility of FPGAs. Furthermore, the implementations of encryption/decryption algorithms by employing the Xilinx Zynq Ultrascale+MPSoC ZCU102 FPGA platform are discussed, and then we introduce our high-speed and lightweight implementation of the well-known AES-128 algorithm, developed on the same FPGA platform, and comparing it with similar solutions already published in the literature. The comparison results indicate that our AES-128 implementation enables efficient hardware usage for a given data-rate (up to 28.16 Gbit/s), resulting in higher efficiency (8.64 Mbps/slice) than other considered solutions. Finally, the applications of the ZCU102 platform for high-speed processing are explored, such as image and signal processing, visual recognition, and hardware resource management

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed

Low-Power Wireless Medical Systems and Circuits for Invasive and Non-Invasive Applications

Approximately 75% of the health care yearly budget of public health systems around the world is spent on the treatment of patients with chronic diseases. This, along with advances on the medical and technological fields has given rise to the use of preventive medicine, resulting on a high demand of wireless medical systems (WMS) for patient monitoring and drug safety research. In this dissertation, the main design challenges and solutions for designing a WMS are addressed from system-level, using off-the-shell components, to circuit implementation. Two low-power oriented WMS aiming to monitor blood pressure of small laboratory animals (implantable) and cardiac-activity (12-lead electrocardiogram) of patients with chronic diseases (wearable) are presented. A power consumption vs. lifetime analysis to estimate the monitoring unit lifetime for each application is included. For the invasive/non-invasive WMS, in-vitro test benches are used to verify their functionality showing successful communication up to 2.1 m/35 m with the monitoring unit consuming 0.572 mA/33 mA from a 3 V/4.5 V power supply, allowing a two-year/ 88-hour lifetime in periodic/continuous operation. This results in an improvement of more than 50% compared with the lifetime commercial products. Additionally, this dissertation proposes transistor-level implementations of an ultra-low-noise/low-power biopotential amplifier and the baseband section of a wireless receiver, consisting of a channel selection filter (CSF) and a variable gain amplifier (VGA). The proposed biopotential amplifier is intended for electrocardiogram (ECG)/ electroencephalogram (EEG)/ electromyogram (EMG) monitoring applications and its architecture was designed focused on improving its noise/power efficiency. It was implemented using the ON-SEMI 0.5 µm standard process with an effective area of 360 µm2. Experimental results show a pass-band gain of 40.2 dB (240 mHz - 170 Hz), input referred noise of 0.47 Vrms, minimum CMRR of 84.3 dBm, NEF of 1.88 and a power dissipation of 3.5 µW. The CSF was implemented using an active-RC 4th order inverse-chebyshev topology. The VGA provides 30 gain steps and includes a DC-cancellation loop to avoid saturation on the sub-sequent analog-to-digital converter block. Measurement results show a power consumption of 18.75 mW, IIP3 of 27.1 dBm, channel rejection better than 50 dB, gain variation of 0-60dB, cut-off frequency tuning of 1.1-2.29 MHz and noise figure of 33.25 dB. The circuit was implemented in the standard IBM 0.18 µm CMOS process with a total area of 1.45 x 1.4 mm^(2). The presented WMS can integrate the proposed biopotential amplifier and baseband section with small modifications depending on the target signal while using the low-power-oriented algorithm to obtain further power optimization

Estimation and prediction of temperature in Iraq using the multi-layered neural network model

The forecasting using the multi-layered neural network model is one of the methods used recently in forecasting, especially in climate forecasts for certain regions, because of its accuracy in forecasting, which sometimes reaches levels close to the real collected data. In this research, the daily temperatures in the climate of Iraq were predicted, by taking data from the Iraqi Meteorological Authority by (228) observations, which represent the daily temperatures of Karbala Governorate in the year (2021), The results of the autocorrelation and partial autocorrelation showed that the daily temperature series of Karbala governorate is unstable, and this was confirmed by conducting the augmented Dickey Fuller test. The data was analyzed using the multi-layered neural network model in two stages, and it was later shown that the accuracy of estimation and prediction using the multi-layered neural network even if the time series is not stable, The results showed an indication of an rising increase in temperatures during the coming years. The researcher concluded that it is necessary to pay attention to the vegetation cover and to conduct many predictive studies of the climate using the multi-layered neural network

RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth

A novel cross-domain attentional multi-task architecture - xDom - for robust real-world wireless radio frequency (RF) fingerprinting is presented in this work. To the best of our knowledge, this is the first time such comprehensive attention mechanism is applied to solve RF fingerprinting problem. In this paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead of synthetic waveform generation) in a rich multipath and unavoidable interference environment in an indoor experimental testbed. We show the impact of the time-frame of capture by including waveforms collected over a span of months and demonstrate the same time-frame and multiple time-frame fingerprinting evaluations. The effectiveness of resorting to a multi-task architecture is also experimentally proven by conducting single-task and multi-task model analyses. Finally, we demonstrate the significant gain in performance achieved with the proposed xDom architecture by benchmarking against a well-known state-of-the-art model for fingerprinting. Specifically, we report performance improvements by up to 59.3% and 4.91x under single-task WiFi and BT fingerprinting respectively, and up to 50.5% increase in fingerprinting accuracy under the multi-task setting.Comment: Accepted to IEEE GLOBECOM 202