Wireless channel estimation is one of the challenging problems in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect Channel State Information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency. The proposed Data Aided Channel Estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training symbols to attain the desired performance. Also, it outperforms the traditional Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) methods for channel estimation in terms of Mean Square Error (MSE) and Bit Error Rate (BER) performance of the system.

In this paper, an automatic antenna impedance matching technique is presented for a wide range of frequency bands where a low-complexity shallow learning model adaptively determines the component values of the matching circuit on a real-time. In general, matching networks require both the real and imaginary parts of the antenna impedance to determine the tuning parameters which involves expensive measurement equipment. The shallow learning model developed in this work needs only the magnitude of the antenna reflection coefficients ( S ₁₁ ) to construct the impedance matching circuit. First, the tuning parameters were theoretically calculated and used for computer simulations to generate the S ₁₁ data. In total 500 samples were generated, of which, 400 were used for training and 100 for validation. The proposed technique was applied to a novel inverted-F antenna resonating at 2.45 GHz for impedance matching. The achieved results show stable performances over a wide range of frequencies from 2 to 3 GHz. For validation, the performances of the impedance matching circuit were simulated using predicted and calculated tuning parameters. Results confirm comparable performances in terms of the antenna’s resonance frequency, reflection coefficients, and operational 10-dB bandwidth. The fast prediction ability of the proposed low complexity shallow learning model makes it suitable for real-time applications. Moreover, repeated K -fold cross validation confirms a stable 0.99985 accuracy of the proposed model when repeated 15 times.

Rapid depletion of fossil fuel reserves, and concerns over climate change have encouraged power generation from sustainable energy based microgrids. And to address the necessity of three-phase inverters in microgrid systems or sustainable-powered households, an Arduino-based three-phase inverter using MOSFET is designed, which converts DC into three-phase AC power. The designed system generates 223V square signals at each phase from a 12V battery through switching of three stages of power MOSFETs using pulse width modulation (PWM) signals at their gates from an Arduino Uno. Each stage of power MOSFETs consists of six transistors making it eighteen in total, which are used to perform the inversion process separately for each three single-phase connections. The system is programmed using an Arduino Uno to generate PWM signals and to keep 120 degrees phase displacement among each phase. Three step-up transformers are coupled at the outputs of MOSFET stages for amplification. The system generates 386.25V of voltage for the three-phase line delivering 0.58A of current using a 60W incandescent bulb at each phase as a load. The design and simulation of the electronic circuit are done by Proteus, and the programming codes are written using Arduino IDE. The designed system is practically contrasted and verified.

This letter reports, for the first time, a molybdenum-disulfide (MoS $_2$ )-coated microwave gas sensor for methanol gas detection. A wideband monopole antenna, resonating at 2.45 GHz for wireless applications, is fit with a 15-mm-long copper-based grating sensing element for high-precision gas sensing. When the sensor surface is coated with a highly sensitive layer of hydrothermally grown MoS $_2$ and exposed to methanol gas, the electrical properties of the sensing material change. The developed sensor confirms the detection of methanol gas in parts per thousand (ppt) range from 5.3 to 42.6 ppt with a sensitivity of $\sim$ 1.0 MHz/5 ppt. Furthermore, the gas-sensing functionality of the sensor does not affect the communication performances of the antenna, suggesting seamless integration into wireless sensor networks (WSNs). The simulation results, experimental data, and sensing mechanisms are provided to support the claims.

This paper presents a machine learning (ML)-assisted novel microwave-sensor for the simultaneous and selective detection of multiple volatile organic compounds (VOCs) at room temperature using molecularly imprinted polymer (MIP)/carbon nanotubes (CNTs)-based sensing layers. The synthesized materials were characterized using Fourier-transform infrared spectroscopy (FT-IR) and subsequently used to develop two chemiresistive sensors, two single-port antenna sensors, and a two-port multiple-input and multiple-output (MIMO) antenna sensor, all systematically developed step by step. First, high-precision interdigitated electrode (IDE)-based sensors were functionalized with MIP/CNT sensing materials for individual target VOCs. These sensors were then evaluated for their electrical and gas-sensing properties and optimized to achieve the desired sensitivity. Next, antenna sensors were developed using these optimized structures, demonstrating selectivity for methanol and ethanol with a sensitivity of ∼1.0 MHz/1,000 ppm. The detection limits were 77 ppm for ethanol and 90 ppm for methanol, both well below their safety thresholds, ensuring the sensors’ suitability for practical applications. Machine learning (ML)-based signal processing techniques were used to isolate cross-reactivity between chemical interferents and mutual coupling effects from closely spaced array elements. This enabled the simultaneous and selective detection of multiple VOCs in complex mixtures. The ML models trained on experimental data achieved an impressive F₁ score of 0.9917, demonstrating accurate discrimination of VOC types. Additionally, the models produced an R-squared value of 0.994 for gas concentration estimation, confirming their predictive accuracy. The developed sensors exhibited high selectivity and specificity when tested against methanol, ethanol, acetone, and isopropanol. Moreover, the antenna sensors operated within their bandwidth during gas sensing, eliminating the need for complex tuning circuits. Additionally, perturbation analysis confirmed the ML model’s robustness, as it maintained high accuracy despite input noise, ensuring reliable real-world performance.