Behavioral modeling and characterization of radio frequency (RF) impairments play a crucial role in comprehending and assessing the performance and limitations of a software defined radio (SDR). Additionally, behavioral modeling of the transmit-receive chain allows us to implement compensation strategies to mitigate the effects of channel impairments. The primary objective of this paper is to model and characterize the non-linear behavior of the complete RF transmit and receive chain of the SDR hardware using well-established memory polynomial (MP) and generalized memory polynomial (GMP) behavioral models. Experimental results demonstrate that the GMP model accurately predicts the RF characteristics of the USRP B210 SDR with an NMSE of -36.6184 dB. Subsequently the model can be used to predict and enhance the performance of wireless systems across diverse scenarios, as well as to optimize their design for specific applications.

This paper presents an effective technique to develop the macromodel of a coaxial-to-microstrip transition (CMT) for X-band applications. CMTs generally manifest as discontinuous structures in the assembly and processing phases of RF and microwave circuit design, especially in systems that employ printed circuit boards. Macro models are necessary to address the parasitic coupling effects in CMT that occur at high frequencies. The proposed approach employs fast relaxed vector fitting (FRVF) to model the CMT as a stable state-space model. The developed model effectively simplifies the simulation process of systems that incorporate CMT which enables a reduction in computational complexity while maintaining system accuracy and stability. The developed macromodel using the proposed approach was found to be stable and causal with an RMSE fitting error of approximate to 10(-4) for an order of approximation of 18.

Digital pre-distortion refers to the use of digital signal processing to address nonlinearities caused by an analog RF front-end in a wireless transmitter. These nonlinearities can lead to nearby channel interference, reduce the transmitted signal's error vector magnitude, and often require the transmitter to operate at a less efficient but more linear region of the device. This work aims to implement a neural network-based digital predistortion algorithm. This pre-distortion algorithm is modelled using a time-delay kernel ridge regression (KRR) algorithm. This is an advanced machine-learning algorithm that considers memory effects. Both software and hardware platforms have been used for this application to produce effective results and discover a comparative comparison between the performance of software and hardware based on some parameters like accuracy, resource utilization, and operation frequency. The training and testing data of an already existing, simulated model is taken, and hardware implementation for the same is carried out using the Verilog HDL programming using the Xilinx Vivado tool.

This article deals with a system design that enables real-time transmission of ASCII text files using software-defined radio (SDR) and a highly directional circular waveguide antenna. The software-defined radio testbed uses the universal software radio peripheral (USRP) and GNU Radio Companion. The key challenge in transmitting ASCII text files is to achieve a high data rate while maintaining precise packet reception. This article deals with applying efficient packet transmission techniques to achieve the reliable transmission of ASCII text files using SDR. The robustness of the proposed approach is experimentally demonstrated utilizing an SDR experimental testbed equipped with circular waveguide antennas with a gain of 9 dBi.

This paper presents the application of contemporary machine learning algorithms to model and linearize the characteristics of Radio Frequency Power Amplifiers (RFPAs). RFPAs are required by wireless systems to increase the signal power for long-distance transmission. However, they cause signals with high peak-to-average power ratios (PAPR) to be distorted due to their inherent nonlinearity qualities and memory effects, raising bit-error rates (BER) in wireless systems. Hence, a key step in reducing signal distortion is linearising its behavioral features utilizing the digital pre-distortion (DPD) technique. Support vector regression (SVR) and Kernel regression (KR), two well-known kernel-based machine learning approaches, are used in this study to model and linearize RF PAs. The modeling and linearising capabilities of both techniques are demonstrated on a class AB GaN-based RFPA. It was found that linearising the RFPA characteristics using SVR with radial basis kernel function yields improved linearization performance compared to the kernel regression-based and standard memory polynomialbased method.

This paper, investigates and presents the behavioral modeling and digital pre-distortion (DPD) of radio frequency power amplifiers (RFPAs) using a time-delay kernel ridge regression (KRR) algorithm. The KRR is an advanced machine learning algorithm that can be effectively used for modeling the baseband characteristics of the RFPA considering both the effects of memory and transistor non-linearity. Compared to the traditional artificial neural network (ANN) based approach which is computationally intensive, the proposed approach using KRR with radial basis kernel function and min-max normalization method, extracts the PA behavioral and DPD model in a short time and yield consistently better results. The performance of the proposed approach in extracting PA and DPD model is demonstrated experimentally on a GaN based class AB power amplifier. The experimental results illustrates that, when compared to ANN based model, the proposed approach yields more accurate PA and DPD models, with an improvement in modeling performance by 2 dB in terms of normalized mean square error (NMSE). In addition, compared to the ANN based approach, the DPD model developed using the proposed approach exhibit improved linearization performance in suppressing spectral regrowth due to PA non-linearity.

In this article, we present a novel method for modeling radio frequency (RF) power amplifiers (PAs). The proposed method for baseband data generation introduced in this paper, enables us to specify operational power range and bandwidth criteria of the PA, yielding a generic power amplifier model, which includes both memory effects as well as non-linearity of the PA. Further, we show that, the modeling approach is useful for bit error rate (BER) computations with PA distortions for any digitally modulated signal. Estimated BER using the proposed PA model for the test case of QPSK and 16-QAM, shows a significant difference in BER at high input back-offs due to memory effects, compared to the conventional non-linear PA models.

This paper, investigates and presents the optimal parameter identification of digital pre-distortion (DPD) models for radio frequency power amplifiers (RF PAs) using a modified differential evolution (MDE) based optimization algorithm. Compared to the conventional exhaustive search method which is computationally intensive, our proposed approach enables the identification of a best-fit DPD model from a combinatorially large model space in a short time. In addition, applying information criteria based objective functions in the optimization process enables us to achieve sparse selection of dynamical models, which balances the model accuracy and model complexity. Experimental validation on a GaN based class AB power amplifier illustrates that, our proposed approach was able to accurately identify complexity reduced optimal DPD models without compromising the modeling accuracy.

This article presents a new approach to determine the optimal behavioral model of a nonlinear radio frequency power amplifier (RF PA) using artificial bee colony (ABC) optimization. The proposed approach, enables us to derive optimal PA models in a short time, thereby saving valuable computational time and resources. The efficiency of the proposed approach is validated by modeling the behavior of a class AB amplifier using an optimal generalised memory polynomial (GMP) model determined using ABC optimization technique.

This article presents a novel information criterion based optimal model parameter selection algorithm for behavioral modeling of Radio Frequency Power Amplifiers (RF PAs). The proposed approach uses Particle Swarm Optimization (PSO) along with the Information Criterion (IC) based cost functions for determining the most parsimonious model from all the available combinatorial models. The proposed technique thereby helps in deriving complexity reduced models without compromising modeling accuracy. The validation of the proposed approach was carried out by modeling a GaAs based PA driven by a 20-MHz generic random input signal. It was shown that, the model performance was maintained while its complexity in terms of number of coefficients was reduced by around 35% in the considered cases. In addition, the proposed PSO based approach helps in deriving the most parsimonious PA model in a very short amount of time compared to the conventional sweep technique.

RF power amplifiers (PAs) used for broadband wireless communication systems such as WCDMA, WiMAX and LTE exhibits a significant amount of dispersion in their amplitude and phase characteristic curves due to memory effects. The conventional Hammerstein model used for modeling RF PAs, characterizes such memory effects very accurately in their linear region and not in their nonlinear region. Hence, to accurately model such dynamic nonlinear characteristics, a new PA behavioral model based on a modified Hammerstein model is used in this article. The enhanced modeling capability of the modified Hammerstein model is validated using a class AB RF Power Amplifier. The results clearly indicate that, the modified Hammerstein model has better modeling performance than the conventional Hammerstein model.

This paper deals with the design and simulation of a Split Ring Resonator (SRR) for the detection of leaks, corrosion and cracks in pipeline networks intended for transporting crude oil, natural gas, petroleum products and other fluid products. The designed SRR is tuned to a resonant frequency of 6.1 GHz and is simulated using High Frequency Structure Simulator (HFSS) tool to detect the ruptures in pipeline coatings made of FR4 epoxy. The designed SRR is mounted on a Rogers substrate above the FR4 epoxy coating and shows a notable variation in the resonant frequency and quality factor whenever gaps are detected between the coating and the pipeline, which can be used for checking the pipeline integrity.

This paper presents the rational modeling of a transmission line interconnect system from the Scattering-Parameter data using minimum-phase-all-pass (MPAP) decomposition and a system identification algorithm. Vector Fitting Algorithm (VFA) is used for system identification and it is found that delay extraction before application of VFA enables us to reduce model identification errors. The efficiency of the algorithms has been tested through examples of several lengths of lossy and lossless transmission lines under matched and mismatched terminations.

This article, deals with the sparse identification of memory effects and nonlinear dynamics for accurate and efficient behavioral modeling of RF Power Amplifiers (PAs). Here, we use sparse regression using a sequential thresholded leastsquares algorithm to determine the fewest relevant terms from a large set of available terms required to accurately represent the dynamics of RF PAs. The proposed approach develops a framework for behavioral modeling of RF PAs, taking into advantage, the advances in sparsity techniques which balances the model accuracy with complexity. We show that, for similar modeling performance, the proposed method requires fewer coefficients than the standard memory polynomial model and simplified Volterra based models.

RF Power Amplifiers (PAs) intended for broadband applications such as WCDMA, WiMAX and LTE systems exhibits significant memory effects along with strong nonlinear behavior when operating at high input power. For these applications conventional Wiener models of PA which are used for modeling their behavior are not suitable, as they model the PA memory effects effectively only in the linear region and not in the nonlinear region. In this article, we propose a new PA behavioral model which accurately models the memory effects in the nonlinear region of the PA. The proposed PA model is based on an enhanced Wiener model known as, Wiener Quasi Dynamic Model which includes a time-delayed nonlinear block. Comparison of the enhanced Wiener model with the conventional Wiener model shows a significant improvement in the modeling performance of PA.

Modern communication systems demand high efficiency RF Power amplifiers (PA) forcing them to be operated close to their nonlinear region resulting in the generation of harmonics and intermodulation products. To reject these harmonics and to reduce the electromagnetic interference, a harmonic suppression antenna is integrated directly with the power amplifier using the concept of Active Integrated Antenna. This method results in improved power output and hence yields high efficiency RF power amplifiers with reduced electromagnetic interference. This paper deals with the design and simulation of a class AB RF power ampifier integrated with a harmonic suppression antenna intended for WiMax applications. The harmonic suppression antenna design, the power amplifier design, the design of the active integrated antenna with the amplifier and the simulation results are presented.

RF Power Amplifiers (PA) consumes a major part of available DC power in any wireless system. This article deals with behavioral modeling of RF Power Amplifiers using artificial neural networks. The developed model enables us to find energy consumption for a signal passing through the PA for a given Gain and maximum allowable distortion. The proposed modeling approach also enables us to linearize the PA by incorporating an inverse model of the PA in the baseband signal processor for compensating the distortion. The PA model can also be used as a sub-system model for evaluating the error performance of the overall system in terms of bit error rate (BER). The modeling method is validated for a class AB power amplifier design.

This article deals with efficient macromodeling of a microstrip-T coupled patch antenna as a state space model. The developed macromodel can be used as an efficient sub-system model of the antenna for simulating overall system containing the antenna. The proposed approach enables us to save valuable computational time and resources during analysis and design of wireless systems.

This paper, deals with the design of a class AB, Gallium Nitride (GaN) transistor based High Power Amplifier (HPA) for Monolithic Microwave Integrated Circuits (MMICs). GaN transistor is selected because of its rugged nature and its capability to work in extreme conditions. The designed HPA is intended to be housed in a Quad Trans-Receive Module (QTRM) of an Active Phased Array RADAR system. The HPA delivers a gain of 10.2 dB and an output power of 37 dBm, as well as high efficiency over many octaves of bandwidth.