National Journal of RF Circuits and Wireless Systems
Aim and Scope : National Journal of RF Circuits and Wireless Systems,ISSN 3107-6807 , publishes peer-reviewed research focused on advancing knowledge in RF engineering and wireless communication systems. The journal emphasizes both theoretical innovations and practical applications, covering: RF Circuit Design: LNAs, PAs, mixers, oscillators, RFIC/MMIC, MEMS, and advanced materials. Wireless Systems: 5G/6G, IoT protocols, MIMO, OFDM, SDR, and cognitive radio. Antennas & Propagation: Reconfigurable and wearable antennas, propagation models, and RF energy harvesting. Security in Wireless: Physical layer security, jamming protection, and secure IoT networks. Testing & Simulation: Use of HSPICE, ADS, CST, OTA testing, and hardware-in-the-loop validation. Application Areas: Biomedical RF, aerospace, automotive, defense, and smart infrastructure. The journal encourages interdisciplinary submissions and maintains a rigorous peer-review policy to uphold academic integrity and innovation. Frequency of publication - It has Three issue per year Language - English Subject - Engineering Year of Starting - 2024 format of publication - Online Only ISSN- 3107-6807
SOCIETY FOR COMMUNICATION AND COMPUTER TECHNOLOGIES
Ultra-Low-Power RF Energy Harvesting and Backscatter Communication System for Implantable Biomedical Sensors
The biomedical sensors implantable need stable performance over a long period with very stringent energy levels when battery replacement is not practicable and harvested RF power values normally are very low especially because of tissue attenuation. The following paper describes ultra-low-power RF energy harvesting and backscatter communication system which is optimised to be used in implantable applications. The suggested architecture incorporates a downsized implant antenna, a multi-stage low-threshold rectifier, and a low-weight maximum power point tracker maximized controller considering sub- mW to several- mW operating conditions. A low-overhead incentive-conduction approach in which operation is duty-cycled is used by MPPT algorithm to reduce controller consumption while ensuring the algorithm converges at a rapid rate with changing RF input conditions. Results in the context of experiment and simulation indicate a highest RF to DC conversion efficiency of up to 60 percent and maintained efficiency over 50 percent at a low input power of down to -18 dBm. The system has a cold-start sensitivity of -19 dBm, which increases the range of operation in biological tissue. The proposed adaptive tracking design is 25 percent better harvested power than a fixed-load solution (with controller overhead of less than 2). Backscatter communication with an integrated backscatter allows transmission of data at 50 -100 bps with a bit error rate of less than 10 -3 over short biomedical telemetry links of short range. The analysis of specific absorption rate (SAR) verifies the adherence to the biomedical standards of safety. These findings confirm the proposed MPPT-enabled harvesting architecture has a tremendous benefit of improving stability of energy, sensitivity and reliability of communication abilities thus making it a viable system in long-term battery-less implantable applications requiring sensing.
Implantable Antenna; Power Management Unit (PMU); Sensitivity Optimization; Specific Absorption Rate (SAR); Wireless Power Transfer; Biomedical Telemetry.
OTA-Validated Hardware-in-the-Loop Framework for mmWave SDR Transceiver Performance Optimization
Millimetre-wave (mmWave) communication systems with rapidly changing channel conditions, hardware inefficiencies, and beam misalignments do not have enough plant stability when using software-defined radio (SDR) transceivers, in which setting of the channels ought to be performed by an innovative approach called static configuration strategies. The article is a report of an over-the-air (OTA)-validated Hardware-in-the-Loop (HIL)-based validation framework that incorporates Deep Reinforcement Learning (DRL) as a real-time adaptive method of transceiver performance optimization. The presented architecture creates a closed feedback architecture where live RF measurements such as Error Vector Magnitude (EVM), bit error rate (BER), Adjacent channel power ratio (ACPR) and received signal strength are constantly fed to a DRL agent to dynamically change transmission parameters such as beam index, modulation order, transmit power, and RF front-end control variables. Deep Q-Network (DQN) is used to solve discrete control problems, and Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) are used to usher continuous RF parameter tuning. This scheme is experimentally tested by using real time OTA experiments on a mmWave SDR platform, on a controlled propagation channel. The experimental findings exhibit up to 30 percent decrease in the EVM, 28 percent increase in the BER performance at the midSNRs, 5 to 7 dB increase in the ACPR, and 18 percent increment of the throughput relative to the static and heuristic settings. The suggested framework of HIL-DRL reaches fast convergence within under 150 ms adaptation periods and spectral compliance. This supports the viability of closed-loop RF optimization, which includes intelligence and supports the use of the framework in next-generation adaptive mmWave and beyond-5G wireless systems.
RF Performance Optimization; Error Vector Magnitude (EVM); Adjacent Channel Power Ratio (ACPR); Adaptive Beam forming; Spectral Efficiency; Next-Generation Wireless Systems.
Hybrid CMOS–MEMS Tunable Oscillator Architecture for Multi-Band Cognitive Radio Systems
Cognitive radio systems require frequency agile local oscillators that can operate over a wide tuning range, with low phase noise, and limited energy consumption in communication bands with dynamically changing accessibility. Traditional CMOS-only VCOs suffer due to poor quality factor (Q) inductor on-chip, poor phase noise characteristics, as well as nonlinear tuning characteristics throughout large frequency bands. This paper suggests a tunable CMOS- MEMS tunable oscillator design, in which a high-Q MEMS electronically tunable resonant component is incorporated into a CMOS cross-coupled negative-resistance core to facilitate the functioning of the multi-band cognitive radio. Coarse band selection is offered by a switched capacitor array and fine frequency control with increased spectral purity by MEMS based capacitive tuning. Post-layout simulations of the design are done in 180 nm CMOS technology, which consists of parasitic extraction and phase noise simulation according to the model of Leeson. The proposed architecture has a broad tuning frequency of 1.836 GHz (66%), phase noise of -121 dBc/Hz 1 difference at 1 MHz (2.4 GHz) and a power consumption of 5.8 mW with 1.8 V of power. This foM of -187 dBc/Hz is within competition with traditional examples of LC-VCO. The hybrid integration method improves the tuning linearity and frequency stability therefore applicable in dynamic spectrum access and reconfigurable RF front-end designs in next-generation wireless systems.
CMOS oscillators, MEMS resonators, tunable oscillators, voltage-controlled oscillators (VCO), cognitive radio, multi-band systems, RF integrated circuits, phase noise, frequency synthesis, dynamic spectrum access.
Kim Yeonjin, Kim Hee-Seob, Lee Hyunjae, Jeon Sungho
Secure Beamforming with Reconfigurable Intelligent Surfaces for Anti-Jamming UAV Communication Links
The Unmanned Aerial Vehicle (UAV) communication links are highly prone to the deliberate jamming because of the dominant line-of-sight propagation and the open author to deployment conditions. Traditional procedures of anti-jamming on the grounds of power adaptation or frequency agility offer less protection against adaptive enemies. Reconfigurable Intelligible Surfaces (RIS) provide a mechanism of low power electromagnetic control that can reconfigure the channel of propagation to restore the better signal robustness. The current paper presents a safe beamforming scheme of RIS-aided communication between UAVs and the ground in the presence of active jamming. The geometric model of UAV and ground channels is a three dimensional model which takes into account the altitude-dependent path loss and Rician fading to characterise UAV and ground channels accurately. Direct transmission, RIS-reflected and malicious interference are explicitly modelled in the received signal model. An optimization maximising secrecy rate is developed with unit-modulus RIS phase, and UAV power of the transmitters. In order to address the ensuing non-convex problem, the alternating optimization algorithm is developed to simultaneously optimise UAV beamforming vectors and RIS phase shifts, including discrete phase quantisation to reflect realistic hardware constraints. The simulation outcome shows that the secrecy rate can be increased by up to 65 percent and reduction of interference by a large value than the conventional beamforming in the absence of RIS. Moreover, the viability of the proposed approach is proved by the complexity analysis and considerations of RF implementation. The findings make RIS-assisted secure beamforming an effective and practical anti-jamming method in next-generation is to be used in UAV communication systems.
UAV communication, reconfigurable intelligent surface, anti-jamming, secure beamforming, secrecy rate, RF systems
AI-Assisted Adaptive Impedance Matching Network for Wideband IoT RF Front-Ends
Multi-band operation and antenna detuning, environmental loading, and proximity effects at RF front-ends are also increasingly causing dynamic variations in impedance at Wideband Internet of Things (IoT) RF front-ends. Traditional fixed or heuristic adaptive impedance matching networks do not achieve the optimum power transfer in large frequency bands resulting in poor return loss, power amplifier efficiency, and link reliability. The following paper proposes adaptive impedance matching network based on AI that should be used in real-time wideband applications of IoTs. The suggested architecture also combines a reconfigurable π-network with a lightweight machine learning inference engine and it is set up in a closed-loop feedback like system. Wideband optimization framework The framework to be formulated can be summarised as minimising the reflection between operating range and control power overhead is limited. The validation of the system is performed by RF circuit simulation and hardware analysis within the 0.8-2.5 GHz frequency spectrum under the conditions of dynamic load. Real-world experiments have shown that average and maximum loss of return, and stability of power effectiveness have been improved significantly in relation to traditional fixed and switched matching methods, and that it is not much higher than the adaptation latency obtainable in embedded IoT systems. The suggested approach designates an energy-conscious and scaleable, smartly developed RF front-end dynamic in coming-generation extensive band wireless.
Wideband IoT RF Front-End, Adaptive Impedance Matching, Machine Learning-Based RF Optimization, Reconfigurable Matching Network, Wideband Reflection Minimization, Energy-Efficient Wireless Systems
Thermal-Aware Co-Design of GaN-Based Power Amplifiers for High-Linearity 6G Sub-THz Transceivers
The realization of high-linearity power amplifiers (PAs) at sub-terahertz frequencies is a critical challenge for emerging 6G transceivers, where extreme power density and wideband modulation exacerbate self-heating effects in GaN devices. Elevated junction temperature significantly degrades carrier mobility, shifts threshold voltage, and intensifies nonlinear distortion, resulting in gain compression and intermodulation distortion (IMD) deterioration. Despite advancements in GaN PA design, existing approaches largely optimize electrical performance without explicitly integrating electro-thermal coupling into linearity-aware design frameworks. This work presents a comprehensive thermal-aware co-design methodology that combines temperature-dependent large-signal modeling, dynamic electro-thermal coupling, and thermal-constrained load-pull optimization. Structural heat-spreading strategies and temperature-compensated bias stabilization are incorporated to mitigate thermal-induced nonlinearities. At 140 GHz, the proposed approach demonstrates substantial IMD3 suppression, improved power-added efficiency (PAE), and stabilized junction temperature under elevated ambient conditions. The results confirm that integrated electro-thermal optimization is essential for achieving robust, high-linearity GaN PAs suitable for next-generation 6G sub-THz front-end architectures.
6G sub-terahertz communications, GaN power amplifier, electro-thermal co-design, thermal-aware load-pull optimization, intermodulation distortion (IMD3), high-linearity RF front-end
Integrated RF Transceiver Design and System-Level Validation for Smart Wireless Sensor Networks
Smart Wireless Sensor Networks (WSNs) demand highly integrated, energy-efficient, and interference-resilient RF transceivers capable of sustaining reliable communication in dynamic and resource-constrained environments. This paper presents the design, implementation, and comprehensive system-level validation of a low-power CMOS-based integrated RF transceiver specifically engineered for smart WSN applications operating in the 2.4 GHz ISM band. The proposed architecture incorporates an inductively degenerated low-noise amplifier, quadrature direct-conversion mixer, fractional-N frequency synthesizer, adaptive Class-AB power amplifier, and digitally controlled baseband processing with energy-aware MAC integration. A cross-layer optimization strategy is employed to co-design RF front-end performance with adaptive transmit power control based on real-time RSSI feedback, thereby minimizing unnecessary energy expenditure. Circuit-level simulations are conducted using HSPICE, while system-level performance, including BER and interference tolerance, is validated through MATLAB-based modeling. Hardware-in-the-loop (HIL) experimentation and a 25-node real-time deployment further verify practical feasibility under realistic interference and environmental conditions. The implemented transceiver achieves a receiver sensitivity of −92 dBm, noise figure of 1.9 dB, power-added efficiency of 38%, and bit error rate below 10⁻⁶, while demonstrating a 27% reduction in overall energy consumption compared to conventional IEEE 802.15.4-based modules. Experimental results confirm improved packet delivery ratio, reduced retransmissions, and extended node lifetime through adaptive power scaling. The proposed integrated solution provides a scalable and deployment-ready platform for smart agriculture, industrial monitoring, environmental sensing, and healthcare Internet of Things (IoT) applications.
RF Transceiver, Wireless Sensor Networks, Low-Power CMOS, Smart IoT, System-Level Validation, Energy Efficiency, Adaptive Communication
Performance Evaluation of mmWave RF Circuits for High-Data-Rate 6G Wireless Communication Systems
The 6G wireless communication system is projected to achieve ultra-high frequency rate of more than 100 Gbps, less than one milliseconds of latency and continuous connexion of massive scale by utilising millimetre-wave (mmWave) and low terahertz frequencies. The high demands on RF front-end circuits are required to meet these high targets, the performance of which at higher frequencies is very poor, both because they suffer dire path loss and transistor gain, and especially because of parasitic terms such as reduced voltage headroom, thermal instability and process variability. This paper has conducted a system performance assessment of the major mmWave RF building blocks containing low-noise amplifiers (LNAs), power amplifiers (PAs), mixers, and voltage-controlled oscillators (VCOs) used in 6G transceivers at 28 GHz, 60 GHz, and 100 GHz. Characterization and analytical modeling and simulation are done through the application of sophisticated 45 nm CMOS, SiGe BiCMOS technologies, in order to study frequency scalability and trade-offs determined by technology. Such critical performance indicators as gain, noise figure (NF), input third-order intercept point (IP3), power-added efficiency (PAE), phase noise, bandwidth, and thermal behaviour are retrieved and compared across bands. Experiments indicate strong effects of efficiency and linearity loss above 60 GHz, phase noise enhancement at 100 GHz because of the Q-factor factors, and the high-frequency resiliency of SiGe BiCMOS is much better than CMOS. The paper also establishes trade-offs between the various designs as to power efficiency, spectral purity, and integration complexity, which can be useful in offering actionable optimization rules to energy-efficient high-data-rate 6G RF front-end designs.
6G, mmWave, RF circuits, power amplifier, low-noise amplifier, mixer, VCO, high-data-rate communication, CMOS, BiCMOS.
A Reconfigurable Microstrip Antenna with Enhanced Bandwidth for Multistandard Wireless Applications
This article shows a design, analysis, and performance of a multistandard wireless reconfigurable microstrip antenna with an improved impedance bandwidth. The proposed antenna is a combination of slot-loaded radiating patch antenna and defected ground structure (DGS) which enhances the performance in bandwidth performance and compact geometry. Switching between the various operating states is through frequency reconfiguration by the use of PIN diode switches that are installed over the slots area, which requires no addition of size. The antenna is structured on an FR4 and is optimised to include LTE frequencies of 1.8 GHz and 2.6 GHz, WLAN frequencies of 2.4 GHz and sub-6 GHz 5G frequencies of 3.5 GHz. The impact on impedance matching and bandwidth increase of slot dimensions, truncation of ground planes and location of switches is parametrically analysed. The results of the simulation indicated that the proposed antenna attains the -10 dB impedance bandwidths of 1.78-1.92 GHz, 2.32-2.52 GHz, 2.52-2.72 GHz and 3.32-3.62 GHz. The fractional bandwidth achieves a maximum of 8.65 per cent, which is very good when compared to a typical rectangular patch. The antenna offers constant radiated quality to 3.6 dBi to 4.4 dBi peak realised governing along with a radiation efficiency of over 78% in all switching states. The proposed antenna is compact in geometry and has multistandard capability, which makes it suitable in wireless communication devices of the modern world.
Microstrip antenna, Reconfigurable antenna, Bandwidth enhancement, Multiband antenna, PIN diode switching, Defected ground structure (DGS), WLAN, LTE, 5G sub-6 GHz.
Machine Learning–Assisted Optimization of RF Power Amplifiers for High-Efficiency Wireless Transmitters
The power consumption of power amplifiers (PAs) dominates the modern wireless transmitters and thus there is a strong desire to improve efficiency as a design goal. But, high power-added efficiency (PAE) and gain and linearity are difficult to meet in RF design spaces, where the spaces are strongly nonlinear and non-dimensional. Traditional methods, e.g. load-pull analysis and tedious parametric sweeps, are computationally intensive and may be inefficient in multi-parameter optimization. The proposal in this paper is a machine learning-aided optimization of a 2.4 GHz Class-AB RF power amplifier that was designed in 65 nm CMOS technology. An artificial neural network (ANN) surrogate model which is trained with harmonic-balance simulation data will provide a high accuracy prediction of important performance quantities of a circuit, such as PAE, gain, and output power. Optimal bias voltages and matching network component values are then found with a performance constraint using Bayesian optimization. The suggested framework reaches up to 10.8% enhancement in PAE in contrast to a traditional grid-based optimization as well as decreasing the optimization time in general by about 35 percent. The surrogate model portrays an excellent prediction accuracy, and low mean absolute error all through the design space. The findings show that machine learning-assisted optimization can provide a quicker convergence, enhanced efficiency, and scalable RF design methodologies, which will provide an efficient way out to high-efficiency wireless transmitter architectures.
RF power amplifier, machine learning, Bayesian optimization, artificial neural network (ANN), power-added efficiency (PAE), surrogate modeling, harmonic balance simulation, CMOS RF circuits, wireless transmitters, multi-objective optimization.
An Energy-Efficient MIMO Transceiver Architecture for IoT-Enabled Wireless Communication Networks
The high rate of growth of Internet of things (IoT) devices has brought strict limit to power consumption, hardware level, and cost of deployment in wireless transceiver design. Although multiple input multiple-output (MIMO)-based methods are generally known to enhance spectral efficiency and connexion reliability, traditional MIMO transceivers are generally maximised to optimise high-throughput systems and use various parallel radio-frequency (RF) chains and computationally expensive baseband processing, which is inappropriate for energy-limited nodes of the IoT. In this paper, a MIMO transceiver architecture that is efficient in energy consumption is proposed targeting the IoT-enabled wireless communication network. The proposed architecture includes adaptive RF-chain activation, low complexity baseband processing and power-constrained transmission modes to dynamically trade-off communication performance and energy consumption by the degree of changing traffic demands and channel conditions. A fully operating transmitter-receiver transceiver chain is designed, which includes RF front-end, information conversion, and information processing blocks with distinct attention to power-scalable operation at the RF level and digital level as well. System-level simulations of the proposed architecture are done by using realistic power consumption models based on CMOS RF literature. The results of the simulation show that the proposed design not only attains limited energy per bittable and substantially improved over traditional always-on MIMO transceiver designs, but also acceptable throughput performance and significantly low bit error rate (BER) owing to the proposed design. These findings affirm that architecture-level adaptation can offer a viable channel through which MIMO functionalities can be realised in low-energy consume devices in the IoT without high-energy expenses.
MIMO transceiver, energy efficiency, IoT wireless networks, low-power RF architecture, adaptive communication.
Design and Performance Analysis of a Compact Wideband RF Front-End for 5G and Beyond Wireless Systems
Combined with the growing pace of the fifth-generation (5G) and even beyond-5G wireless systems the need of compact, wideband, and energy-efficient radio-frequency (RF) front-end architecture with the ability to support multi-standard and reconfigurable transceivers has risen. This paper describes and discusses a small-scale wideband RF front-end that is aimed at supporting 5G and other sub-6 GHz wireless technologies. This proposed architecture has incorporated a wideband low noise amplifier (LNA) and RF switch to allow broadband impedance matching, low noise performance, and an improved linearity using a smaller silicon area. Front-end is at standard CMOS and is optimised to operate over wideband on the order of careful matching network design, layout-wise considerations. The RF front-end proposed allows continuous operation at 3 -8 GHz, encompassing important 5G bands that are sub-6 GHz, with stable gain, low input reflection, and power consumption. A thorough performance assessment is realised at post-layout simulations, that is, S-parameter analysis, noise figure, linearity measurements, power efficiency. Influence of structure of parasitic effects and layout on overall RF performance is also discussed. The comparative analysis against recently reported RF front-end design proves that the proposed solution has a better trade-off between bandwidth, noise performance, linearity and chip area. These findings demonstrate that the suggested compact wideband RF front-end can be effectively used in the next generation multi-band and versatile wireless transceiver based on 5G and more wireless systems.
RF front-end, wideband LNA, CMOS RF circuits, 5G wireless systems, compact RF design
AI Powered Automated Drug Delivery System with Real Time Monitoring
The healthcare sector faces significant challenges in monitoring patients’ health in real-time, especially for those with chronic conditions. Traditional patient monitoring systems lack predictive capabilities and rely heavily on manual intervention. This can lead to delays in treatment and poor health outcomes. The proposed solution utilizes cloud analytics and deep learning techniques for smart patient monitoring. By integrating wearable health devices with cloud infrastructure, the system continuously monitors patients’ vital signs such as heart rate, blood pressure, and oxygen levels. Deep learning models analyze this data to detect anomalies and predict potential health risks. The goal is to provide early detection of health deteriorations and offer personalized recommendations for treatment. This system aims to enhance patient care, reduce healthcare costs, and improve response times to critical events. Advantages include real-time monitoring, predictive analytics, remote accessibility, and the ability to offer tailored healthcare interventions.
Smart Helmet System for Real-Time Hazard Detection And Response
The increasing number of road accidents involving two-wheelers, especially due to drunk driving, underage riding, and not wearing helmets, indicates the need to include advanced safety measures. Conventional safety relies too heavily on the compliance of riders, which is seldom consistent and results in preventable injuries and fatalities. This project proposes a Smart Helmet Safety System that incorporates basic electronics and sensors with AI to promote better safety for riders and ensure that regulations are followed. An Arduino controls this system, which will monitor alcohol consumption using an MQ3 sensor, check if the rider is wearing the helmet with a limit switch, and authenticate the rider through RFID-based license scanning and AI-based facial recognition, including checking for under-age riding. Additionally, if the model experiences any vibration from accidents, it will send GPS location and alert contacts via a GSM module in no time. The novelty of this work is that by providing an overall automated solution to preventive unsafe travel and speedy emergency responses, two-wheeler travel would be safer using intelligent monitoring and control.
Two-Wheeler Safety, Rider Authentication, Accident Prevention, Emergency Response, Helmet Compliance, Alcohol Detection, Age Verification.
IoT-Based Smart Health-Monitoring Ventilator System For Enhanced Patient Safety
This work tackles the constraints of current low-resource ventilator systems. These systems sometimes lack built-in monitoring for the environment and vital signs, which reduces patient safety and increases the need for using multiple devices. The proposed solution is a smart, affordable health-monitoring ventilator system. It combines temperature, humidity, light, heart rate, blood oxygen levels, and dangerous gas detection into a single automated platform. The system’s goal is to provide constant, real-time monitoring. It also seeks to enhance comfort by automatically adjusting fans and lights, according to input from various sensors. The Arduino microcontroller handles both data gathering and control, operating effectively on its own, without needing any outside components. To improve use in both home and clinical settings, the readings are shown immediately on an LCD panel. The aim is to offer a cost-effective, space-saving, and dependable multi-parameter monitoring system, well suited for environments with limited resources. This system’s uniqueness comes from merging environmental sensing, biomedical monitoring, and automatic ventilation control into a single, completely integrated, and affordable system.
Smart Health Monitoring, Integrated Sensing, Patient Safety, Real-Time Tracking, Low-Cost Design, Automated Ventilation, Biomedical Monitoring.
RF Fingerprinting Techniques for Passive Identification of IoT Devices
At the device level, identification is an important issue in protecting wireless Internet of Things (IoT) infrastructures. The study presents an effective radio-frequency (RF) fingerprinting model of passively identifying IoT devices. Using inherent hardware flaws like I/Q imbalance, transient response and spectral distortion, the proposed system uses these flaws to create distinct RF signatures. A convolutional neural network (CNN) classifier is used to process these signatures and the recognition accuracy is 97 % of 50 types of devices. The system does not require any cryptographic keys or active handshake protocols, and provides a lightweight and scalable authentication layer to low-power IoT systems. The experimental findings support the robustness of the method when applied in the dynamic conditions of the channel, which proves that the method can support the development of device-level trust in the situation of large-scale networks.
RF fingerprinting, IoT device identification, wireless security, passive sensing, signal classification, machine learning.
Reciprocity-Driven Physical Layer Key Generation for Wireless Body Area Networks
This study proposes a lightweight, reciprocity-driven physical layer key generation scheme tailored for secure communication in Wireless Body Area Networks (WBANs). By harnessing the inherent randomness and temporal variation of on-body wireless channels, cryptographic keys are dynamically generated between wearable devices without reliance on pre-distributed secrets or computationally intensive algorithms. The protocol employs adaptive channel probing and quantization, followed by efficient error reconciliation and privacy amplification, ensuring strong key agreement even under mobility and diverse body movements. Experimental evaluations on a real WBAN testbed demonstrate high key generation rates, low bit mismatch, and resilience to passive eavesdropping, validating the scheme’s suitability for resource-constrained, low-power healthcare wearables. The results highlight the potential of physical-layer security as a practical solution for next-generation, privacy-aware health monitoring systems.
Physical Layer Security; Key Generation; Channel Reciprocity; Wireless Body Area Networks (WBANs); Wearable Devices; On-Body Communication; Cryptographic Protocols; Low-Power Security; Healthcare IoT; Eavesdropping Resistance
OTA-Validated SDR-Based mmWave RF Transceiver Architecture for 5G Wireless Communication Systems
The fast-moving development of the 5 G Wireless communication has driven the development of fully flexible and high-frequency transceiver architecture that can both support millimeter-wave (mmWave) operation and real-time reconfigurability. Here, solutions based on Software-Defined Radio (SDR) offer an easily reconfigurable operating framework that enables quick prototyping and testing performance. This article describes the design, implementation and Over-the-Air (OTA) validation of a simulation tested mmWave Software Defined Radio transceiver (SDR) operating on a 28 GHz frequency band to support 5G New Radio (NR) specifications. The architecture under design leverages reconfigurable baseband processing such as through the GNU Radio and MATLAB/Simulink platforms and high-frequency analog front-ends such as beam-steerable patch array antennas that are positively designed and optimized by means of the CST Microwave Studio. The physical layer is implemented using a USRP X310 platform supplemented with outside mmWave up/down converters and link-level simulations are used to measure parameters, like signal-to-noise ratio (SNR), error vector magnitude (EVM), and system throughput. It conducted OTA testing in controlled anechoic to prove end-to-end link performance with line-of-sight (LOS), and non-line-of-sight (NLOS). The transceiver is measured at an EVM of below 3.5% latency with an end-to-end under 2ms, and peak throughput of 600 Mbps illustrating that it is differentiated to 3GPP Release 16. These findings justify the possibility and scalability of the proposed SDR-based transceiver to be used within the 5G mmWave applications, and further developments would focus on applications to multi-antenna MIMO and AI-assisted dynamic beam management.
mmWave Transceiver Design, Software-Defined Radio (SDR), Over-the-Air (OTA) Validation, 5G Wireless Communication, Beamforming Architecture, USRP Prototyping
Ruggedized RF Transmitter Design for High-Temperature Aerospace Environments
The growing need to have reliable wireless communication systems in the aerospace platforms also requires the need to have RF transmitter architecture that can maintain its capability to operate under the extreme environment especially the high temperature and radiation environment. This paper gives full design, modeling and validation of a ruggedized RF transmitter that can be used with aerospace environment where ambient temperatures can reach a high temperature of 250C. The main building block of the proposed architecture is a power amplifier based on GaN that is predicted to have better heat tolerance and higher power ratings than silicon- or gallium arsenide-based predecessors. The system is thermally stable with temperature-compensated biasing circuit and effective thermal system with ceramic boards and heat-spreading case. The signal travel path with components radiation-hardened is used across the signal chain to provide greater survivability in high altitude and deep space conditions where total ionizing dose and latch-up conditions are major concerns. To thoroughly validate the design, a combination of ADS and CST co-simulation is used to characterize the RF performance, thermal modeling is done in COMSOL, and testing is done under both accelerated thermal cycling and radiation stress conditions. At 5.8 GHz the transmitter delivers a maximum power output of 28 dBm and high power beyond 42% power-added efficiency (PAE) and all within a wide temperature range with less than 1.2 dB variation in gain. Moreover, the system portrays stable impedance matching (S11 < -10 dB), low bit error rates when subjected to QPSK modulation, and negligible degradation of performance even under radiation dose of 100 krad(Si). This project not only proves that high temperature RF communications is not only possible in aerospace but also defines the design process of its future ruggedized communicational modules targeting high-altitude drones, planetary entry systems, and spacecraft payloads. The combination of rugged thermally and radiatively robust components in a small scale, mass producible transmitter platform is a first in the RF/harsh environment electronic arena of aerospace.
High-Temperature Electronics; Ruggedized RF Transmitter; Gallium Nitride (GaN); Aerospace Communication; Thermal Management; Radiation-Hardened Design; Power Amplifier (PA); ADS-CST Co-Simulation; Temperature-Compensated Biasing; Harsh Environment Electronics
Co-Simulation of RFIC Performance Using HSPICE and CST for Aerospace Communication Link
The evolutionary progress of aerospace communication resources requires the fabrication of high performance Radio Frequency Integrated Circuits (RFICs) that are capable of exhibiting high levels of both performance and reliability even in extreme environmental conditions, such as wide temperature variations, and radiation exposure, and also a high-frequency spectral requirement base. Conventional medicine uses circuit-based design methods, which are limited in capturing complex electromagnetic (EM) and layout-dependant parasitic effects, which make a critical impact on the performance of RFIC devices in real environments. The paper achieves this by formulation of a very well developed and strong co-simulation model that closely integrates full-wave electromagnetic circuit analysis using CST Microwave Studio and transistor-level circuit analysis using HSPICE. The methodology suggested can offer comprehensive modeling and optimization of the active and passive elements of the circuit, including circuit inductors, transmission lines, bonds, packaging, and so on. Performance factors like S parameters, voltage gain, NF, PAE, linearity and EM radiation performance are measured at Ku- and at Ka-band frequencies which are essential in the satellite-ground and inter-satellite communication links in aerospace applications. Additionally, the algorithm supports layout parasitic extraction (LPE), thermal-aware simulation, and radiation-hardened design design to achieve high precision when subjected to operational stresses most likely to be found in aerospace missions. S-parameter models of EM structures that are important in the circuit functioning are formed through the application of CST to its corresponding regions and the resulting model is directly inserted into a HSPICE simulation, which in turn, forms a feedback loop between physical effects on layout and circuit behavior. The co-simulation solution has demonstrated that it aligns well with the actual performance in addition to enabling the optimization cycle in design to be faster due to the window that is opened by removing the separation between EM design and electrical validation. Degradation in thermal robustness and performance at elevated total ionising dose (TID) are also discussed in order to illustrate resiliency. This co-simulation platform is an acceptable accuracy and scalable approach which offers a solution to RFIC designers interested in aerospace-grade transceivers, as well as to the next-generation modules of high frequency communication with improved reliability, smaller size, and reduced power.
RFIC Co-Simulation, HSPICE, CST Microwave Studio, Aerospace Communication, Ka-band, Electromagnetic Simulation, S-Parameters, Layout Parasitic Extraction, System-in-Package (SiP), Radiation-Hardened Design, EM-Circuit Integration
Ultra-Wideband RF Front-End for Real-Time Brain Signal Acquisition in Neuroprosthetic Devices
A high-performance ultra-wideband (UWB) radio frequency (RF) front-end architecture that is inherently designed with a capability to acquire brain signals in real-time in next-generation neuroprosthetic systems is presented, developed, and experimentally validated in this paper. As more wireless brain machine interfaces (BMI) and closed-loop neuroprosthetic devices become clinically useful, the RF front-end circuitry has been a critical need in support of transmission of low-amplitude, high-resolution neural activity; such as intracranial electroencephalography (iEEG) and local field potentials (LFPs) with minimal delays, high fidelity and ultra-low power requirements. The suggested front-end consists of an integrated-global positioning system (GPS) front-end architecture that is miniaturized and fully integrated including a low-noise amplifier (LNA), high-Q bandpass filter, passively conceived frequency mixer with low-phase-noise local oscillator (LO) and high-rate analog-to-digital conversion interface. The system is designed based on sophisticated 65 nm CMOS production technology and is working effectively at an operating frequency of up to 0.1-6GHz UWB and optimized in terms of implantable or wearable models. Front-end circuit-level Simulation and analysis range conjugated with Cadence and ADS determine a gain of 18.5 dB, noise figure of less than 1.2 dB and a 2.3 mW power consumption with an end-to-end latency of less than 5 microseconds, assured within the safe limits of biomedical applications. Co-simulation of electromagnetic in CST also verifies the validity of the RF signal pathway in realistic layout and packaging parasitics. A prototype was implemented in proof-of-concept cutting edge to test on synthetic brain signal emulators and wireless transmission systems having a robust performance with signal recovery below millivolts and minimum bit error rate (BER) in the context of mobility induced fading. Its high bandwidth, power, and signal integrity make the system a very promising technique in future consideration of implantable neural recording and stimulation systems. The present research establishes a basis of scalable, low-latency, and interference-tolerant RF telemetry in braincomputer interface living and paves the way to smart neuromodulation systems that provide real-time feedback.
Ultra-wideband (UWB), RF front-end, brain signal acquisition, neuroprosthetic devices, low-noise amplifier (LNA), real-time neural recording, implantable electronics, biomedical telemetry, CMOS design, wireless brain–machine interface (BMI).
Co-Simulation of ADS and CST for RFIC Performance Verification in Aerospace Communication Systems
The increasing application of high-performance communication systems in aerospace activities has spurred the prospect of advanced Radio Frequency Integrated Circuits (RFICs) which are fundamental to successful signal processing in the face of difficult activity environments. Simulation techniques used in the efficient and accurate simulation of RFICs can provide design flaws in the most perfect manner by mimicking what it will feel like under real-life circumstances to come up with the perfect performance in aerospace systems. The proposed co-simulation framework in this paper gives a new design that incorporates the Advanced Design System (ADS), and Computer Simulation Technology (CST) verifying the performance of RFICs in aerospace communication networks. The circuit level design/simulation tool so utilizes ADS (Ansys), and the full wave electromagnetic simulation tool CST (CST Global). The methodology allows a full RFIC selection to be performed that looks at several key components in an RFIC including low noise amplifier (LNA), power amplifier (PA), mixer, and oscillator. A set of case studies are performed to confirm the framework in which it is proven as improving the accuracy of simulation greatly, requiring less time to compute and optimize the performance of the calculation. The outcomes indicate that the single combined ADS-CST model provides a better estimate of RFIC performance resulting in more effective upper-level design of aerospace communication systems. To sum up, the suggested co-simulation environment turns out to be a beneficial tool to speed up the progression of RFIC and raise the dependability of aerospace communication systems.
Radio Frequency Integrated Circuits (RFICs), Co-Simulation, Advanced Design System (ADS), Computer Simulation Technology (CST), Aerospace Communication Systems, Electromagnetic Simulation
Antenna-in-Package (AiP) Integration Strategies for High-Frequency RFICs in 5G/6G Edge Devices: Design Challenges, Performance Trade-offs, and Future Opportunities
Within the context of super-fast development of 5G and perceived use of 6G wireless networks, the rate at which small, high-frequency and power efficient Radio Frequency Integrated Circuits (RFICs) that can drive edge intelligence grows fast. An attractive candidate solution is known as the Antenna-in-Package (AiP) technology, which is the concept of integrating both RF transceiver and antenna groups in the same, miniaturized component to reduce parasitic losses and provide a better performance at millimeter-wave (mmWave) and sub-THz frequencies. This paper seeks to explore the state-of-the-art AiP-based solutions to the high-frequency RFICs in the 5G/6G edge devices. The overall approach synthesizing the tools of literature review,comparative analysis, and the electromagnetic simulation considerations is used to determine the significant design bottlenecks. The most important of these difficulties are electromagnetic interference, heat risen outflow, interconnect dispenses, and constraints in substances material. Antenna gain versus beam-steering fidelity graph and form factor versus cost graph are performed. Other technologies discussed in the review include advanced packaging concepts, which include fan-out wafer-level packaging (FOWLP), system-in-package (SiP) and 3D heterogeneous integration. The paper finds a conclusion by pointing out some new research challenges such as reconfigurable AiPs, AI-enabled co-design processes, conformal flexible packages, and sub-THz antenna arrays to be used beyond 6G. The results herein offer a great basis of future research and implementation of next-generation wireless edge systems with scalable, high-performance AiP architectures to the industry.
Antenna-in-Package (AiP), Millimeter-Wave (mmWave), Radio Frequency Integrated Circuits (RFIC), 5G/6G Edge Devices, High-Frequency Packaging, Beamforming Antenna Arrays, Thermal Management in RFICs, System-in-Package (SiP), Electromagnetic Interference (EMI), 3D Heterogeneous Integration, Sub-THz Communications, Flexible and Conformal AiP, AI-Driven RF Design, Fan-Out Wafer-Level Packaging (FOWLP), Reconfigurable Antenna System
AI-Assisted Modeling and Optimization of RF Circuits Using HSPICE and Neural Metaheuristics
This paper suggests an AI-aided modeling and optimization approach to RF circuits, that combines the HSPICE simulation with the neural metaheuristic approaches, thus allowing amplifying the automation and accuracy of performance matching in circuits. Its first goal will be to overcome some of the drawbacks of manually sweeping parameters and using trial and error procedures in the common RF design process by adding a smart optimization loop that will improve important parameters including gain, return loss and power efficiency. The approach uses transistor-level modeling and waveform-accurate simulation by HSPICE and involves search of design space by a neural metaheuristic engine, i.e., the use of a hybrid neuralgenetic algorithm that explores the design space efficiently and computes no gradients. Iterative tuning is also automated so that fitness is judged using the result of the simulation and optimal parameters written back into HSPICE completing the loop. An example to prove the followed approach would be designing a Class-A RF power amplifier, at 2.4 GHz based on 0.18 micrometer CMOS technology. The optimization resulted in a 23% gain in small-signal gain, a 6 dB increase in return loss and a 30% decrease in weighted total harmonic distortion (THD) of the manually tuned designs. The difference in time of performing optimization exceeded 80 per cent, which illustrates the efficiency of the framework. The findings indicate that intensive applications of AI to conventional circuit simulators can tremendously shorten the RF design cycles and enhance the outcomes of performance. This project forms a starting point towards next generation automation of RF design across wireless and communication systems.
RF Circuit Design, HSPICE, Neural Metaheuristics, AI Optimization, Gain Enhancement, Return Loss, Power Efficiency, Circuit Automation
OTA and Hardware-in-the-Loop Testing of an SDR-Based RF Transceiver for 5G Applications
Being a fast-growing sphere of wireless communication where 5G reinvents the roadmap, the router of Software-Defined Radio (SDR)-based RF transceiver performance has been of great importance in demonstrating the efficacy of conventional systems and their readiness in meeting the strict standards as expected in the deployment process. Standard RF test techniques usually do not emulate the elaborate, dynamic propagation environments emblematic of 5G trials, especially those associated with the sub-6 GHz frequencies, along with millimeter-wave (mmWave) range. In order to fill this gap, this paper presents an overview of an end-to-end Over-the-Air (OTA) and Hardware-in-the-Loop (HIL) testing system that can combine real-time signal generation, adaptive feedback and channel emulation to test RF transceivers based on SDR technology. The test system is based on Universal Software Radio Peripheral (USRP) X310 platforms to test sub-6 GHz and National Instruments mmWave transceiver platforms to test 28 GHz. The OTA tests are made in both types of testing rooms viz. the anechoic room and reverberation rooms to characterize the system performance within the optimal environment and a multi-path environment. The HIL element, meanwhile, will utilize real-time FPGA based feedback loops that interface with the LabVIEW and MATLAB/Simulink-based environments to test closed-loop communication under closed-loop channel, user mobility and scenario interference by relying on simulated dynamic channel models. The key performance indicators such as Error Vector Magnitude (EVM), Bit Error Rate (BER) and Spectral Emission Compliance and the Beamforming Latency are measured and analyzed with reference to the 3GPP TS 38.104 standard. Findings show that the integrated testbed is an excellent way to capture the responsiveness of the transceiver to changes in the environment, real-time beam-steering, and adaptive modulation and coding scheme validation. Not only the proposed testing plan shortens the validation time, but the accuracy of the prototyping is improved and this testing procedure may be scaled to any complexity while still being cost-effective to test 5G and up-and-coming 6G transceivers before being deployed. The proposed research will also provide a useful validation process to filler the gap that exists between theoretical development of SDRs and practicality in real world performance testing thereby speeding up the realization of intelligent and reconfigurable wireless front-end systems for next generation communication networks.
5G Transceiver, Software-Defined Radio, OTA Testing, Hardware-in-the-Loop, USRP, mmWave, Beamforming, 3GPP Compliance, SDR Testbed.
Hardware-in-the-Loop Validation of SDR-Based Reconfigurable Transceivers for Tactical Wireless Communication Systems
The hardware-in-the-loop (HIL) validation framework proposed in this paper is aimed at the validation of software-defined radio (SDR)-based reconfigurable transceivers which are designed as part of wireless communication systems to operate as tactical applications. Such systems require strong and flexible operations in fast evolving, spectrum-congested and interference-prone scenarios. The proposed architecture integrates the ability to model the signals using MCRD and the SDR hardware resources in a co-simulation environment that enables closed-loop operation (across a large number of tactical scenarios). The important characteristics of the transceiver are adaption modulation schemes, and real-time channel equalizations, dynamic spectrum access, and FPGA acceleration of the baseband processing. The USRP B210 is employed to perform physical-layer transmission and reception in the HIL platform, and channel emulation modules are used that simulate urban multipath fading, Doppler shifts and jamming. As it is experimentally proven, bit error rate (BER) decreases dramatically under hostile RF circumstances under the adaptive modulation scheme up to 65 percent BER improvement and frequency hopping scheme up to 42 percent improved spectral efficiency. The model also attains sub-5 ms reconfiguration latency, which qualifies it as appropriate for time-bound functions. The findings affirm that the HIL approach can be used in benchmarking the undeniably live performance of SDR-based transceivers before they are released to the field. The suggested framework is both scalable and expandable where missions-critical RFsystems at different frequency bands and network topologies have been assessed. The future development designs will introduce cognitive adaptation by use of AI and multi-node tactical mesh networking abilities.
Hardware-in-the-Loop (HIL) Simulation, Software-Defined Radio (SDR), Reconfigurable Transceiver, Tactical Communication Systems, FPGA-Based Signal Processing, Adaptive Modulation, Dynamic Spectrum Access, Real-Time RF Validation, Bit Error Rate (BER), Channel Emulation.
Design and Performance Analysis of an Envelope-Tracking Doherty Power Amplifier for 6G Wireless Base Stations
The transition to 6G wireless communication networks requires installation of energy-efficient, high-linearity power amplifiers (PAs) as the base station RF-front-ends to address ultra-high data rates, massive connections, and onerous power limitations. The conventional PA architectures are characterized by low efficiency of the power back-off point which is experienced during power back-off especially under high peak to absolute power ratio ( PAPR ) waves which appear in broadband 6 g signals. In the efforts to solve this challenge, this paper develops a high-efficiency Doherty Power Amplifier (DPA) architecture with an Envelope Tracking (ET) that will focus on enhanced back-off efficiency and linearity of a 6G base station. The principle aim of the present work is designing, simulating and analysing an ET-enabled DPA implemented with GaN High Electron Mobility Transistor (GaN HEMT) at the 28 GHz mmWave frequency. The suggested design augments a Class-AB principle (load modulation) fundamental amplifier and a Class-C peak amplifier, whereas the ET module adjustably adjusts the drain voltage on an envelope-based correction of the RF signal to decrease power waste in terrain-off differentiations. Circuit level co-design and simulation is employed at both RF behavior, with Advanced Design System (ADS), and dynamic power supply response, with HSPICE simulation, levels. The matching network is matched in order to maximize the power transfer, and the load-pull simulations are applied to receive the best impedance conditions. Simulation results indicate that the proposed ET-DPA paves a way to a peak power-added efficiency (PAE) of 67%, small-signal gain of 13.8 dB and adjacent channel power ratio (ACPR) of -28 dBc. In addition, the amplifier achieves high efficiency (53%) at 6 dB output back-off and represents significant enhancement of conventional DPA designs. To recap it all, during envelope tracking in a Doherty architecture, an immense improvement is noticed in power efficiency and linearity in high-frequency 6G communication systems. The work provides one of the possible design strategies of energy-aware base station power amplifiers working within mmWave bands, as well as that paves the way to experimental verification and real deployment.
6G base stations, Doherty Power Amplifier (DPA), Envelope Tracking (ET), RF front-end, GaN HEMT, energy efficiency, power-added efficiency (PAE), mmWave communication, ADS simulation, linearity enhancement.
ADS–CST Co-Simulation and Performance Evaluation of MMIC Power Amplifier for Millimeter-Wave Automotive Radar Applications
This paper shows a detailed co-simulation guided design and analysis of a monolithic microwave integrated circuit (MMIC) power amplifier (PA) to be used in millimeter-wave (mmWave) automotive radar with voltage-controlled oscillator (VCO) operating at 76-81GHz band. The suggested technique is comprised of Keysight Advanced Design System (ADS) (nonlinear, circuit-level synthesis) and CST Microwave Studio (full-wave, 3D, electromagnetic (EM) simulations) to help it perform package-aware as well as prove to be layout-accurate. The MMIC PA is realized in 0.15 µm GaAs pHEMT technology and it has a two-stage common-source topology with a high-gain, power-added efficiency (PAE) optimisation. The early-stage design in ADS incorporates load-pull simulation and harmonic balance analysis of the design combined with simulation in CST to model the EM effects due to layout parasitics, ground discontinuities and interconnects. Co-simulation output performance shows small-signal gain peaking at 21 dB at 77 GHz, having a saturated output of +18.2 dBm as well as a peak PAE of 32% all with equally good input and output matching (S11 < -10 dB). The amplifier has realistic substrate and bias conditions and remains thermally stable and electromagnetically sound. The co-design method minimizes the differences between schematic and fabricated performance which facilitates reduced prototyping time and lead time and the robustness of the design. This research emphasizes that EM-circuit co-simulation plays a very crucial role in mmWave MMIC design and determines a proven approach to the design of high-performance compact power amplifiers in new generation vehicles radar and 6G communications applications.
MMIC Power Amplifier, ADS-CST Co-Simulation, Millimeter-Wave, Automotive Radar, 77 GHz, GaAs pHEMT, Power-Added Efficiency, EM-Circuit Integration
Wireless RF Sensor Network Architecture for Real-Time Damage Detection and Health Assessment in Smart Bridges
Real-time SHM systems of bridges are developing due to the growing demand of resilience and intelligence of infrastructure. As effective as the traditional wired solutions may be, they are not scalable, have a complex installation process, or have high maintenance burdens. This work suggests a wireless RF sensor system that has the capability to detect damages and evaluate the structure of the smart bridge in real-time (verified by simulation). Integrated onto distributed sensor nodes are vibration and strain sensors, low-power RF transceivers (LoRa/Zigbee), and edge-processing microcontrollers within a system that can be used to autonomously collect and locally analyse data, and transmit to a central monitoring unit wirelessly. The suggested architecture can deal with adaptive sampling, energy-efficient communication rules, and strong coverage of bridge structures with long spans. Packet delivery reliability is high (>98%) and damage alerts have sub-second latency with simulation and testbed evaluations showing node lifetime of over 24 months during the typical duty-cycled operation. In addition, the system realizes great sensitivity to pick structural abnormalities like microcracks and joint displacements. The findings confirm the viability and the robustness of the suggested RF basis WSN framework as the viable option to support the realm of constant, real-time SHM in the contemporary smart bridge infrastructure.
Wireless Sensor Network (WSN), Structural Health Monitoring (SHM), Smart Bridges, RF Communication, Damage Detection, Real-Time Monitoring, Low-Power Design, LoRa, Zigbee, Edge Processing, Infrastructure Safety, Sensor Node Architecture
Physical Layer Key Generation Using Channel Reciprocity for Secure Wireless Body Area Networks
Reliable communication over Wireless Body Area Networks (WBANs) is an essential aspect of Wireless Body Area Networks (WBANs) because the physiological data flows between wearable sensor nodes is sensitive data. The convectional cryptography methods usually apply a lot of computational complexity as well as energy overhead besides which they are not going to fit in the ultra-low-power WBANs. In the current paper a light-weight, energy-efficient Physical Layer Key Generation (PLKG) scheme the possibilities of which based on the native randomness and reciprocity of the wireless channel is used to generate secure symmetric keys is presented. With WBANs, there is a lot of temporal and spatial dynamics in the human body and its surrounding environment, which causes great variability in channel characteristics, in terms of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). Such physical layer parameters can be used as perfect entropy sources in generation of unique, unpredictable secret keys with whose knowledge, communication between nodes is possible and mutually exclusive only. The channel probing in the proposed framework is also robust, multi-bit quantization is performed adaptively, information reconciliation is performed through error correction codes, and privacy amplification is made through cryptographic hashing. We have developed a resilient system against bit mismatch and efficiently against passively eavesdropping, we guarantee the secrecy of the generated keys under adversary situation. With realistic WBAN models simulation and experiment validation shows that the proposed approach is capable of generating keystokes with very high key generation rate (50 bps) and a bit mismatch rate of lower than 3 percent as compared to existing schemes that solely relied on RSSI, making this protocol more secure and reliable to use. In addition, the framework meets the statistical randomness requirements specified by NIST and it does not leak mutual information to eavesdroppers. The scheme allows real-time use on resource-constrained devices, e.g., wearable medical devices and health-monitoring sensor, as it has low computational and memory overhead. The findings establish the feasibility of employing physical layer properties to achieve secure WBANs communication without any pre-shared keys or a centralized trust paradigm, thus, opening the way to scalable and privacy-preserving healthcare systems.
Physical Layer Security, Channel Reciprocity, Wireless Body Area Networks (WBANs), Key Generation, RSSI, Channel State Information (CSI), Quantization, Information Reconciliation, Secure Communication, Entropy Extraction