Radio Propagation Research Articles

Influence of atmospheric turbulence on the spectral composition of the radio signal

Background. It is shown that it is necessary to study the effect of atmospheric turbulence on the spectral characteristics of a radio signal. Aim. The influence of atmospheric turbulence on the spectral fluctuations of the radio signal intensity and on the displacement of the spectral components of the radio signal has been studied. Methods. The research was carried out on the basis of an analysis of the relationship between two-wave and single-wave correlation ratios. Based on the solution of the differential equation for fluctuations of the eikonal amplitude of an electromagnetic wave and the use of the derived trigonometric ratio, a connection between the two-wave Fourier=spectrum and single-wave spectra is obtained. In this case, a single source of turbulence effects on the radio signal was used at the coordinate point of the radio wave propagation by introducing a new variable equal to the average value of the coordinates of the turbulence effect. To find the resulting double integral, one of the coordinates of the turbulent action is transformed into an angular variable. Results. The dependence of the relative dimensionless mean of square of the integral intensity of the radio signal fluctuations on the wave number of turbulent pulsations of the atmosphere at various displacements of the spectral wavelengths of the radio signal is found. Conclusion. It is shown that turbulence slightly distorts the spectral information essence of a propagating radio signal in various wavelength ranges.

Optimal Capacitance Design for IRS Aided Wireless Power Transfer for Sustainable IoT Communication

ABSTRACTIntelligent reflecting surface (IRS) is a cutting‐edge technique that can significantly improve wireless propagation. It can efficiently utilize wireless power transfer to enable sustainable Internet‐of‐Things (IoT) transmission by reconfiguring the incident signal from the active transmitter. However, the flexibility of capacitance tuning in the IRS system, which controls underlying reflections, is often overlooked. The effective capacitance design in the IRS system can provide a new degree of freedom in the IoT communication system, which can enable additional performance gain in the received power. To achieve this, a novel IRS circuital optimization model is proposed in this work. It incorporates various electrical parameters of the meta‐surface unit cell for improved IoT‐enabled communication. The proposed optimization model provides an optimal capacitance as a function of phase shift (PS), which is controlled by IRS, incident frequency, and other IRS electrical parameters. This optimal capacitance is then used to define the received power. The convexity of the optimization problem is proved, and the global optimal capacitance is obtained for received power maximization. Our simulations show that the proposed optimization model outperforms the existing constructive interference‐based optimal PS method, for which the capacitance is first calculated. Finally, the analytical results are numerically validated with several optimal design insights.

Time series forecasting of rain-induced attenuation using coupled ARIMA-SARIMA models for radio propagation applications over subtropical locations

This present research aimed to identify the optimum model for predicting the rain-induced attenuation along the terrestrial and satellite communication networks and employed the time series forecasting models of Autoregressive integrated moving average (ARIMA) and Seasonal Autoregressive integrated moving average (SARIMA) to examine and contrast several techniques for predicting rain-induced attenuation across a sub-tropical area of South Africa. In this research, the ARIMA was developed using the Box and Jenkins technique to predict long-term rain-induced attenuation in the following South African provinces: Kwazulu-Natal, Eastern Cape, Gauteng, and Northern Cape. The datasets used for the research were obtained from the South African Weather Station from 1994–2023 were used to build and check the model after generating rain-induced attenuation using synthetic storm techniques (SST). The forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model and that of autoregressive integrated moving average (ARIMA) were compared with four forecast performance measures: - Mean Squared Error (MSE), Mean Absolute Error (MAE), R- squared or the coefficient of determination (R2) and Root Mean Squared Error (RMSE). The results of the study showed that due to its lower forecast performance error indicators, SARIMA performed better than ARIMA in forecasting rain-induced attenuation in South Africa. There is no statistically significant difference between the two projected values, according to the results of a Ljung-box test for significant difference. The authors draw the inference that the two approaches can effectively be employed in their proper locations. Rain-attenuation prediction data indicates that this long-term projection can help decision makers to improve the performance of microwave networks in the face of random fluctuations and avoiding unexpected signal loss with efficient installation of communications infrastructure. It also has applications in prediction of floods, urban planning, and crop management.

SOAMC: A Semi-Supervised Open-Set Recognition Algorithm for Automatic Modulation Classification

Traditional automatic modulation classification methods operate under the closed-set assumption, which proves to be impractical in real-world scenarios due to the diverse nature of wireless technologies and the dynamic characteristics of wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms of detection effectiveness and computational complexity. To address the limitations of modulation classification and recognition in open-set scenarios, this paper proposes a semi-supervised open-set recognition approach, termed SOAMC (Semi-Supervised Open-Set Automatic Modulation Classification). The primary objective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples is manually labeled. The proposed method consists of three key stages: (1) A signal recognition pre-training model is constructed using data augmentation and adaptive techniques to enhance robustness. (2) Feature extraction and embedding are performed via a specialized extraction network. (3) Label propagation is executed using a graph convolutional neural network (GCN) to efficiently annotate the unlabeled signal samples. Experimental results demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limited amounts of labeled data and high signal similarity. These findings are critical for the practical identification of complex and diverse modulation signals in real-world wireless communication systems.

Machine learning-based intelligent localization technique for channel classification in massive MIMO

Multiple-input multiple-output (MIMO) technology has been widely adopted in wireless communications, which enables the simultaneous transmission of multiple data streams via multiple transmitting and receiving antennas. In a MIMO system with non-line-of-sight (NLOS), transmitted signals are reflected by various obstacles along the path, reaching the antenna at different angles and times. In 5G networks, the NLOS problem is a major challenge for massive MIMO localization, significantly reducing positioning accuracy. In this work, an intelligent localization technique based on NLOS identification and mitigation is proposed to address this problem. In this solution, a Convolutional Neural Network (CNN) based hybrid Archimedes-based Salp Swarm Algorithm (HASSA) technique is proposed to detect NLOS or the line of sight (LOS) and estimate the location. The accuracy can be analyzed by considering the angle of arrival of signals, threshold-based time of arrival, and time difference of arrival from different antennas. A novel reinforcement learning-based optimization approach is used for the mitigation of NLOS in the radio wave propagation path, which in turn reduces the computational complexity. We use the Ensemble Deep Deterministic Policy Gradient-Based Approach (EDDPG)-based Honey Badger algorithm (HBA) for the aforementioned process. The simulation of this approach assesses diverse scenarios and considers different parameters, and the approach is compared with various state-of-the-art works. From the simulation results, our proposed approach can be used for the identification and detection of LOS and NLOS components and can precisely enhance the localization compared with other approaches.

Performance Evaluation of Radio Frequency Visualisation Tool for Wireless Communications: A UNIUYO Campus Case Study

This study was motivated by the challenge of evaluating radio frequency (RF) signal behaviour concerning transmitter and receiver positioning prior to deployment and during troubleshooting. This paper presented the development and testing of a MATLAB-based signal visualisation application to enhance RF signal analysis across various frequencies and terrains, specifically focusing on the University of Uyo (UNIUYO) permanent site and selected locations in Uyo. The application featured three distinct tabs with specialised functions. The first tab integrated campus radio broadcasts at 144.5 MHz and visualised signals for mobile communication standards (2G, 3G, and 4G). The second tab offered a computation interface for selected radio propagation parameters and a tool for converting geographic coordinates from degrees, minutes, and seconds (DMS) to decimal degrees (DD). The third tab was dedicated to 5G testing and deployment. To determine the signal travel extent for each transmitter location, the Longley-Rice and Freespace propagation models were used to represent worst-case and best-case scenarios, respectively. The results, illustrated graphically, demonstrated the application’s effectiveness and versatility in RF signal analysis and 5G deployment planning. The study explored two primary 5G deployment strategies: the standalone (SA) approach and the non-standalone (NSA) technique. The SA strategy employed mid-band frequency (3.5 GHz) point-to-multipoint stations within clusters. The application utilised up to 19 transmitters (one main transmitter and 18 repeaters) per cluster. Conversely, the NSA technique integrated 5G technology with the existing 4G infrastructure to enhance coverage. The findings offer valuable insights into optimising 5G deployment in terrains similar to those at the UNIUYO permanent site and Uyo in general.

Performance of advanced-deep learning algorithm for modeling and prediction of rain height over some selected locations in the tropics and sub-tropics zone for radio propagation applications

As demand for high-frequency broadband communication services keeps rising, rain-induced attenuation remains the predominant threat to radiowave propagation. Accurate prediction of attenuation requires continuous measurement and monitoring of rain-induced meteorological parameters, specifically rain rate and rain height, due to their spatio-temporal variations. Rain height is an upper dataset mostly computed from Zero- degree Isotherm Heights (ZDIH) measured by radar. This research proposes a novel approach for predicting rain height from earth surface data such as surface temperature, pressure, total cloud cover, dew point temperature, surface solar radiation, water vapor amount in the air, and humidity. This research investigates the relationship between meteorological surface data and rain height. Subsequently, six machine learning models were employed for predicting rain height using the ten years surface data as input variables. The models were applied to six sub-tropical (Polokwane, Pretoria. and Cape Town) and tropical (Sokoto, Akure, and Port Harcourt (PH)) stations in South Africa and Nigeria, respectively. Analysis of the results shows that the Gradient Boosting Algorithm (GBA) performed best with determination coefficients greater than 0.80 and RMSE less than 350 in all three stations in South Africa. However, all the models failed to produce good result for the Nigeria stations. The Random Forest model has the fairest performance metrics with r2 of 0.40, 0.46 and 0.46 in Sokoto, Akure and PH. respectively. GBA is recommended for predicting rain height in South Africa. The research outcome would assist radio engineers in improving the prediction of rain-induced attenuation and determining appropriate fade mitigation techniques.

Design of a High-Power Nanosecond Electromagnetic Pulse Radiation System for Verifying Spaceborne Detectors

The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics.

Shadow fading analysis over asymmetric beam channel at 2.1 GHz in outdoor scenarios

AbstractIn this letter, we investigate the radio propagation characteristics in an open outdoor place through a frequency‐domain channel measurement campaign at a frequency band of 2.1 GHz via asymmetric beam patterns. Specifically, we focus on the large‐scale fading characteristics and analyze the transceivers' separation‐dependent and boresight direction‐dependent received power, shadow fading (SF), and SF auto‐correlation properties under the case of perfect beam alignment. Furthermore, the effects of asymmetric beamwidths are also thorough investigated. The measured results reveal that transceivers' beams can reduce the shadow fading variances evidently in contrast to the omnidirectional antennas' case. Besides, it is found that the pattern where mobile receiver (Rx) uses narrow beams and static transmitter (Tx) adopts the omnidirectional antenna yields a high SF auto‐correlation value compared to the other beam patterns. The conclusions help to understand the impacts of variable beamwidths on radio propagation.

An Environment-Adaptive Radio Propagation Path Loss Model With Ray-Based Validation

An Environment-Adaptive Radio Propagation Path Loss Model With Ray-Based Validation

A Software Tool for the True Height Analysis of Ionograms Using the Iterative Gradient Correction (IGC) Method

AbstractDeriving the precise true height electron density profile from the measured ionosonde virtual heights is quite a challenging problem. Recently, Ankita and Tulasi Ram (2023, https://doi.org/10.1029/2023RS007808) presented a new method, Iterative Gradient Correction (IGC) method, for true height analysis that uses HF radio wave propagation path computations to reconstruct the true height profile. Through iterative corrections on electron density gradients between successive points, the IGC method minimizes errors below a specified tolerance at each point and reconstructs a complete electron density profile. The derived profiles from the IGC method are found to be accurate when compared with Incoherent Scatter Radar and Global Navigation Satellite System—Radio Occultation observations. To facilitate true height analysis by IGC method for a wider user community, a MATLAB‐based software has been developed and is outlined in this report. The software can be installed on any Windows platform and is designed with a user‐friendly interface for easy and efficient application by the users. It can analyze multiple scaled ionograms in a single run and outputs the real height profiles in ASCII format. Further, the software also captures important ionospheric parameters such as the base altitudes and peak frequencies of E‐ and F‐layers (e.g., hE, hF, foE, and foF2) etc., from the computed true height profiles and tabulates in a separate output file for the ready use. The software also provides the option for extrapolation of true height profile into top‐side ionosphere up to a user‐specified height and reconstructs the complete vertical electron density profile.

Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches.

Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey

The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.

Beamforming for Multi-Bit Intelligent Reflecting Surface with Phase Shift-Dependent Power Consumption Model.

In recent years, the intelligent reflecting surface (IRS) has attracted increasing attention for its capability to intelligently reconfigure the wireless propagation channel. However, most existing works ignore the dynamic power consumption of IRS related to the phase shift configuration. This relationship gets even more intractable for a multi-bit IRS because of its nonlinearity and implicit form. In this paper, we investigate the beamforming optimization for multi-bit IRS-aided systems with the practical phase shift-dependent power consumption (PS-DPC) model, aiming at minimizing the power consumption of the system. To solve the implicit and nonlinear relationship, we introduce a selection matrix to explicitly represent the power consumption and the phase shift matrix of the IRS, respectively. Then, we propose a generalized Benders decomposition-based beamforming optimization algorithm in the single-user scenario. Furthermore, in the multi-user scenario, we design a coordinate descent-based algorithm and a genetic algorithm for the beamforming optimization. The simulation results show that the proposed algorithms significantly decrease the power consumption of the multi-bit IRS-aided systems.

Multi-Cluster Approaches to Radio Sensor Array Channel Modeling and Simulation.

In this paper, we explore the physical propagation environment of radio waves by describing it in terms of distant scattering clusters. Each cluster consists of numerous scattering objects that may exhibit certain statistical properties. By utilizing geometry-based methods, we can study the channel second-order statistics (CSOS), where each distant scattering cluster corresponds to a CSOS, contributes a portion to the Doppler spectrum, and is associated with a state-space multiple-input and multiple-output (MIMO) radio channel model. Consequently, the physical propagation environment of radio waves can be modeled by summing multiple state-space MIMO radio channel models. This approach offers three key advantages: simplicity, the ability to construct the entire Doppler power spectrum from multiple uncorrelated distant scattering clusters, and the capability to obtain the channels contributed by these clusters by summing the individual channels. This methodology enables the reconstruction of the radio wave propagation environment in a simulated manner and is crucial for developing massive MIMO channel models.

Measurement-Based Tapped Delay Line Channel Modeling for Fixed-Wing Unmanned Aerial Vehicle Air-to-Ground Communications at S-Band

Fixed-wing unmanned aerial vehicles (UAVs) are widely considered as a vital candidate of aerial base station in beyond Fifth Generation (B5G) systems. Accurate knowledge of air-to-ground (A2G) wireless propagation is important for A2G communication system development and testing where, however, there is still a lack of A2G wideband channel models for such a purpose. In this paper, we present a wideband fixed-wing UAV-based A2G channel measurement campaign at 2.7 GHz, and consider typical flight phases, based on which a wide-sense stationary uncorrelated scattering (WSSUS)-based tapped delay line (TDL) wideband channel model is proposed. Parameters of individual channel taps are analyzed in terms of gain, amplitude distribution, Rice factor and delay-Doppler spectrum. It is shown that UAV flight phases significantly influence the channel tap parameters. Particularly, the “Bell”-type spectrum is found to be the most suitable model for the delay-Doppler spectrum under various flight scenarios for A2G propagation. The proposed channel model can provide valuable assistance and guidance for UAV communication system evaluation and network planning.

Performance Analysis of Tropospheric Radio Refractivity along with Refractivity Gradient and Effective Earth Radius in the Coastal Zone of Nigeria

Study’s Excerpt Analysis of the variability in tropospheric radio refractivity in Ogoja and Warri, Nigeria, was carried out by examining a 42-year dataset (1981-2022) of meteorological factors obtained from NASA. The contributions of dry and wet terms to radio refractivity variations were identified and key parameters such as refractivity gradients and the effective earth radius (k-factor) for both locations were evaluated. The radio wave propagation in the region is lowest during the dry season. Full Abstract Radio refractivity is the ratio of the velocity of radio waves in a specific medium to that in free space. Radio waves propagate according to variations in tropospheric radio refractive index. This study used the refractive index and other pertinent meteorological factors to calculate the radio refractivity of the seasonal troposphere and analyse its variability with monthly temperature, relative humidity, and atmospheric pressure measurements that were collected from the National Aeronautic and Space Administration (NASA) for Ogoja and Warri over a forty-two-year period (1981 to 2022). We looked at the proportion contributions of the dry and wet term radio refractivity, the refractivity gradient, and the effective earth radius. The outcome signified that in the two locations, radio refractivity values were lowest for the dry season and highest during the rainy season. For Ogoja and Warri, the highest and lowest average radio refractivity values recorded in the wet and dry seasons are, respectively, 382.9085 N-units in May, 346.4311 N-units in January, and 390.9042 N-units in April, 370.3009 N-units in January. For Ogoja and Warri, the wet term (Nwet) provides 31.5210 % and 30.1793%, respectively, to the primary variation in radio refractivity's overall value, whereas the dry term (Ndry) adds up to 69.8207 % and 68.4790 %. Mean refractivity gradients of -43.8326 and -44.5326 N-units/km were found in the subject areas under examination. Furthermore, it was discovered that the mean effective earth radius (k-factor) for Warri and Ogoja were 1.3959 and 1.3873, respectively. The values provided represent the propagation conditions of superrefraction.

A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 MHz, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)-LoRa, etc.

Performance Evaluation of Reconfigurable Intelligent Surfaces-Assisted Millimeter-Wave Network Using Stochastic Geometry-Based Model

Millimeter-wave (mm-wave) bands have received significant attention due to their large bandwidth and high frequency. However, mm-wave signals experience high attenuation primarily as a result of their high directivity and vulnerability to blockage, leading to non-line-of-sight (NLOS) conditions and signal outages. Buildings, structures, and natural obstacles can cause signal blockage, which increases connection challenges and creates coverage gaps. A reconfigurable intelligent surface (RIS) is a potential technique that can improve the coverage of mm-wave networks by intelligently reconfiguring the wireless propagation environment. RIS offers novel solutions to blockage issues by passively reflecting and rerouting mm-wave signals in desired directions. This paper presents a simulation-based evaluation of the signal-to-interference ratio coverage probability in RIS-assisted mm-wave networks based on stochastic geometry. We will use random shape theory to represent the blockages within the network. Furthermore, the impact of increasing the density of RIS and the number of reflective elements on network performance will be examined. The results show that RIS can enhance network coverage and decrease the effects of blockages, with considerable increases in coverage probability when compared to networks without RISs.

Performance modeling of composite fading channels: an application to wearable wireless communication systems

In pragmatic wireless propagation scenarios, communication between wearable devices may be significantly impacted by fading, particularly when wireless communication is crucial for mission control, situational awareness, and team coordination in defense operations. There may be times when communication links are temporarily unavailable in places due to contemporaneous effect of slow fading and fast fading, including cities or regions with tough terrain. This phenomena results in composite fading environments. Moreover, the data transfer speeds may drop as a result of fading, making it a challenging task to rapidly transmit crucial real time data. In addition, the precision of GPS and positioning systems, that are essential for operations viz., target tracking, navigation and rescue can be impacted by fading. In this setting, the work portrays modeling the fading channels over the Weibull distribution by exploiting Tsallis’ q-statistical framework. From the analytical observations, it was evident that in contrast to the well-known composite models, the q-Weibull model provides a better characterization of the generated fading signals. Furthermore, the closed form solution to the different performance metrics that are crucial in characterizing the fading environments are provided.