This paper introduces two comprehensive composite fading models: the α-FMixture and the α-GMixture. These models unify a broad spectrum of previously reported composite fading distributions and enable the systematic construction of hundreds of new ones. Their first-order statistics (namely, probability density function (PDF), cumulative distribution function (CDF), moment-generating function, and higher-order moments) are derived in compact, tractable forms that, for specific cases, yield singularity-free expressions improving upon existing state-of-the-art solutions. By consolidating a wide range of multiplicative shadow (MS) and LOS shadow (LOSS) fading families into a single parameterized framework, they provide a unified analytical basis for obtaining the key statistics of numerous composite fading scenarios. Serving asplug-and-playbase models, they enable direct evaluation of metrics such as outage probability (OP) and average symbol error rate (ASER) for both established and new composite fading models without rederiving channel statistics. Their unified structure and flexible parameterization also support systematic analysis of fading and shadowing in emerging scenarios, providing practical and extensible tools for next-generation wireless channel characterization.

Fluid antenna systems (FASs) offer a promising solution to enhance communication reliability by dynamically exploiting spatial diversity. Although FAS has shown potential in static scenarios, its application to vehicular communication has not yet been explored. In this context, this paper is the first to investigate channel estimation for vehicular communication systems employing FAS, specifically based on the IEEE 802.11p standard. To this end, a port selection method is proposed, leveraging the preamble-based information to identify the optimal FAS port for efficient reception. Conventional estimation and deep learning (DL)-based approaches are compared to evaluate their performance in providing accurate channel estimates under realistic vehicular conditions, including nonlinear effects. Our results present the superior performance of FAS-aided communication systems over single-antenna ones, highlighting their adaptability to dynamic environments and varying system and channel conditions. In particular, the proposed system achieves a gain of at least 8 dB compared to single-antenna transmission. To further enhance reception reliability in high-mobility scenarios, a maximum ratio activated-port combination method is explored, providing an additional gain of at least 15 dB using only two activated ports.

In this work, we delve into the performance of the recently proposed cosine feature recognition detection (CRD) spectrum sensing technique, which leverages the phase difference distribution of the received signal. The analysis takes into account impulsive noise (IN) and noise uncertainty (NU), both of which are well-known factors that degrade communication system performance. The conventional energy detector (ED) approach serves as a benchmark for comparison. The simulation results indicate that the CRD technique achieves near-perfect robustness in the presence of IN and excellent performance under NU, whereas the performance of ED is drastically degraded under these harsh conditions. These findings provide valuable insights into the trade-offs between detection complexity and robustness, contributing to the design of more reliable spectrum sensing systems for cognitive radio networks.

Automated modulation classification (AMC) is crucial in electronic warfare because it enhances situational awareness and prompts responses to hostile transmissions. This research addresses the challenges of deploying AMC in limited computational environments by proposing using response-based knowledge distillation (KD) to train compact yet accurate AMC models. The methodology involves a framework that integrates a signal-based convolutional neural network (SBCNN) and an image-based convolutional neural network (IBCNN). The SBCNN extracts features from preprocessed signal data, which is subsequently used to train the IBCNN. Experimental results indicate that the SBCNN-based approach, when trained with teacher distillation, achieves superior performance compared to its standalone counterpart. Our findings show that KD has significant potential to enhance AMC performance in real-time applications by balancing computational demands with classification accuracy.

In this paper, we investigate a novel representation of a band-limited and continuous-time signal using a sequence of impulses or events. To begin with, we evaluate the mean squared error between the original signal and its reconstructed version assuming that: (i) the signal samples are transmitted at the Shannon channel capacity; (ii) the signal representation using the cosine phase method is done before its transmission. The proposed approach is then compared with an existing approach based on the signal transmission of quantized samples. Finally, we provide some representative examples through Monte Carlo simulations, consistently revealing the superiority of the proposed signal representation approach over the existing one regarding transmission bandwidth and peak-to-peak value of the original signal.

The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement for wireless communication systems. However, acquiring accurate CSI, which can be done through channel estimation or channel prediction, is an intricate task due to the complexity of the time-varying and frequency selectivity of the wireless environment. To this end, we propose an efficient machine learning (ML)-based technique for channel prediction in orthogonal frequency-division multiplexing (OFDM) sub-bands. The novelty of the proposed approach lies in the training of channel fading samples used to estimate future channel behavior in selective fading.

This work introduces a new perspective for physical media sharing and radio access in multiuser communication by jointly considering (i) the meaning of the transmitted message and (ii) its function at the end user. Specifically, we have defined a scenario where multiple users (sensors) employ an event-trigger transmission scheme to communicate their own state value concerning a predetermined event. On the receiver side, there is an alarm monitoring system, whose function is to decide whether such a predetermined event has happened in a certain time period and, if yes, in relation to which user. The proposed solution leverages the lack of explicit use of a predetermined spectrum resource – which defines an energy slot – to infer that no event has happened, thus building an implicit communication channel. When an event occurs at a given sensor, a sequence of “use” and “lack of use” instances for such a spectrum resource will be transmitted as a sequence of “on” and “off” values with respect to the occupancy of the energy slot. The receiver’s role is to detect such a sequence to identify that an event has happened and in which sensor. This direct map from the data acquisition to the transmission is the basis of semantic-functional communication. The challenge of this scheme is to construct a coding–decoding scheme where the receiver can effectively recover the states of the different sensors (in terms of the occurrence of the events) with high fidelity. We have demonstrated the high efficacy of the proposed nonorthogonal solution when the transmitters select a random code, and the receiver only detects whether the energy slot is used (i.e., there is no actual demodulation so that there is no packet collision when two or more sensors are transmitting). We have shown the proposed method leads to a better event transmission efficiency than an adaptation of well-known techniques like the orthogonal-based TDMA and the random-access-based slotted ALOHA when the same number of limited resources is employed. Remarkably, for almost all studied cases, the proposed method asymptotically achieves 100% efficiency and 0% error probability, while consistently outperforming TDMA and slotted ALOHA variations.

In this paper, the privacy of wireless transmissions is improved through an efficient technique termed DDM and is subsequently assessed in terms of the measure of information leakage. Recently, a variation of DDM termed LPDDM has attracted significant attention as a prominent secure transmission method owing to its ability to improve the privacy of wireless communications. Roughly speaking, this modulation operates by randomly selecting the transmitting antenna from an antenna array whose radiation pattern is known. Thereafter, the modulator adjusts the constellation phase so as to ensure that only the legitimate receiver recovers the information. To begin with, we highlight some privacy boundaries inherent to the underlying system. In addition, we propose features that the antenna array must meet in order to increase the privacy of a wireless communication system. Last, we adopt a uniform circular monopole antenna array with equiprobable transmitting antennas in order to assess the impact of DDM on information leakage and the BER.

In this paper, the performance of a digital communication system subject to $\kappa-\mu$ Extreme fading conditions is investigated. Specifically, exact and novel analytical expressions for the normalized average channel capacity and average error rate are derived assuming arbitrary fading parameters. An asymptotic error analysis is also performed in order to get further insights for the system performance. Finally, our analytical results are corroborated through Monte Carlo simulations. Although the derived expressions of the exact performance metrics include an infinite series, it is worth noting that they all converge rapidly and steadily after a few terms. In light of this, a truncation error analysis on one of the underlying performance metrics under investigation is provided and it is shown that the relative error is less or equal to 0.001% for as many as 10 terms.

In this paper, the error performance of binary and quaternary phase-shift keying signals with imperfect carrier phase recovery is investigated assuming a flat slow α-μ fading channel and the presence of thermal noise. The phase noise is considered to be Tikhonov and Gaussian distributed. More specifically, an analytical expression for the exact average bit error rate is obtained, as well as its corresponding asymptotic expression for large signal-to-noise ratio (SNR) values. The derived expressions are valid for arbitrary values of the fading parameters, namely α and μ, and they are validated through Monte Carlo simulations. Several important conclusions concerning the system performance as a function of the channel parameters, namely, non-linearity, clustering, and loop SNR, are drawn.

A thorough millimeter-wave measurement campaign is carried out in an indoor environment with an aim at characterizing the short-term fading channel behavior. The measurements are conducted in a variety of scenarios, with frequencies ranging from 55 GHz to 65 GHz, in lineof-sight and non-line-of-sight conditions, and combinations of horizontal and vertical polarizations at both transmitter and receiver. A number of fading models are tested, namely Rayleigh, Rice, Nakagami-m, α-μ, κ-μ, η-μ, and α-η-κ-μ. The statistics under analysis are those characterizing the fading amplitude and the frequency selectivity. In particular, the probability density and cumulative distribution functions for the former and level crossing rate per bandwidth unit for the latter are the respective first- and second-order statistics used. To this end, from the experimental data, the parameters of the models are estimated and the corresponding theoretical curves are plotted and compared with the empirical ones. Whereas the required theoretical formulations of the first-order statistics of these models are already well known, those of the second-order statistics as well as these fitting process in such a band shown here are unprecedented in the literature.

The aim of this paper is to propose a simple and efficient algorithm to generate white samples for the envelope of the α-η-κ-μ fading model. To this end, capitalizing on results available in the literature, the random number generator is implemented via the acceptance-rejection approach. The goodness-of-fit is objectively measured via the Kolmogorov-Smirnov test. We also address the performance of a digital communications system over the α-η-κ-μ fading channel in terms of the average bit error rate showing the impact of the fading parameters as an application for the random sequence generator. Theoretical and simulation results are contrasted, validating the accuracy of the generator.

Spectrum sensing using cooperative power spectral density cancellation (CPSC) over both fast and frequency-selective fading channels has not been investigated yet. We investigate the performance of the newly proposed CPSC method over this severely-impaired Rayleigh channel. We initially compare two approaches to calculate the decision variable in the CPSC algorithm. Then, it is assumed that the fading model can rapidly vary its characteristics over a single sensing interval. The channel model is then also made frequency-selective, by modeling additional resolvable paths. Performance is found by running simulations to find the Receiver Operating Characteristic (ROC) curves under these constraints. Finally, the theoretical achievable throughput of a secondary cognitive user network operating under this scenario is found as another performance criteria. The results reveal that the CPSC method is more sensible to the fast channels when compared to the selective ones. Moreover, it is shown that each scenario has one optimal sensing time, which yields the highest throughput for the secondary network.

In this paper, we study the impact of eigenvalue-based spectrum sensing techniques in the achievable throughput of the secondary cognitive radio network. For this analysis, the channel between the primary and secondary users is modeled as a complex Nakagami-m distribution, in order to investigate the effects of not only the channel envelope, but also the novel non-uniform phase distribution in the detection process. Simulated scenarios are run using the energy detection, the maximum eigenvalue detection, the maximum-minimum eigenvalue detection and the generalized likelihood ratio test. The achievable network throughput is then found as a function of the number of samples, where the channel envelope and phase parameters are arbitrarily varied to investigate their effects on the spectrum sensing and, consequently, on the secondary network throughput itself.

In this Paper, an estimator of the α-κ-μ distribution, based on the maximum likelihood (ML) estimation method, is proposed and verified. The technique is applied in samples generated by the Smith spectrum method, which is also a contribution in this paper. This distribution has been recently proposed to model the received field strength in a nonlinear environment with line-of-sight condition. Hence, efficient estimators are required to infer the parameters of the received signal amplitude distribution in propagation mediums modeled by it. The ML estimator for the α-κ-μ distribution is derived and verified against chosen true values of the parameters by means of a simulation. We verify that, for a large sample size, the estimators are unbiased, from a practical point of view, for the useful range of α, κ and μ. The results conclude that the estimator variations about its sample mean are smaller for the α parameter.

In this paper, a new estimator of the η-μ distribution is derived based on the Maximum Likelihood (ML) Estimation method. This distribution has been recently proposed to model the received field strength in a non-line-of-sight scenario. Hence, efficient estimators are demanded to infer the parameters of the received signal amplitude distribution in wireless communication propagation mediums. The ML parameter estimation is shown, as well as its characteristics and some other estimation measurements in order to evaluate the method. Then, the η-μ distribution ML estimator is proposed, analyzed and verified against the true values of the parameters chosen by means of a simulation employing a well-established random sequence generator. The ML method is also verified against another well-known estimation method, the Moments Based Estimator.

In this paper, a new correlated shadowed channel model based on grid points is proposed. It is presented a three-dimensional model capitalizing on the two-dimensional model available in the literature. Additionally, a closed-formula to obtain the theoretical correlation between any two points is derived. In order to show the usefully of the proposed model and as an application example, we provide an important performance analysis related to spectrum sensing for cognitive radio. The effect of correlation in the various aspects of system performance is then investigated. The validity of the analytical results is supported by means of Monte Carlo simulation. The results show that the system performance analysis is more accurate when the proposed model is considered, this conclusion is discussed throughout the paper.

In this paper, we propose a simple and highly efficient random sequence generator for α-κ-μ and α-η-μ variates. The proposed algorithm may yield an efficiency of almost 100% for α-κ-μ and α-η-μ. This high efficiency can be reached for all special cases such as α-μ, κ-μ, η-μ, Nakagami-m, Nakagami-q, Weibull, Hoyt, Rayleigh, Rice, Exponential, and the One-Sided Gaussian. These generators are implemented via the rejection technique and allow for arbitrary fading parameters. The goodness-of-fit is objectively measured using the the p-value of the Kolmogorov-Smirnov test.

New, exact expressions for the probability density and distribution functions of a bivariate Hoyt (Nakagami-q) process with arbitrary correlation pattern in a nonstationary environment are derived, a solution to a longstanding unsolved problem. More specifically, the following are obtained: joint probability density function, joint cumulative distribution function, power and envelope correlation coefficients, and some statistics related to the signal-to-noise ratio at the output of the selection combiner, namely, outage probability and probability density function. The exact expressions are given in infinite series form, but are mathematically tractable, easy to evaluate, and flexible enough to accommodate a myriad of correlation scenarios, useful in the analysis of a more general fading environment. The power correlation coefficient is found in an exact, simple, closed-form formula, whereas the envelope one, also exact, is more mathematically elaborate. Approximate, simple closed-form expressions for the joint distribution are also obtained that yield (i) excellent results for small to medium correlation coefficients and (ii) reasonable ones for high correlation coefficient.

This paper derives an exact, easily computable series-type formulation for the multivariate α-µ joint cumulative distribution function. Then, based on useful and practical assumptions, alternative, new, simpler formulations for the α-µ multivariate joint density function and its corresponding distribution function are provided. The formulations are still general and yet very simple, assuming arbitrary parameters and arbitrary correlation among the marginal α-µ variates. As an application example, the outage probability of the selection combining scheme is obtained, again in an exact manner. The results show that only a few number of terms of the series is sufficient to provide an excellent agreement between approximate and exact results.

An infinite series formulation for the multivariate α-μ joint probability density function with arbitrary correlation matrix and non-identically distributed variates is derived. The expression is exact and general and includes all of the results previously published in the literature concerning the distributions comprised by the α-μ distribution. The general expression is then particularized to an indeed very simple, approximate closed-form solution. In addition, a multivariate joint cumulative distribution function is obtained, again in simple, closed-form manner. As an application example, the exact and approximate performances of the selection combining scheme given in terms of the outage probability is shown. Approximate and exact results are very close to each other for small as well as medium values of correlation.

New, exact expressions for the bivariate Nakagami-q (Hoyt) processes with arbitrary correlation in a nonstationary environment are derived. More specifically, the following are obtained: joint probability density function, joint cumulative distribution function, power correlation coefficient, and some statistics related to the signal-to-noise ratio at the output of the selection combiner, namely, outage probability and probability density function. The expressions are mathematically tractable and flexible enough to accommodate a myriad of correlation scenarios, useful in the analysis of a more general fading environment.

New, exact expressions for the bivariate Hoyt (Nakagami-q) processes in a nonstationary environment are derived. More specifically, the following are obtained: joint probability density function and joint cumulative distribution function. In addition the outage probability for the Maximal- Ratio and Equal-Gain Combining techniques in a correlated Hoyt channel are obtained. The expressions are mathematically tractable and flexible enough to accommodate a myriad of correlation scenarios, useful in the analysis of a more general fading environment.

In this paper, a new infinite series representation for the multivariate alpha-mu joint probability density function is derived allowing for an arbitrary correlation matrix and non-identically distributed variates. The formulation is general and exact and comprises all of the other joint densities that arise from alpha-mu distribution published in the literature.

In this paper, a new infinite series representation for the multivariate alpha-mu joint probability density function is derived allowing for an arbitrary correlation matrix and non-identically distributed variates. The formulation is general and exact and comprises all of the other joint densities that arise from alpha-mu distribution published in the literature.

New, exact expressions concerning the bivariate Nakagami-m processes with arbitrary correlation and fading parameters are derived. More specifically, the following are obtained in the present work: joint moment generating function; joint probability density function; joint cumulative distribution function; power correlation coefficient; and several statistics related to the signal-to-noise ratio at the output of the selection combiner, namely, outage probability, probability density function, and mean SNR. The expressions make use of the well known generalized Laguerre polynomials. They are mathematically tractable and flexible enough to accommodate a myriad of correlation scenarios, useful in the analysis of a more general fading environment. We maintain, however, that a relation among the correlation coefficients of the corresponding Gaussian components must be kept so that convergence is attained.