Covert communication has sparked widespread attention since it seeks to secure potentially undetected transmissions by employing a variety of confusion sources. Despite the highly directed nature of Terahertz (THz) beams, receiver vibration causes beam direction misalignment, lowering communication speeds, and reducing communication covertness. In this research, we present and analyze a resilient reconfigurable intelligent surface (RIS)-aided covert transmission scheme for THz transmissions, in which a multi-antenna node transmits to both a covert user and a public user in the presence of an unfriendly warden, under both perfect and imperfect channel state information assumptions. The proposed approach is based on superimposing the two legitimate users’ signals, confusing the warden node’s detection. Closed-form expressions for the optimal detection threshold at the warden, detection error probability (DEP), and minimum DEP (MDEP) are provided. Simulation findings corroborate the analytical results, and insightful discussions on the monotonicity of the main metrics and their impact on the system’s covertness are included. Then, using an alternating optimization (AO)-based framework, an optimal design of the power allocated to each user’s signal, their respective beamforming vectors, and the RIS phase shifts is carried out to maximize the system’s achievable communication rate subject to power constraints and an MDEP threshold at the warden. The convexity of all intermediate subproblems has been thoroughly examined, and the covertness advantage of the proposed approach has been proven.

This paper investigates covert communication in reconfigurable intelligent surface (RIS)-assisted wireless systems when an adversarial detector (Willie) patrols the environment along a circular trajectory. Unlike prior works assuming stationary eavesdroppers, we explicitly model adversary mobility and derive analytical feasibility conditions for covert transmission under average leakage constraints. Leveraging Rayleigh quotient optimization with unimodular constraints, we obtain a closed-form RIS phase design maximizing legitimate received power while satisfying covert constraints. Extensive Monte Carlo simulations validate the theoretical predictions, revealing a fundamental trade-off between Willie’s mobility radius and achievable performance at Bob. Sensitivity analyses demonstrate that RIS size and covert constraint tightness critically impact system behavior, confirming the importance of modeling adversary mobility in practical RIS-assisted covert communication systems.

Federated Learning in Internet of Things (IoT) networks involves a critical trade-off between resource efficiency and global model performance. We propose a lightweight optimal stopping theory-assisted node selection strategy, which unifies local model accuracy (LMA) and received signal strength (RSS) into a single scoring metric governed by a tunable parameter α. The optimized value α maximizing the probability of picking the best elements following our strategy has been obtained through a probabilistic framework. The analytical results have been confirmed through simulation. Experiments have been conducted employing the MNIST dataset and show consistent improvements in global model accuracy compared to four baseline strategies, while ensuring a suitable complexity for real-time IoT deployment. Moreover, unlike clustering-based or secretary problem-inspired approaches, our method evaluates all nodes globally and performs reliably even with limited candidate availability.

An in-depth analysis and extension of a hybrid approach, combining Local Particle Swarm Optimization (LoPSO) and an optimal stopping strategy inspired by the Secretary Problem, is presented for the search and validation of mobile targets by Unmanned Aerial Vehicles (UAVs). The mathematical foundations of the system model, communication primitives, the LoPSO optimization algorithm, and robust sequential decision strategies are detailed. Furthermore, recent advances in intelligent UAVs, specific LoPSO topologies, and challenges related to swarm coordination and decision-making in constrained environments are incorporated. Key additions include a detailed energy model, a formal communication model, pseudocode for all algorithms, complexity analysis, comparative tables of hardware platforms, and a plan for future experimentation. Finally, ethical and regulatory considerations are discussed, and directions for real-world implementation.

Implementing Federated Learning (FL) in IoT environments presents notable challenges, primarily due to variations in both clients' local model performance and the reliability of their communication links, often indicated by received signal strength (RSS). In this study, we introduce a deterministic and lightweight selection mechanism that filters participants based on predefined thresholds for local model accuracy (LMA) and RSS. The thresholds are analytically optimized to maximize the probability of selecting exactly$m$suitable clients. Theoretical analysis is complemented by parameter-specific insights to better illustrate the system's behavior. Comprehensive experiments using the MNIST dataset demonstrate that our approach consistently surpasses strategies that rely solely on LMA or RSS, validating its effectiveness for FL in resource-limited IoT contexts.

This paper addresses the challenges faced by air-ground vehicle networks (AGVN), with a focus on the rapid mobility of both unmanned ground vehicle (UGV) and unmanned aerial vehicles (UAVs) clusters. UAVs employ the non-orthogonal multiple access (NOMA) transmission technique to efficiently transfer data to UGV, aiming to enhance network performance by increasing spectral efficiency and supporting simultaneous transmissions. The system integrates UAV into the network perception environment, enabling cognitive activities and convenient transmission for UGV beyond base station coverage. We provide closed-form outage probability (OP) expressions for UAVs and UGV in two assist modes, i.e., amplify-and-forward UAV (A-UAV) and decode-and-forward UAV (D-UAV), using real-world situations with double Rayleigh fading (DRF) and outdated and imperfect channel state information (ipCSI). Additionally, in both relaying modes, the asymptotic expression for the OP is provided in the high SNR regime for UAV and UGV links. In addition, the system’s diversity order is derived, and the asymptotic analysis shows an intricate interplay of relative speed, channel outdatedness, and OP. Our research results show that when the relative speed decreases or the power increases, the OP decreases. Under the same conditions, the performance of A-UAV is more stable than that of D-UAV. Additionally, the study underscores the importance of assist mode and the number of UAVs in achieving optimal outage performance. Simulation results confirm our analytical results, emphasizing the trade-offs and performance indicators for AGVN system design. The study demonstrates potential avenues for maximizing system performance even within stringent constraints through strategic parameter adjustments.

Among the most employed tools to improve the performance of any wireless communication system are the multiple-input and multiple-output antennas. To achieve that task, they overcome the fading phenomenon which can affect any signal during its propagation to make it very strong for reliable communication. The signal-to-noise ratio represents the cornerstone without it, no performance criterion can be evaluated. Taking into account the complexity of evaluating a matrix of SNR by the use of a MIMO system, we have opted for a MIMO system with space-time block coding which gives an output SNR in the form of a scalar instead of a matrix product. In this regard, we have derived the effective capacity expressions for the Weibull fading channel by employing an original and tight approximate probability density function, which has been already derived in our previous works, of the output SNR of a MIMO system with space-time block coding. The channels are assumed independent and not necessarily identically distributed. Additionally, we have proposed a high-SNR approximation that is streamlined and makes it possible to analyze the system's performance in terms of its fading characteristics simply. In the end, we have checked the accuracy of our proposed expressions via Monte Carlo simulation methods.

The Industrial Internet of Things (IIoT) has been recognized as having the potential to offer substantial benefit to a wide range of industrial sectors. However, the widespread development and deployment of IIoT pose a set of challenges, including the shortage of spectrum resources and network security. Given the heterogeneity of IIoT devices, conventional cryptographic security techniques are not sufficient, since they suffer from challenges, including computation, storages, latency, and interoperability. In this article, we present a physical layer security analysis for cognitive IIoT networks. In our system, IIoT devices opportunistically utilize the primary spectrum; thereby improving spectrum efficiency and allowing access to a large number of devices. Specifically, the considered network uses space-time block coding (STBC) in conjunction with nonorthogonal multiple access (NOMA) in underlay cognitive mode to realize data transmission for IIoT devices with high-spectrum efficiency. The secure STBC-NOMA transmission model is established by taking into account the interference from a primary user. New approximate and asymptotic expressions of secrecy outage probability (SOP) for the secondary users (SUs) are derived to characterize the system’s secrecy performance. In addition, the SU’s closed-form secrecy ergodic rate (ER) is provided. The proposed STBC-NOMA approach, when compared to the classic single antenna (SA)-based NOMA, achieves better SOP performance for all SUs, as confirmed by both analytical and simulation findings. Furthermore, we show that the proposed STBC-NOMA framework improves the SOP performance of weaker users. Additionally, the STBC-NOMA framework outperforms the SA-NOMA framework in terms of secrecy ER performance for all SUs.

In this paper, the secrecy of a dual-hop unmanned aerial vehicle-based wireless communication system, in the presence of mobility and imperfect channel state information (CSI), is investigated. The system consists of a decode-and-forward relay connecting a source and destination node. The transmission is performed under the presence of two eavesdroppers aiming to intercept independently the source-relay and source-destination communication channels. It is assumed that the transmitters are equipped with one transmit antenna, while the receivers have multiple receive antennas. Based on the statistical properties of the per-hop signal-to-noise ratio (SNR), a closed-form formula for the network’s secrecy intercept probability (IP) is derived, in terms of the main system and channel parameters. The results correlate the impact of such parameters on the secrecy level of the system, where the latter can be enhanced by increasing the number of antennas at the legitimate receivers and the average SNRs of the legitimate links. Furthermore, it is shown that as the CSI imperfection level, nodes’ speed, delay, and carrier frequency increase, the system’s secrecy degrades. All the derived results are verified through Monte Carlo simulations.

In this paper, we investigate the average bit error probability (ABEP) performance of a relaying radio- frequency/free-space optic (RF/FSO) coded and uncoded communication system employing an energy harvesting-based fixed gain amplify-and-forward (AF) relay. Along with maximal-ratio combining (MRC) at both nodes, the convolutional self orthogonal codes and majority logic decoding method (MLGD) are explicitly examined at the source and destination, respectively. In addition, the source-relay channel experiences i.n.i.d. Nakagami-m fading, while the relay-destination link is modeled by Málaga-$\mathcal{M}$ fading with the effect of pointing errors taking into account many apertures at the destination. Due to the significant coding benefits that these codes offer, the results of the Monte Carlo simulation proved that the proposed decoding technique is adequate for such communication.

Guaranteeing decent secrecy levels in future wireless network generations has gained crucial importance due to the unprecedented increase in wireless connectivity worldwide. In this paper, an enhanced hybrid channel/device-based physical layer authentication (PLA) scheme is proposed. The channel state information (CSI) and device-dependent carrier frequency offset (CFO) are employed to discriminate various malign and benign devices in a wireless network. In particular, CSI-based hypothesis testing (HT) is applied initially on the received authentication requests of several unknown transmitters, relying on legitimate CSI values from previous transmissions to classify such nodes. Then, a second stage takes place using the CFO HT on either the interval containing misclassified legitimate or illegitimate nodes to improve the initial stage’s classification results. Approximate expressions for the authentication and detection probabilities are retrieved, whose tightness is endorsed by extensive Monte Carlo simulations. Results show that the proposed scheme enhances the single-attribute PLA performance (CSI-and CFO-based). Also, the authentication probability improves by the increase in the average legitimate signal-to-noise ratio (SNR) and lower nodes’ speed. In addition, the detection probability rises at high legitimate and intruder links SNRs, while it degrades for low SNR (i.e., noisy attributes observations). Lastly, the more significant the difference between the actual malign and benign nodes’ CFOs, the better the detection probability.

In this article, the physical-layer security of a dual-hop unmanned aerial vehicle-based wireless network, subject to imperfect channel state information (CSI) and mobility effects, is analyzed. Specifically, a source node$(S)$communicates with a destination node$(D)$through a decode-and-forward relay$(R)$, in the presence of two wiretappers$(E_{1}$and$E_{2}$) independently trying to compromise the two hops. Furthermore, the transmit nodes$(S,R) $have a single transmit antenna, while the receivers$(R,D,E_{1},E_{2}) $are equipped with multiple receive antennas. Based on the per-hop signal-to-noise ratios (SNRs) and correlated secrecy capacities’ statistics, a closed-form expression for the secrecy intercept probability (IP) metric is derived, in terms of key system parameters. Additionally, asymptotic expressions are revealed for two scenarios, namely: 1) mobile nodes with imperfect CSI and 2) static nodes with perfect CSI. The results show that a zero secrecy diversity order is manifested for the first scenario, due to the presence of a ceiling value of the average SNR, while the IP drops linearly at high average SNR in the second one, where the achievable diversity order depends on the fading parameters and number of antennas of the legitimate links/nodes. Furthermore, for static nodes, the system can be castigated by a 15-dB secrecy loss at IP$=3\times 10^{-3}$, when the CSI imperfection power raises from 0 to 10−3. Finally, the higher the legitimate nodes’ speed, carrier frequency, delay, and/or relay’s decoding threshold SNR, the worse is the system’s secrecy. Monte Carlo simulations endorse the derived analytical results.

Deep neural networks have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, they offer partial but not full robustness. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This article proposes a novel online selection and relabeling algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers to maximize the machine learning (ML) system's robustness. The OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs and moving them to the crowdsourced workers to be validated and corrected. As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system's reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-of-the-art black box defense technique, resulting in a more robust system. Simulation results show that the OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA's performance has a positive correlation with system robustness.

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Impairments baseband model in-phase and quadrature-phase Imbalance (IQI) and Residual hardware impairments (RHI) are two key factors degrading the performance of wireless communication systems (WCSs), particularly when high-frequency bands are employed, as in 5G systems and beyond. The impact of either IQI or RHI on the performance of various WCSs has been investigated exclusively in a separate way. To fill this gap, in this paper, the joint effect of both IQI and RHI on the performance of a WCS subject to Hyper Fox’s H-fading (HFHF) channel, is investigated. Such a fading model generalizes most, if not all, of well-known fading and turbulence models. To this end, closed-form expressions for the outage probability (OP), Average channel capacity (ACC) under constant power with optimum rate adaptation (ORA) policy, and average symbol error probability (ASEP) for both coherent and non-coherent modulation schemes are derived. Specifically, all the analytical expressions are derived for three different scenarios: (i) ideal Tx and Rx impaired, (ii) Tx impaired and ideal Rx, and (iii) both Tx and Rx are impaired. Further, asymptotic expressions for OP, ACC under ORA policy, and ASEP are obtained, based on which, insightful discussions on the IQI and RHI impacts are made.$\alpha -\mu $and Málaga$\mathcal {M}$turbulence with pointing error distribution models have been considered as particular cases of the HFHF distribution. The analytical derivations, endorsed by simulation results, demonstrate that the RF impairments’ effects should be seriously taken into account in the design of next-generation wireless technologies.

Large intelligent surface (LIS) has recently emerged as a potential enabling technology for 6G networks, offering extended coverage and enhanced energy and spectral efficiency. In this work, motivated by its promising potentials, we investigate the error rate performance of LIS-assisted nonorthogonal multiple access (NOMA) networks. Specifically, we consider a downlink NOMA system, in which data transmission between a base station (BS) and L NOMA users is assisted by an LIS comprising M reflective elements (REs). First, we derive the probability density function (PDF) of the end-to-end wireless fading channels between the BS and NOMA users. Then, by leveraging the obtained results, we derive an approximate expression for the pairwise error probability (PEP) of NOMA users under the assumption of imperfect successive interference cancellation. Furthermore, accurate expressions for the PEP for M = 1 and large M values ( M > 10) are presented in closed-form. To gain further insights into the system performance, an asymptotic expression for PEP in high signal-to-noise ratio regime, asymptotic diversity order, and tight union bound on the bit error rate are provided. Finally, numerical and simulation results are presented to validate the derived mathematical results.

Machine learning (ML), and deep learning (DL) in particular, play a vital role in providing smart services to the industry. These techniques, however, suffer from privacy and security concerns since data are collected from clients and then stored and processed at a central location. Federated learning (FL), an architecture in which model parameters are exchanged instead of client data, has been proposed as a solution to these concerns. Nevertheless, FL trains a global model by communicating with clients over communication rounds, which introduces more traffic on the network and increases the convergence time to the target accuracy. In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process. Next, we propose an online stateful FL heuristic to find the best candidate clients. Additionally, we propose an IoT client alarm application that utilizes the proposed heuristic in training a stateful FL global model based on IoT device-type classification to alert clients about unauthorized IoT devices in their environment. To test the efficiency of the proposed online heuristic, we conduct several experiments using a real data set and compare the results against state-of-the-art algorithms. Our results indicate that the proposed heuristic outperforms the online random algorithm with up to 27% gain in accuracy. Additionally, the performance of the proposed online heuristic is comparable to the performance of the best offline algorithm.

Ensuring physical layer security is a crucial task in conventional and emerging communication systems, which are typically characterized by stringent quality of service and security requirements. This also accounts for wireless technologies in the context of the Internet of Things paradigm, which are expected to exhibit considerably increased computational complexity. Based on this, the present contribution investigates the secrecy outage performance of a dual-hop decode-and-forward (DF) mixed radio-frequency/ underwater optical wireless communication (RF/UOWC) system. Such wireless network configurations are particularly useful in efficient and demanding scenarios, such as military communications. Therefore, our analysis considers one single-antenna source node (S) communicating with one legitimate destination node (D) via a DF relay node (R) equipped with multiple antennas for reception. Particularly, the relay receives the incoming signal from S via an RF link, applies selection-combining (SC) technique, fully decodes it, re-encodes it, and then forwards it to the destination via a UOWC link. The communication is performed under the eavesdropper's attempt to intercept the S - R hop (RF side). In this context, a closed-form expression for the secrecy outage probability is derived along with a thorough asymptotic analysis in the high SNR regime, based on which the achievable diversity order is provided. The offered results provide useful insights on the impact of some key system and channel parameters on the secrecy outage performance, such as the number of eavesdroppers, the number of relay antennas, fading severity parameters of RF links, and water turbulence severity of the UOWC link. The conducted analysis shows that the secrecy outage probability is dominated only by the R-D link in the high SNR regime, regardless of the S-R parameters, such as the number of relay antennas and the average SNR at the relay branches. The offered analytic results are corroborated with respective results from computer simulations. Since these parameters are closely related with the computational complexity at the involved terminals, the offered insights are useful for the design and computationally sustainable operation of such systems.

The present contribution investigates the physical layer security in a cognitive radio network (CRN). To this end, we consider an underlay uplink CRN consisting of multiple secondary sources, a single-antenna secondary base station, and multiple eavesdroppers. In addition, we assume that the secondary sources transmit their data sequentially and that a jammer is randomly chosen from the remaining source nodes to send a jamming signal to the eavesdroppers. However, in an uplink underlay CRN, a friendly jammer is not always allowed to use its maximal transmit power as the secondary users are required to continuously adapt their power in order to avoid causing interference to the primary users. As a consequence, enhancing the system security using a jammer with low transmit power in the presence of numerous eavesdroppers turns out to be questionable. In this regard, we derive novel analytic expressions that assist in quantifying the achievable security levels and the corresponding limitations. This leads to the development of useful insights on the impact of network parameters on the performance of the system's security. The offered analytic results are corroborated through Monte Carlo simulation. It is shown, that for a low transmit power of the friendly jammer, the system's security can only be enhanced for a small number of eavesdroppers.

In this paper, the upper bound and asymptotical approximation for the bit error rate (BER) as well as for the frame error rate of linear block codes under maximum-likelihood decoding operating over exponentially correlated (EC) generalized-fading channels are proposed. Particularly, our analysis takes the convolutional codes as an example and three different fading scenarios are considered, namely Rice, Nakagami-m, and Weibull fading. Such scenarios have a common property that is spherically invariant random process (SIRP). Our analysis relies on the derivation of new lower bounds per each eigenvalue since the BER as well as the FER expressions are written in terms of the pairwise error probability, which by its turn depends on the eigenvalues of the channel covariance matrix. Based on that, the upper bounds and asymptotic expressions of the considered metrics subject to the three above fading models are derived, which show to be tighter than the previous results published elsewhere in the literature, especially for the high signal-to-noise ratio regions.

In this paper, a tighter upper bound for the bit error rate (BER) of convolutional codes operating under exponentially correlated Nakagami- m fading is proposed. Our analysis relies on the derivation of new lower bounds per each eigenvalue since the BER expression is written in terms of the pairwise error probability, which by its turn depends on the eigenvalues of the channel covariance matrix. In comparison to previous results published elsewhere in the literature, the proposed bounds are shown to be tighter, specially for high signal-to-noise ratio regions, being therefore of great importance and usefulness for the forthcoming researches in the field. Moreover, this result can be generalized to all linear block codes as well as other fading models as the proposed eigenvalues' upperbounds are the main key of this work.

In this paper, a unified performance analysis of underwater wireless communication system is presented. The radio link between the source (S) and the destination (D) is subject to κ-μ shadowed fading channel. We present an analytical closed-form expression for the cumulative distribution function (CDF) and the probability density function (PDF) of the total end-to-end SNR in terms of the Kummer's Hypergeometric function. Based on these results, we present exact closed-form expressions of communication system performance criteria, such as outage probability (OP) and average bit/symbol error rate (ABER/ASER) with their respective asymptotic expansion, at both low and high SNR regimes, expressed in terms of basic elementary functions. All the derived analytical expressions are validated through computer-based simulation.

In this paper, we study the performance of a dualhop amplify-and-forward (AF) communication system over asymmetric links composed of Radio-Frequency (RF) and Free-Space-Optical (FSO) links in the presence of atmospheric turbulence as well as pointing error impairments under both heterodyne detection and intensity modulation with direct detection (IM/DD) techniques. The RF link is assumed to operate under Weibull fading, while the FSO channel undergoes Gamma-Gamma (GG) fading. Using a Channel-State-Information (CSI)-assisted relaying, we derive closed-form expressions for the cumulative distribution function (CDF) and the probability density function (PDF) of the end-to-end signal-to-noise ratio (SNR) for the considered system in terms of the Meijer G-function. Based on these results, a novel closed-form expression of the average symbol error rate (ASER) in terms of the bivariate Fox H-function is derived. Finally, numerical results are presented to validate the analytical expressions.

This work presents a study on the performance of a hybrid 5G RF/FSO communication system. Both transmitter and receiver communicate through two paths: the first link operates under Radio-Frequency (RF) technology, assumed to undergo a Weibull fading channel, while the second link operates under Free Space Optical (FSO) technology, subject to Málaga-M turbulence model with the presence of pointing error impairment. Both incoming signals are combined at the receiver side using Maximal Ratio Combining (MRC) technique. We retrieve, in particular, the Moment Generating Function (MGF) of the total Signal-toNoise Ratio (SNR). Based on that, we present the exact closedform expression of the Average Symbol Error Rate (ASER) of the considered RF/FSO system for different modulations schemes in terms of the bivariate Fox's H-Function.

This paper presents tighter bounds on the pairwise error-event probability and therefore very tighter upper bounds of bit error rate(BER) for communication systems employing convolutional code over Rayleigh fading channel with exponential correlation coefficient. To obtain our bound, we start by proposing a closed form upper bound for Gaussian Q-function and as well as deriving a new lower bound of each channel covariance matrix eigenvalue. The simulation results show that our proposed bound is tighter than those previously developed, especially when the covariance parameter q increases. A gain of over 6.5dB was achieved for q = 0.95 at BER=10-5from the better previous bound, whereas for the lower values of q we reach the simulated curve.

Ascertaining the importance of the Maximal Ratio Combining (MRC) receiver and the suitability of the Weibull model to describe mobile fading channels, we study the performance of this combiner over correlated Weibull Fading Channels (WFC) with arbitrary parameters. We adapt the approximate closed-form expressions of many performance criteria of L-branch MRC derived in [1] to the case of correlated channels. Additionally, to reduce the computing complexity of performance criteria, the probability density function (PDF) and the moment generating function (MGF) of the output SNR are written otherwise in terms of simple polynomial and Laguerre generalized function. All the results are evaluated and illustrated by using Mathematica Software, verified and validated by monte-carlo simulation and comparison with previous results [2].

Vehicular ad-hoc networks (VANETs) are a promising communication technology. they offers many applications, which will improve traffic management and safety. Nevertheless, those applications have stringent security requirements, as they affect road traffic safety. Security requirement like authentication, privacy and Integrity are crucial to VANETs, as they avoid attacks against vehicle-to-vehicle and vehicle-to-roadside communication. In this paper, we investigate the authentication and privacy issues in VANETs. We explore the Attribute Based Signature (ABS) primitive and its variants. We then select among existing ABS literature, an efficient scheme (the best known) that achieve both traceability and user-privacy (anonymity). Finally, we propose a protocol for VANETs that uses traceable ABS in general context of multi-hop routing.

In this paper, we derive a novel probability density function (PDF)-based expression for various performance criteria with L-branch maximal-ratio combining over independent and not necessarily identical Weibull fading channels. Approximating the output SNR by a simple H-distribution, useful closed formulas for cumulative distribution function, moment generating function, outage probability, amount of fading, average capacity, and average symbol error rate for some M-ary modulation schemes are derived in terms of Meijer G-function. All results are illustrated and validated by computed simulations using Mathematica software.