Generation Wireless Communication Networks Research Articles

Design and Development of Dual Band Millimeter Wave Substrate Integrated Waveguide Antenna Array

Abstract New communication paradigms have emerged to make better use of the available wireless spectrum due to its scarcity. Millimeter wave high-frequency spectrum could offer a viable solution to the problem of spectrum scarcity. Millimeter wave devices and antennas are becoming increasingly popular and are used in a wide variety of applications and planned Fifth Generation (5G) wireless communication networks. In this work, we develop a Substrate Integrated Waveguide (SIW) based antenna array and millimeter-wave feeding network with the aim of achieving optimal performance. A microstrip array antenna is developed for use at millimeter wave frequencies of 28 GHz and 38 GHz. Next, an SIW array antenna will be created. For high-frequency uses, SIW technology excels due to its low loss, easy integration and high quality factor. The two unequal longitudinal slots in a slotted SIW antenna cause the structure to resonate at 28 GHz and 38 GHz. The SIW structure is fabricated by making two parallel rows of metallic vias, carefully determined through sizes to ensure minimal internal losses. A microstrip line that transitions into a SIW feeds into the proposed layout. In this paper, the authors investigate the design and construction of an integrated waveguide antenna array for use at dual millimeter-wave frequencies.

Optical to terahertz frequency conversion using near zero-index nonlinear hybrid waveguides

Terahertz communications has emerged as an excellent candidate for the next generation of wireless communication networks. Despite of significant advances, one still looks for a high-efficiency terahertz source using nonlinear optics, such as optical refraction and difference-frequency generation. Concurrently, recent years have witnessed significant progress in metamaterials-assisted nonlinear optical processes. In this study, we propose a theoretical approach using near zero-index nonlinear hybrid waveguides to realize a high generation efficiency of terahertz waves via on-chip difference-frequency generation. Remarkably, thanks to the simultaneous realization of near zero-index both at terahertz and telecom frequencies, such a nonlinear hybrid waveguide can overcome two major bottlenecks in the conversion from optical wave to terahertz waves: phase mismatch and weak nonlinear optical-terahertz waves interactions, resulting in a high conversion as 2.2 × 10−3W−1 just within the propagation length of several terahertz wavelengths. Our results demonstrate the feasibility of an on-chip terahertz source, offering unique properties such as high integration and ultra-low power consumption for terahertz communication systems.

Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control

Due to the extensive implementation of the fifth generation wireless communication networks (5 G), numerous base stations are being strategically deployed in densely inhabited areas to address the escalating need for network traffic. Within wireless cellular networks, over 80% of energy consumption is primarily concentrated on the side of the base station. In comparison to 4 G base stations, the 5 G base stations exhibit notable enhancements in terms of bandwidth, peak rate, and transmission power. Nonetheless, they suffer from limited signal coverage and extensive resource consumption. To address this challenge effectively, the present study introduces a Mobile Perception Dual-Level (MPDL) base station sleep control strategy based on cellular traffic forecasting. The strategy aims to satisfy the demands for reduced power consumption and enhanced service quality by monitoring both the current network traffic of the base station and the user's mobility, consequently placing the base station into two different sleep states. Specifically, we first designed an inverted Transformer (iTransformer) model to accurately forecast cellular traffic. The model is capable of effectively capturing both the spatial and temporal attributes of cellular traffic. It also takes into account the impact of geographical features, such as Points of Interest (POIs) and the quantity of Base Stations (BSs), on the forecasting outcomes. Consequently, it attains precise predictions of the cellular traffic. Based on the forecasting outcomes of cellular traffic, this study presents a mobility perception dual-level sleep strategy. This strategy proposal differentiates sleep into deep sleep and light sleep, while also taking into account the mobility attributes of users. It incorporates considerations for Quality of Service (QoS) and energy consumption. We assess the performance and application impacts of cellular traffic forecasting and base station sleep strategy in real-world datasets. Several experiments and validations have confirmed that the MPDL, as proposed in this paper, effectively decreases power consumption by a range of 22% to 41% when compared to baselines.

Enabling intrinsic intelligence, ubiquitous learning and blockchain empowerment for trust and reliability in 6G network evolution

This study investigates the 6th Generation (6G) wireless communication network as a vital component of Ubiquitous Learning, which makes it possible to build trustworthy relationships and accurate intelligence in a variety of contexts. The important significance of blockchain technology is recognized, and its potential to improve the performance of the 6G network is investigated. Blockchain technology powers Ubiquitous Learning as a decentralized tool. But in Ubiquitous Learning contexts, problems arise when varied communication settings, information asymmetry, and resource limits confront wireless-connected devices functioning as blockchain consensus nodes. To solve these difficulties, which are critical for Ubiquitous Learning, a blockchain sharding and incentive system is presented. The purpose of this technique is to improve the learning process in the 6G network by cultivating reliable intelligence. A sharding architecture that integrates a contract theory-based incentive mechanism and a realistic Byzantine fault tolerance mechanism, particularly tailored for Ubiquitous Learning settings, is described. This model analyses the useful Byzantine consensus mechanism and block broadcast characteristics in wireless networks. This study finds the best contract combination for both completely symmetric and asymmetric information scenarios, optimizing block rewards for customers that seek blockchain services.

Energy aware routing with optimal deep learning based anomaly detection in 6G-IoT networks

The sixth generation (6G) wireless communication networks are being planned for the transformation of client services and applications through the Internet of Things (IoT) technology that is headed towards a completely intelligent and autonomous system. The IoT-6G network offers ubiquitous applications on the basis of wireless communication technologies. However, various problems need to be critically addressed in order to realize this technological application. Primarily, the devices encounter energy constraints that prevent it from smart functioning due to limited battery power; so the connectivity issue arises resulting in the failure of the connections, once the device’s energy gets exhausted. Besides, security also remains a challenging issue which can be solved with the help of Machine Learning (ML) and Deep Learning (DL)-based anomaly detection techniques. In this background, the current study presents a Metaheuristic Energy-Aware Routing with Optimal DL-based Anomaly Detection Technique (MER-ODLADT) for 6G-IoT networks. The presented MER-ODLADT technique encompasses two major processes namely, routing and anomaly detection. For the routing process, the proposed MER-ODLADT technique applies the Marine Predator’s Optimization (MPO) algorithm with a fitness function involving multiple input parameters. On the other hand, the MER-ODLADT technique employs the Eco-Geography Optimization with Deep Belief Network (EGODBN) model for anomaly detection and classification. The performance of the proposed MER-ODLADT technique was validated using a benchmark dataset and the results were examined under distinct measures. The experimental outcomes demonstrate the enhanced performance of the proposed MER-ODLADT technique over other existing approaches with a maximum sensitivity of 98.34 %, specificity of 97.91 %, accuracy of 99.43 %, precision of 99.58 % and an F1-score of 98.41 %.

Intelligent Trust Ranking Security Preserving Model for B5G/6G

Trust assessment is a crucial parameter in the next generation WCN (Wireless Communication Network) for the secure collaboration of nodes with each other and the detection of the possible security menaces in the network. The trust ranking of nodes especially in the platforms of defense networks and healthcare networks is of vital importance to the security threats inside the network. In this paper, we have suggested an RM dataset based on the current possible attacks along with advanced authentication attributes for the next generation WCN (B5G/6G). Further, we proposed a trust-ranking model based on the SVM machine learning technique to define the trust ranks of the users present in the network. The model consists of five trust rankings. The fifth trust rank is defined as the highest level of trust and the first trust rank designates the lowest level of trust. The attained rank of trust determines the specific services offered to the corresponding node. The proposed prediction model based on the SVM (Support Vector Machine) algorithm maintains the triple-off balance between the computational time, accuracy, and security. The simulation results indicate that the proposed SVM-based trust ranking model intelligently identifies possible malicious threats and provides an advancement in network security.

Space-Air-Ground-Sea Integrated Networks: Modeling and Coverage Analysis

Due to its potential to enable global connectivity in remote locations, such as rural areas and islands, Space-Air-Ground networks have become an ambitious solution for terrestrial communication in the sixth generation (6G) wireless communication network. In this paper, we propose a novel structure of Space-Air-Ground-Sea integrated networks (SAGSINs) to study and derive the coverage probability (CP) of users who are annotated as surface stations (SSs) on the far-reaching ocean surface that is far away from the coastline. By incorporating different types of relays such as onshore stations (OSs), tethered balloons (TBs), high altitude platforms (HAPs), and satellites (SATs), communication links between the terrestrial core connected base stations (CCBSs) and SSs are established via one of the four types of relay stations. Considering practical scenarios with a random distribution of SSs, we model the channel using the point-to-area model, which is recommended by ITU (for OSs to SS), the Rician model (for TBs or HAPs to SS), and the Shadowed-Rician model (for SATs to SS). When the SS’s distance from the coastline continues to increase from zero, since different channel models are considered, different relay stations will result in specific received signal strengths at SSs. The most powerful relay station will be chosen as the relay at one time. Hence, as we move away from the coastline, the respective strengths of the different types of relay stations vary, and hence, the association preference (among HAPs, OSs, TBs, and SATs) of the SSs changes leading to a CP value high enough even at locations far away from the coastline into the ocean. We analyze the CP using tools from stochastic geometry. Comparisons of CP between the integrated system with four types of relay stations and the single relay station system (only one type of relay station available) are represented. Numerical results verified by Monte-Carlo simulations reveal insights into the applicability of SAGSINs.

A deep reinforcement learning for energy efficient resource allocation Intelligent Reflecting Surface (IRS) driven Non-Orthogonal Multiple Access Beamforming (NOMA-BF)

This paper proposes a deep reinforcement learning centralized intelligent reflective surface (IRS) assisted non-orthogonal multiple access beamforming (NOMA-BF) system to improve the capacity gains and energy efficiency of next generation wireless communication networks. A single IRS is deployed at base station (BS) and consists of with R reflecting elements which is used to communicate with N single receiver antennas, referred to as centralized deployment of IRS- assisted NOMA. In the proposed central control scheme, we consider the practical situation where the instantaneous Channel State Information (CSI) of the channel between the IRS and the users is unknown. However, its long-term average is known and therefore makes the system more practical. Consequently, we exploit deep reinforcement technique learning technique to investigate the phase shift design and to tackle optimization problem. The energy efficiency problem is formulated as a non-convex optimization problem and is solved using deep and reinforcement learning algorithms. The first order Taylor approximation and difference of convex (DC) programming method using MATLAB tool box has been adopted to obtain the optimal values for power allocation coefficients. For interference mitigation, efficient user clustering along with beamforming algorithm is exploited. The NOMA user interference is tackled efficiently through the proposed algorithms that compute power allocation coefficients along with desirable phase shifts. The closed form expressions for achievable rates and SINR are derived under the required constraints. Simulation results demonstrate that proposed deep learning and reinforcement learning algorithms achieve improved performances in terms of sum rate, energy efficiency and coverage with lower computational complexity and symbol error rate. The computational complexity reduction has been proved both analytically and graphically and is in agreement.

Adaptive Clustering of Users in Power Domain NOMA.

By enabling multiple non-orthogonal transmissions, power domain non-orthogonal multiple access (PD-NOMA) potentially increases a system's spectral efficiency. This technique can become an alternative for future generations of wireless communication networks. The efficiency of this method fundamentally depends on two previous processing steps: an appropriate grouping of users (transmission candidates) as a function of the channel gains and the choice of power levels that will be used to transmit each signal. Thus far, the solutions presented in the literature to address the problems of user clustering and power allocation do not consider the dynamics of communication systems, i.e., the temporal variation in the number of users and the channel conditions. In order to consider these dynamic characteristics in the clustering of users in NOMA systems, this work proposes a new clustering technique based on a modification of the DenStream evolutionary algorithm, chosen for its evolutionary capacity, noise robustness and online processing. We evaluated the performance of the proposed clustering technique considering, for simplicity, the use of an already widely known power allocation strategy called improved fractional strategy power allocation (IFSPA). The results show that the proposed clustering technique can follow the system dynamics, clustering all users and favoring the uniformity of the transmission rate between the clusters. Compared to orthogonal multiple access (OMA) systems, the proposed model's gain was approximately 10%, obtained on a challenging communication scenario for NOMA systems since the channel model adopted does not favor a large difference in the channel gains between users.

Selected Aspects of Non orthogonal Multiple Access for Future Wireless Communications

In this paper overview of recent selected works that deal with novel directions in which Non orthogonal multiple access (NOMA) research is progressing is presented. These include the cell-free NOMA, deep learning extensions and optimizations of NOMA, energy optimization and task offloading with mobile-edge computing, NOMA and physical layer security, as well as virtualization, centralized-RAN aspects. All these are hot issues towards deployments of NOMA in the designs of beyond 5G and 6th generation (6G) wireless communication networks. Even though 3rd Generation Partnership Project (3GPP) has not yet made the decision regarding which NOMA techniques should be adopted, it seems like researchers already indicate clearly that NOMA has important place in the future network deployments based on ultra-density, novel 5G use-cases (massive machine type communications, ultra-reliable low latency communications). This paper highlights the most promising directions for NOMA research. The paper is summarized with necessary steps that are required to get NOMA into practical usage.

GCWCN: 6G-Based Global Coverage Wireless Communication Network Architecture

At present, the world is experiencing a significant transformation from the fifth generation (5G) wireless communication network to the sixth generation (6G) wireless communication network. An important goal is to achieve seamless global coverage of wireless signals. The combination of space network, air network and ground network has been proved to be effective, but the maritime network has not been taken into account. Therefore, how to effectively achieve the global coverage of wireless networks is a challenging topic. This paper first compares the performance of 5G and 6G, and analyzes the possibility of a wireless communication network with global coverage based on 6G technology. After that, we respectively analyze the typical characteristics of space network, air network, terrestrial network and maritime network, and then lead to the key enabling technologies to combine them. Based on the typical characteristics of the above heterogeneous networks and combined with the enabling technologies, we try to propose a 6Gsupported space-air-ground-sea integrated network (SAGSIN) architecture, to achieve seamless global wireless signal coverage. In the end, we rationally analyze a series of challenges and difficulties that may be faced in implementing the architecture.

Resource Allocation for an IRS-Assisted Dual-Functional Radar and Communication System: Energy Efficiency Maximization

Dual-functional radar and communication (DFRC) techniques are promising in the next generation wireless communication network. An intelligent reflecting surface (IRS) can improve the energy efficiency (EE) of the DFRC system, which is of crucial importance in practical applications. However, the EE achieved by using the sum-rate can be limited by the channel conditions and the radar performance requirement. Thus, in this paper, the EE maximization problem is studied in an IRS-assisted DFRC system. The EE is maximized by jointly optimizing the transmit beamforming and the IRS phase shift matrix under both the perfect channel state information (CSI) and the imperfect CSI cases. To tackle the challenging nonconvex fractional optimization problem, an alternating optimization algorithm is proposed. The high-order challenging radar performance constraint is tackled by using properties of the convex function and Hermitian matrix involved in the analysis. In the imperfect CSI case, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{S}}$ </tex-math></inline-formula> -Procedure is applied to tackle the semi-infinite inequality arising from the channel uncertainty. Simulation results demonstrate that our proposed design enjoys fast convergence and achieves a higher EE compared to the benchmark schemes, even under imperfect CSI case. Furthermore, simulation results demonstrate that the EE of the DFRC system can be significantly improved by applying IRS.

Learning-Based MIMO Channel Estimation Under Practical Pilot Sparsity and Feedback Compression

Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay estimator (P2DE) that acquires the truncated delay CSI via sparse frequency pilots. We show the accuracy of the P2DE under frequency downsampling, and we demonstrate the P2DE’s efficacy when utilized with existing CSI estimation networks. Additionally, we propose to use trainable compressed sensing (CS) networks in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet (MN-IE), which combines a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that MN-IE has better asymptotic performance than networks comprised of only one type of network.

A Novel Energy Efficiency Metric for Next-Generation Green Wireless Communication Network Design

As a core performance metric for green communications, the conventional energy efficiency (EE) definition has successfully resolved many issues in the energy-efficient wireless network design. In the past several generations of wireless communication networks, the traditional EE measure plays an important role to guide many energy-saving techniques for slow varying traffic profiles. However, for the next-generation wireless networks, the traditional EE fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultrascale multiple antennas and space–air–ground integrated network (SAGIN). In this article, we present a novel EE metric named integrated relative EE (IREE), which is able to jointly measure the traffic profiles and the network capacities from the EE perspective. On top of that, the IREE-based green tradeoffs have been investigated and compared with the conventional energy-efficient design. Moreover, we apply the IREE-based green tradeoffs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and SAGIN. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional EE metric. Since the IREE-oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy-efficient network design.

Efficient resource management in 6G communication networks using hybrid quantum deep learning model

The sixth generation (6G) wireless communication network is expected to provide wide-ranging coverage, short latency, cost effectiveness, lower power consumption, and a high level of security. The Quality of Service (QoS) can be improved with the help of proper resource management by utilizing Artificial Intelligence and Machine Learning procedures. The proposed Hybrid Quantum Deep Learning (HQDL) model which comprises of Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). The CNN achieves network reconfiguration, resource distribution and slice collection while RNN is utilized for error proportion, load balancing etc. The future model QoS performance is evaluated by utilizing numerous indefinite devices, slice features and congestion environments. The proposed model achieved an overall accuracy of 97.16% that replicates its pertinence.

Enhancing circular microstrip patch antenna performance using machine learning models

Machine learning (ML) will be heavily used in the future generation of wireless communication networks. The development of diverse communication-based applications is expected to boost coverage and spectrum efficiency in relation to conventional systems. ML may be employed to develop solutions in a wide range of domains, such as antennas. This article describes the design and optimization of a circular patch antenna. The optimization is done through ML algorithms. Six ML models, Decision Tree, Random Forest, XG-Boost Regression, K-Nearest Neighbour (KNN), Gradient Boosting Regression (GBR), and Light Gradient Boosting Regression (LGBR), were employed in this work to predict the antenna's return loss (S11). The findings show that all of these models work well, with KNN having the highest accuracy in predicting return loss of 98.5%. The antenna design &amp; optimization process can be accelerated with the support of ML. These developments allow designers to push beyond the limits of antenna technology, optimize performance, and offer novel solutions for emerging applications such as 5G, 6G, IoT, and flexible wireless communication systems).

Channel Estimation in Visible Light Communication Systems: The Effect of Input Signal-Dependent Noise

Visible light communication (VLC) has been proposed as a promising way for next generation wireless communication networks to mitigate the scarcity of the radio frequency (RF) spectrum, and has consequently attracted much attention. This paper introduces a single-input single-output (SISO) VLC system under the joint effects of statistical random channel and signal-dependent shot noise (SDSN). Moreover, it estimates the channel of the considered system using maximum likelihood (ML), least square (LS), linear minimum mean square error (LMMSE), maximum posteriori probability (MAP) and minimum mean square error (MMSE) estimators. Furthermore, a Bayesian Cramér-Rao lower bound (BCRLB) is derived for the proposed system and it is compared to the mean square error (MSE) of the proposed estimators. The problem of unknown SDSN factor, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\zeta ^{2}$</tex-math></inline-formula> , at the receiver side is discussed and two solutions are investigated. The receiver of a VLC system under SDSN and random channel gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h$</tex-math></inline-formula> is designed and its BER is studied. Finally, Monte Carlo simulation results of the proposed estimators, which show the dramatic effect of the SDSN on the considered system, are provided. In particular, the presence of noise variance, as well as the SDSN factor, causes an increase in the MSE of the system, while increasing the power reinforces the system performance.

Artificial Dust Based Attack Modelling: A Threat to the Security of Next Generation WCN

This paper introduces a systematic and novel mechanism for devising a security attack in the WCN (Wireless Communication Network). The proposed model involves the implementation of the AD (Artificial Dust) by the intruder, followed by the execution of the HD (Half-Duplex) attack. The communication network is based on the deployment of urban and rural scenarios with an unknown CSI (Channel State Information). Depending on the achieved path loss based on the distance of the user from the BS, the user with the highest path loss is particularized for the attack. The formulation of AD divulges the increased susceptibilities of the secure network specifically for the selected legitimate user. The parameter of visibility defines the amount of AD present in the communication channel. Based on the enumerated attenuation created by the artificial dust, the parameter of secrecy rate is evaluated with varying distance of the user from the BS and the operating frequency. Furthermore, the proposed scheme of the HD attack is initiated by the intruder at the specified valid user. The strategy of the attack focuses on the continuous monitor of the uplink and attempts the spoofing attack on the downlink wherein the allocation of the resources takes place. The efficacy of the proposed approach is corroborated through the examination of simulation results. The assessment of the proposed mechanism highlights notable characteristics as compared to the conventional methodology of the FD (Full-Duplex) attack.

Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes

The accelerated evolution of communication platforms including Internet of Things (IoT) and the 5th generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks to monitor the chemical safety of our surroundings and personal health in real time. However, this application scenario requires a very challenging combination of characteristics of gas sensors including small formfactor, low-cost, ultra-low power consumption, superior sensitivity, and high intelligence. Herein, we demonstrate self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network for the first time. The SINGOR features ultra-high sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10 ppb, 2 ppb, 1 ppb and 1 ppb, respectively, obtained under room temperature sensor operation. Therefore, ultra-low power consumption of 68.6 μW at sensor level and 1.71 mW at the system level has been achieved. As the result, an individual SINGOR system can be self-driven by indoor light with a Si solar cell. Furthermore, each SINGOR consists of an array of nanostructured sensors powered by machine learning algorithm and thus a SINGOR has the capability of gas pattern recognition and classification. And multiple SINGOR systems are wirelessly connected together to form a sensor network for smart home application demonstrations. Intriguingly, the sensor network has successfully demonstrated flammable gas leakage detection and alarm function when they are deployed in multiple rooms. And when the SINGOR network is deployed in one single room, it can achieve gas leakage localization with satisfactory precision. These successes open up the possibility of using nanostructured-gas-sensor network for wide range applications including smart home, smart building and future smart city.

Comprehensive Performance Analysis of Hovering UAV-Based FSO Communication System

Unmanned-aerial-vehicle (UAV)-based communications are expected to play an important role in the future generation wireless communication networks. It is a promising solution to enhance the wireless connectivity of devices without infrastructure. As compared to the terrestrial communications, UAVs offer various advantages such as line-of-sight (LoS) connectivity, dynamic deployment and flexible reconfiguration. Further, free space optics (FSO) communication is considered as a possible solution to enable UAV-based communication links due to its cost effective and high bandwidth nature. However, despite offering large bandwidth, UAV-based FSO links are marred by atmospheric path loss, atmospheric turbulence, non-zero boresight pointing errors, and angle-of-arrival (AoA) fluctuations. In this paper, the performance of a hovering UAV-based FSO communication system is investigated. The closed-form expressions for outage probability, average symbol error rate (SER), and ergodic capacity are derived taking into account both heterodyne (HD) and direct detection (DD) techniques for ground-to-UAV (G2U), UAV-to-UAV (U2U), and UAV-to-ground (U2G) links over the generalized Málaga distribution. Moreover, the asymptotic expressions for the above performance metrics are also derived to get the diversity gain of the system.