Terrestrial Communication Networks Research Articles

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.

A study of satellite network based on a comprehensive analysis

Satellite networks have emerged as a crucial component of modern communication systems. Historically, traditional terrestrial communication networks faced limitations in reaching remote and isolated areas. Conversely, Satellite networks possess the ability to provide coverage over vast geographical regions, including areas where laying terrestrial infrastructure is impractical or prohibitively expensive. The development of satellite networks was driven by the increasing demand for global connectivity, enabling seamless communication across various applications such as navigation, remote sensing, weather monitoring, and telecommunications. Moreover, advancements in technology have made satellite networks more efficient and capable. The launch of smaller yet more powerful satellites has reduced costs while increasing data transmission capacity and speed. They also play a vital role in disaster management and emergency response. This paper mainly focuses on analyzing the advantages and disadvantages of satellite networks along with several challenges encountered during their application in different fields. Considering that satellite network technology continues to develop at a significant pace, this study will also discuss future trends and make predictions accordingly. Therefore, through diverse analytical perspectives, this research demonstrates that satellite networks have emerged as critical infrastructure in the modern digital era while highlighting their highly promising prospectsa novel approach often overlooked by existing scientific literaturethus providing ample motivation for scientists to explore this domain.

Routing networking technology based on improved ant colony algorithm in space-air-ground integrated network

Space-air-ground integrated networks comprise a multi-level heterogeneous integrated network that combines satellite-based, aerial, and terrestrial networks. With the increasing human exploration of space and growing demands for internet applications, space-air-ground integrated networks have gradually emerged as the direction for communication network development. These networks face various challenges such as extensive coverage, diverse communication node types, low-quality communication links, and simultaneous operation of multiple network protocols. However, the rapid development and widespread application of artificial intelligence and machine learning technologies in recent years have offered new perspectives and solutions for the communication architecture and routing algorithm research within space-air-ground integrated networks. In these networks, not all nodes can typically communicate directly with satellites; instead, a specific set of specialized communication nodes facilitates data communication between aerial and satellite networks due to their superior communication capabilities. Consequently, in contrast to traditional communication architectures, space-air-ground integrated networks, particularly in the terrestrial layer, often need to address challenges related to the diversity of communication node types and low-quality communication links. A well-designed routing approach becomes crucial in addressing these issues. Therefore, this paper proposes an AODV routing network protocol based on an improved ant colony algorithm (AC-AODV), specifically designed for the terrestrial layer within the space-air-ground integrated networks. By integrating information such as the type, energy, and location of communication nodes, this protocol aims to facilitate network communication. The objective is to guide information flow through nodes that are more suitable for communication, either by relaying communication or by connecting with satellites through specialized nodes. This approach alleviates the burden on ordinary nodes within the terrestrial communication network, thereby enhancing the overall network performance. In this protocol, specialized nodes hold a higher forwarding priority than regular nodes. When a source node needs to transmit data, it enters the route discovery phase, utilizing its own type, location, and energy information as heuristic data to calculate forwarding probabilities. Subsequently, it broadcasts route request (RREQ) messages to find the path. Upon receiving the RREQ message, the destination node sends an RREP message for updating information elements and selects the optimal path based on these information elements. Compared to AODV, AC-AODV shows significant improvements in performance metrics such as transmission latency, throughput, energy conversion rate, and packet loss rate.

Federated Learning-Enabled Smart Jammer Detection in Terrestrial and Non-Terrestrial Heterogeneous Joint Sensing and Communication Networks

Federated Learning-Enabled Smart Jammer Detection in Terrestrial and Non-Terrestrial Heterogeneous Joint Sensing and Communication Networks

Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs

Aerial unmanned vehicles (UAVs) play a significant role in improving the connectivity and coverage of terrestrial communication networks. However, UAV-assisted air-to-ground (A2G) data transmissions usually encounter several fundamental challenges, such as terminal mobility, random nature in channel fading and contention, resource constraints, and application-specific transmission requirements. To tackle these challenges, we formulate a bi-level optimization problem that jointly considers the control of the UAV mobility and transmission power and the scheduling of A2G data transmissions. The objective is to optimize energy consumption and maximize A2G transmission reliability. Particularly, we first theoretically characterize the A2G transmission reliability from a probabilistic perspective concerning the effects of channel fading, channel access contention, and application requirements. We then derive a closed-form expression for the optimal expected transmission reliability. Using the closed-form reliability, we transform the bi-level optimization into a mathematically-tractable optimal control problem and propose an efficient iterative algorithm to solve it. Simulation results show that our approach provides a comprehensive improvement in terms of both energy utilization and A2G transmission reliability, in particular, with a reduction of more than 12.1% in energy consumption and an increase of 7.53% in reliability on average, compared to several baselines.

Dynamic resource management in integrated NOMA terrestrial–satellite networks using multi-agent reinforcement learning

The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. Moreover, in today’s interconnected world, connectivity’s reliable and widespread availability is increasingly important across various domains. This is especially more crucial for applications like the Internet of Things (IoT), remote sensing, disaster management, and bridging the digital divide. However, allocating the limited network resources efficiently and ensuring seamless handover between satellite and terrestrial networks present significant challenges. Therefore, this study introduces a resource allocation framework for integrated satellite–terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial–satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods. This research highlights the importance and addresses the need for efficient resource allocation and cache design in integrated terrestrial–satellite networks, paving the way for enhanced connectivity and improved network performance in various applications.

Performance Analysis of NOMA-RIS Aided Integrated Navigation and Communication (INAC) Networks

Satellite communication constitutes a promising solution for the sixth generation (6 G) wireless networks in terms of providing global communication services. In order to provide a cost-effective satellite network, we propose a novel medium-earth-orbit (MEO) satellite aided integrated-navigation-and-communication (INAC) network. To overcome the severe path loss of MEO satellites, we conceive a network for simultaneous serving navigation and communication for ground users by adopting the non-orthogonal multiple access (NOMA) technique and the reconfigurable intelligent surface technique. Based on the power allocation strategies, communication-oriented (CO-) and navigation-oriented (NO-) INAC scenarios are proposed. We first derive the closed-form expressions for the new channel statistics, outage probability and channel capacity of the INAC-user. For gleaning further insights, the diversity orders and navigation accuracy are evaluated for illustrating the performance of the INAC networks. According to our analysis, when RIS elements are sufficient, the proposed INAC network can perform better than conventional terrestrial communication networks in terms of channel capacity. Numerical results are provided for confirming that the NO-INAC and CO-INAC scenarios have superior performance for communication in the low signal-to-noise-ratio (SNR) regimes and high SNR regimes, respectively, which indicates a hybrid CO/NO-INAC network is preferable.

Multi-Domain Network Slicing in Satellite–Terrestrial Integrated Networks: A Multi-Sided Ascending-Price Auction Approach

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.

IRS-Enabled Secure G2A Communications for UAV System With Aerial Eavesdropping

As one of the most promising new technologies in the upcoming 6G communication, intelligent reflecting surface (IRS) can significantly improve the reliability of wireless transmission and deal with the security threat in the presence of eavesdroppers by smartly reconfiguring the wireless propagation environment. However, existing research mainly focuses on terrestrial communication networks. In contrast, there is not much research on IRS-aided secure unmanned aerial vehicle (UAV) communication problems, especially for ground-to-air (G2A) communication scenarios with air eavesdroppers that will bring greater security threats. In this article, we consider an IRS-assisted G2A communication network with an altitude-dependent Rician fading channel, where the ground base station (BS) transmits legitimate information to the UAV user in the presence of air eavesdroppers. The transmission power, active and passive beamforming, and UAV 3-D trajectory are jointly optimized to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is used to solve it. Specifically, the overall optimization problem is divided into four subproblems, and nonconvex subproblems are solved by successive convex approximation and semidefinite relaxation algorithms. Simulation results show that the proposed alternating optimization algorithm has a good convergence performance and can increase the average secrecy rate by 28% compared with the scenario without IRS assistance.

Adaptive Varying Contention Window MAC Protocol Based on Underwater Acoustic Propagation Delay

Underwater acoustic wave propagation time delay and fluctuations are much greater than radio waves. However, many of the protocols used for underwater acoustic networking and communication are designed according to the principles of terrestrial wireless communication networks, failing to give full consideration to utilizing the underwater acoustic propagation and its time delay variation. For this reason, this paper proposes an adaptive varying contention window MAC (AVCW-MAC) protocol based on propagation delay. The protocol uses AVCW random backoff mechanism to differently set up the contention window (CW) of each node based on the different propagation delays from each underwater sensor node to the cluster head node to improve the channel utilization. In addition, to avoid excessive difference in the CW of each node, the backoff factors <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> are introduced in the AVCW random backoff mechanism to improve the fairness of nodes’ accessing the channel. Simulation results show that for the same amount of sensor nodes, the AVCW-MAC protocol have significantly improved network throughput and decreased average end-to-end latency when compared to legacy CW-MAC protocols.

Dynamic SFC Embedding Algorithm Assisted by Federated Learning in Space–Air–Ground-Integrated Network Resource Allocation Scenario

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space-air-ground integrated network (SAGIN) provides a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multi-dimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This paper proposes a service provision method based on Service Function Chaining (SFC) to solve this problem. Network Function Virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federation learning (FL) based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue-cost ratio, respectively.

5G-NTN GEO-based over-the-air demonstrator using OpenAirInterface

5G services combined with the satellites, also termed 5G Non-Terrestrial Networks (5G-NTN), have the capability of providing connectivity to the areas which were previously either unreachable or too costly to be reached by terrestrial communication networks. Proof-of-Concept (POC) demonstrators, preferably based on open-source implementation are desirable to expedite the ongoing research on 5G-NTN. In this work, we discuss the contributions made during the project 5G-GOA: 5G-Enabled Ground Segment Technologies Over-The-Air Demonstrator which aims to provide direct access to 5G services to a UE through a transparent payload Geostationary (GEO) satellite. 5G-GOA uses the open-source Software-Defined-Radio (SDR) platform OpenAirInterface (OAI) and does the necessary adaptations to achieve its objectives. Adaptations span physical layer techniques (e.g., synchronization) up to upper layer implementations (e.g., timers and random-access procedures) of the Radio Access Network (RAN). The adaptations are based on 3GPP 5G-NTN discussions and the solutions are compliant with the recently frozen 3GPP Release-17. An end-to-end SDR-based 5G-NTN demonstrator has been developed for Over-The-Satellite (OTS) testing. We present results from several experiments that were conducted for in-lab validation of the demonstrator using a satellite channel emulator before going live with OTS tests. Experimental results indicate the readiness of the demonstrator for OTS testing which is scheduled during ICSSC 2022. The source code has been submitted to OAI public repository and is available for testing.

Weighted Sum Age of Information Minimization in Wireless Networks With Aerial IRS

In this letter, we analyze a terrestrial wireless communication network assisted by an aerial intelligent reflecting surface (AIRS). We consider a packet scheduling problem at the ground base station (BS) aimed at improving the information freshness by selecting packets based on their ages of information (AoIs). To further improve the communication quality, the trajectory of the unmanned aerial vehicle (UAV) which carries the IRS is optimized with joint active and passive beamforming design. To solve the formulated non-convex problem, we propose an iterative optimization problem based on successive convex approximation (SCA) algorithm. The simulation results show significant performance improvement in terms of weighted sum AoI, and the SCA solution converges quickly with low computational complexity.

Orthogonal variable spreading factor encoded unmanned aerial vehicle‐assisted nonorthogonal multiple access system with hybrid physical layer security

AbstractPhysical layer security (PLS) can improve the security of both terrestrial and nonterrestrial wireless communication networks. This study proposes a simplified framework for nonterrestrial cyclic prefixed orthogonal variable spreading factor (OVSF)‐encoded multiple‐input and multiple‐output nonorthogonal multiple access (NOMA) systems to ensure complete network security. Various useful methods are implemented, where both improved sine map and multiple parameter‐weighted‐type fractional Fourier transform encryption schemes are combined to investigate the effects of hybrid PLS. In addition, OVSF coding with power domain NOMA for multi‐user interference reduction and peak‐to‐average power ratio (PAPR) reduction is introduced. The performance of ‐rated convolutional, turbo, and repeat and accumulate channel coding with regularized zero‐forcing signal detection for forward error correction and improved bit error rate (BER) are also investigated. Simulation results ratify the pertinence of the proposed system in terms of PLS and BER performance improvement with reasonable PAPR.

Applications and prospects of artificial intelligence in covert satellite communication: a review

Satellite communication has the characteristics of wide coverage and large communication capacity, and is not easily affected by land disasters. It is quite suitable as a supplement to terrestrial communication networks and has been widely used in education, navigation, emergency relief, military, etc. However, due to the openness of the channel of satellite communication systems, satellite communication signals are easily eavesdropped on by eavesdroppers. This greatly threatens the privacy and security of countries and individuals. Covert satellite communication can effectively improve the covertness of satellite communication systems and greatly reduce the probability of detection by eavesdroppers. So, it has attracted more and more attention. In addition, with the development of artificial intelligence (AI), AI has been applied in many technics of covert satellite communication, which has achieved higher reliability and stronger concealment in covert satellite communication systems. The research status of key technics in covert satellite communication is discussed in this study, and the applications of AI in covert satellite communication are shown. Finally, future research directions of covert satellite communication are looked forward to. In the future, covert satellite communication technology will be an indispensable part of satellite communication systems.

Relay-Aided D2D MIMO Scheme (RAS) for Achieving Energy Efficiency in Satellite-Air-Ground Integrated Networks (SAGIN) Schéma D2D MIMO assisté par relais (RAS) pour atteindre l’efficacité énergétique dans les réseaux intégrés satellite-air-sol (SAGIN)

Space-air-ground integrated network (SAGIN), as a three-tiered architecture that assimilates satellite systems, aerial, and terrestrial communication networks, has become an intensive research domain in the present era of communications. SAGIN-based communication models are developed to enhance the user’s quality of experience (QoE). Besides providing noteworthy benefits in various applications and services, SAGIN has unprecedented challenges because of its self-organized, unpredictable, and heterogeneous nature. Relaying equipment in SAGIN can be a very low-orbit satellite, a base station (BS), and an unmanned vehicle assisting a pair of mobile users’ communications. Thus, developing a robust device-to-device (D2D) direct and relaying communication model concerning channel distribution is crucial. Based on this concern, this article proposes a relay-aided D2D multiple–input and multiple–output (MIMO) scheme (RAS) for enhancing the optimal energy efficiency (EE) as a function of spectral efficiency (SE). The proposed model derives a relay-based amplify-and-forward (AF) MIMO multihop communication system for implementation. The proposed computations of optimal EE and SE for D2D MIMO show that the approximation provided by a random matrix approximation is constrained to a specific signal-to-noise ratio (SNR) range when the optimal SE and EE are derived using Gaussian quadrature and a hypergeometric function.

Resource Allocation for Networked Telemetry System of Mega LEO Satellite Constellations

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and geostationary earth orbit (GEO)-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems, which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system and approximates the upper bound performance.

Comparative Computational Study of Double Rotating Cylinder Embedded on Selig S1223 Aerofoil and Flat Plate for High Altitude Platform

The high-altitude platform was built as an alternative approach to address the weakness of the terrestrial and satellite communication networks. It can be an aircraft or balloon positioned 20 to 50 km above the earth’s atmosphere. The use of the Magnus effect was not noticeable in the production of the high-altitude platform, while past research study has denoted its aerodynamic performance in generating greater lift and stall angle delay, which would be beneficial in creating such a flying device. This research delineates the proposed designs using the computational fluid dynamics approach utilizing ANSYS WORKBENCH 2019 software. The embedment of the rotating cylinder onto the design would best portray the use of the Magnus effect in generating higher lift coefficients with probable delay in stall angle. Hereby, the design of embedding rotating cylinder onto Selig S1223 aerofoil and the flat plate is proposed to test their aerodynamic performances for high altitude platform purposes. Here, Fluent fluid flow analysis was simulated for 500 RPM and 1000 RPM momentum injection with free stream velocities from 5 m/s to 30 m/s for different angles of attack of 0 to 20 degrees. The analysis has resulted in a greater impact on its lift coefficient and stall angle delay of about 39% and 53% enhancement for modified aerofoil while showing 128% and 204% betterment for modified flat plate than their respective unmodified model. Therefore, it is perceived that the CyFlaP has better stability yet is simplistic in a design suitable for HAP application.

Grant-Free Coexistence of Critical and Noncritical IoT Services in Two-Hop Satellite and Terrestrial Networks

Terrestrial and satellite communication networks often rely on two-hop wireless architectures with an access channel followed by backhaul links. Examples include cloud-radio access networks (C-RAN) and low-Earth orbit (LEO) satellite systems. Furthermore, communication services characterized by the coexistence of heterogeneous requirements are emerging as key use cases. This article studies the performance of critical service (CS) and non-CS (NCS) for Internet-of-Things (IoT) systems sharing a grant-free channel consisting of radio access and backhaul segments. On the radio access segment, IoT devices send packets to a set of noncooperative access points (APs) using slotted ALOHA (SA). The APs then forward correctly received messages to a base station over a shared wireless backhaul segment adopting SA. We study first a simplified erasure channel model, which is well suited for satellite applications. Then, in order to account for terrestrial scenarios, the impact of fading is considered. Among the main conclusions, we show that orthogonal interservice resource allocation is generally preferred for NCS devices, while nonorthogonal protocols can improve the throughput and packet success rate of CS devices for both terrestrial and satellite scenarios.

Joint Bayesian Channel Estimation and Data Detection for OTFS Systems in LEO Satellite Communications

Lower earth orbit (LEO) satellites play an important role in the integration of space and terrestrial communication networks, which typically encounter high-mobility scenarios. It has been shown that orthogonal time frequency space (OTFS) modulation performs well in such high-mobility scenarios by transforming the time-varying channels into the delay-Doppler domain. In this paper, we develop a joint channel estimation and data detection algorithm for OTFS-based LEO satellite communications. Firstly, we adopt the powerful variational Bayesian inference (VBI) method for estimating the delay-Doppler channel vector, which contains the channel gain, the delay and the Doppler. Secondly, we exploit the unknown data symbols in an OTFS frame as ‘virtual pilots’ for improving the accuracy of channel estimation and detect them simultaneously. Our simulation results demonstrate that the proposed algorithm achieves improved channel estimation mean square error and bit error rate performance than its conventional counterparts.