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Dynamic resource management in integrated NOMA terrestrial-satellite networks using multi-agent reinforcement learning
Abstract
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.
Unmanned aerial vehicles (UAV) have emerged as a practical solution that provides on-demand services to users in areas where the terrestrial network is non-existent or temporarily unavailable, e.g., due to natural disasters or network congestion. In general, UAVs' user-serving capacity is typically constrained by their limited battery life and the finite communication resources that highly impact their performance. This work considers the orthogonal frequency division multiple access (OFDMA) enabled multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive an efficient technique for the allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: (i) 3D placement and user association and (ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after fewer than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, results showed that the optimal power and sub-carrier allocation help to mitigate the inter-cell interference that directly impacts the system's performance.
The forthcoming era of the automotive industry, known as Automotive-Industry 5.0, will leverage the latest advancements in 6G communications technology to enable reliable, computationally advanced, and energy-efficient exchange of data between diverse onboard sensors, drones and other vehicles. We propose a non-orthogonal multiple access (NOMA) multi-drone communications network in order to address the requirements of enormous connections, various quality of services (QoS), ultra-reliability, and low latency in upcoming sixth-generation (6G) drone communications. Through the use of a power optimization framework, one of our goals is to evaluate the energy efficiency of the system. In particular, we define a non-convex power optimization problem while considering the possibility of imperfect successive interference cancellation (SIC) detection. Therefore, the goal is to reduce the total energy consumption of NOMA drone communications while guaranteeing the lowest possible rate for wireless devices. We use a novel method based on iterative sequential quadratic programming (SQP) to get the best possible solution to the non-convex optimization problem so that we may move on to the next step and solve it. The standard OMA framework, the Karush–Kuhn–Tucker (KKT)-based NOMA framework, and the average power NOMA framework are compared with the newly proposed optimization framework. The results of the Monte Carlo simulation demonstrate the accuracy of our derivations. The results that have been presented also demonstrate that the optimization framework that has been proposed is superior to previous benchmark frameworks in terms of system-achievable energy efficiency.
The integration of satellite and terrestrial networks has become inevitable in the next generations of communications networks due to emerging needs of ubiquitous connectivity of remote locations. New and existing services and critical infrastructures in remote locations in sea, on land and in space will be seamlessly connected through a diverse set of terrestrial and non-terrestrial communication technologies. However, the integration of terrestrial and non-terrestrial systems will open up both systems to unique security challenges that can arise due to the migration of security challenges from one to another. Similarly, security challenges can also arise due to the incompatibility of distinct systems or incoherence of security policies. The resulting security implications, thus, can be highly consequential due to the criticality of the infrastructures such as space stations, autonomous ships, and airplanes, for instance. Therefore, in this article we study existing security challenges in satellite-terrestrial communication systems and discuss potential solutions for those challenges. Furthermore, we provide important research directions to encourage future research on existing security gaps.
Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks.
What will the future of UAV cellular communications be? In this tutorial article, we address such a compelling yet difficult question by embarking on a journey from 5G to 6G and expounding a large number of case studies supported by original results. We start by overviewing the status quo on UAV communications from an industrial standpoint, providing fresh updates from the 3GPP and detailing new 5G NR features in support of aerial devices. We then dissect the potential and the limitations of such features. In particular, we demonstrate how sub-6GHz massive MIMO can successfully tackle cell selection and interference challenges, we showcase encouraging mmWave coverage evaluations in both urban and suburban/rural settings, and we examine the peculiarities of direct device-to-device communications in the sky. Moving on, we sneak a peek at next-generation UAV communications, listing some of the use cases envisioned for the 2030s. We identify the most promising 6G enablers for UAV communication, those expected to take the performance and reliability to the next level. For each of these disruptive new paradigms (non-terrestrial networks, cell-free architectures, artificial intelligence, reconfigurable intelligent surfaces, and THz communications), we gauge the prospective benefits for UAVs and discuss the main technological hurdles that stand in the way. All along, we distil our numerous findings into essential takeaways, and we identify key open problems worthy of further study.
Industrial Internet of Things (IIoT) formation of
richer ecosystem of intelligent interconnected devices while en�abling new levels of digital innovation has essentially transformed
and revolutionized global manufacturing and industry 4.0. Con�versely, the prevalent distributed nature of IIoT, Industrial 5G,
underlying IoT sensing devices, IT/OT convergence, Edge Com�puting, and Time Sensitive Networking makes it an impressive
and potential target for cyber-attackers. Multi-variant persistent
and sophisticated bot attacks are considered catastrophic for
connects IIoTs. Besides, botnet attack detection is extremely
complex and decisive. Thus, efficient and timely detection of
IIoT botnets is a dire need of the day. We propose a hybrid
intelligent Deep Learning (DL)-enabled mechanism to secure
IIoT infrastructure from lethal and sophisticated multi-variant
botnet attacks. The proposed mechanism has been rigorously
evaluated with latest available dataset, standard and extended
performance evaluation metrics, and current DL benchmark
algorithms. Besides, cross validation of our results are also
performed to clearly show overall performance. The proposed
mechanisms outperforms in identifying accurately multi-variant
sophisticated bot attacks by achieving 99.94% detection rate.
Besides, our proposed technique attains 0.066(ms) time that also
shows the promising results in terms of speed efficiency.
In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine‐learning solutions for real‐time decision‐making and radio resource management. Traditional machine learning employs fully centralized architecture in which the entire training data is collected at one node for example, cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Toward this end, a distributed machine‐learning paradigm termed as federated learning (FL) has been proposed recently. In FL, each participating edge device trains its local model by using its own training data. Then, via the wireless channels the weights or parameters of the locally trained models are sent to the central parameter server (PS), that aggregates them and updates the global model. On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. Thus, a “bidirectional” relationship exists between FL and wireless communications. Although FL is an emerging concept, many publications have already been published in the domain of FL and its applications for next generation wireless networks. Nevertheless, we noticed that none of the works have highlighted the bidirectional relationship between FL and wireless communications. Therefore, the purpose of this survey article is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.
Non-terrestrial networks (NTNs) will become an indispensable part of future wireless networks. Integration with terrestrial networks will provide new opportunities for both satellite and terrestrial telecommunication industries and therefore there is a need to harmonize them in a unified technological framework. Among different NTNs, low earth orbit (LEO) satellites have gained increasing attention in recent years and several companies have filed federal communication commission (FCC) proposals to deploy their LEO constellation in space. This is mainly due to several desired features such as large capacity and low latency. In addition, recent successful LEO network deployments such as Starlink have motivated other companies. In the past satellite and terrestrial wireless networks have been evolving separately but now they are joining forces to enhance coverage and connectivity experience in the future wireless networks. The 3rd Generation Partnership Project (3GPP) is one of the dominating standardization bodies that is working on various technical aspects to provide ubiquitous access to the 5G networks with the aid of NTNs. Initial steps have been taken to adopt 5G state of the art technologies and concepts and harmonized them with the conditions met in non-terrestrial networks. In this article, we review some of the important technical considerations in 5G NTNs with emphasis on the radio access network (RAN) part and provide some simulation based results to assess the required modifications and shed light on the design considerations.
In recent years, wireless communication has experienced a massive shift from a single service (i.e., voice) to an interconnected web of networks. Although many techniques have been developed improving the offered services to mobile users, still the demand for high-quality services cannot be reached. Therefore, this paper proposes a joint non-orthogonal multiple access (NOMA)-enabled optimization framework for small-cell network (SCNet) by utilizing the concepts of multi-objective problem. In particular, the transmit power of base station (BS) in each small-cell simultaneously optimizes to maximize
the sum-capacity and total energy efficiency (EE) of SCNet. The multi-objective optimization problem is formulated as non-convex subject to several practical constraints, i.e., individual quality of service requirement, maximum power budget of small-cell BS, and efficient decoding of superimposed signal using successive interference cancellation. Based on the nature of the problem, the optimal solutions are provided using sequential quadratic programming, and Karush-Kuhn-Tucker approaches. The obtained results show significant performance gains over conventional orthogonal multiple access technique in terms of sum-capacity and total EE.
Multi-layer satellite networks (MLSNs) is of great potential for the integrated 5G networks to provide diversified services. However, MLSNs confront frequency interference coordination problem between satellite systems in different orbits. This paper investigates a joint user pairing and power allocation scheme in a non-orthogonal multiple access (NOMA)-based geostationary earth orbit (GEO) and low earth orbit (LEO) satellite network. Specifically, a novel NOMA framework with two uplink receivers, i.e. the GEO and LEO satellites is established where the NOMA groups are formed considering the subcarrier assignment of ground users. To maximize the system capacity, an optimization problem is then introduced subject to the decoding threshold and power consumption. Since the formulated problem is non-convex and mathematically intractable, we decompose it into user pairing and power allocation schemes. In the user pairing scheme, virtual GEO users are generated to transform the multi-user pairing problem into a matching problem and a max-min pairing strategy is adopted to ensure the fairness among NOMA groups. In the power allocation scheme, the non-convex problem is transformed into multiple convex subproblems and solved by iterative algorithm. Simulation results validate the effectiveness and superiority of the proposed schemes when compared with several existing schemes.
To meet the demands of massive connections, diverse quality of services (QoS), ultra-reliable and low latency in the future sixth-generation (6G) Internet-of-vehicle (IoV) communications, we propose non-orthogonal multiple access (NOMA)-enabled small-cell IoV network (SVNet). We aim to investigate the trade-off between system capacity and energy efficiency through a joint power optimization framework. In particular, we formulate a nonlinear multi-objective optimization problem under imperfect successive interference cancellation (SIC) detecting. Thus, the objective is to simultaneously maximize the sum-capacity and minimize the total transmit power of NOMA-enabled SVNet subject to individual IoV QoS, maximum transmit power and efficient signal detecting. To solve the nonlinear problem, we first exploit a weighted-sum method to handle the multi-objective optimization and then adopt a new iterative Sequential Quadratic Programming (SQP)-based approach to obtain the optimal solution. The proposed optimization framework is compared with Karush-Kuhn-Tucker (KKT)-based NOMA framework, average power NOMA framework, and conventional OMA framework. Monte Carlo simulation results unveil the validness of our derivations. The presented results also show the superiority of the proposed optimization framework over other benchmark frameworks in terms of system sum-capacity and total energy efficiency.
Non-orthogonal multiple access (NOMA) and backscatter communications are considered to be promising technologies for beyond the fifth-generation (5G) due to their applications in large-scale Internet-of-things networks for providing low-powered and spectral-efficient communication. NOMA also provides a new way to enable vehicle-to-everything (V2X) networks and improve the achievable rates through cooperation. Motivated by these developments, we provided a novel analysis for NOMA-enabled backscatter-based V2X networks. Specifically, we consider that several vehicles are connected to the base station (BS) via different roadside units (RSUs) and the backscatter tags along the road. These backscatter tags can be considered ultra-low-powered safety sensors that communicate with the vehicles using the same spectrum resources. To optimize the performance of such networks, a joint problem of optimal power allocation at BS and RSUs has been formulated. Subsequently, the problem has been transformed into a convex problem and solved using KKT conditions and sub-gradient methods. To evaluate the performance of the proposed solution, Monte-Carlo simulations have been performed in MATLAB. The acquired results clearly demonstrate that the proposed approach performs better than the conventional suboptimal NOMA scheme and joint optimal TDMA scheme.
This paper proposes a new optimization framework for power-domain non-orthogonal multiple access (PD-NOMA) ambient backscatter communication (AmBC) system with imperfect successive interference cancellation decoding. In particular, we jointly optimize the transmit power of the source and the reflection coefficient of the backscatter tag to enhance the sumrate of the system. Closed-form solutions are derived for the considered joint optimization problem via iteratively computing the Lagrange multipliers by the sub-gradient method. Simulation results validate the superiority of the proposed joint optimization framework over other benchmark frameworks, namely suboptimal PD-NOMA AmBC, optimal pure PD-NOMA, and orthogonal multiple access.
Non-Orthogonal Multiple Access (NOMA) has the potentials to improve the performance of multi-beam satellite systems. The performance optimization in satellite-NOMA systems can be different from that in terrestrial-NOMA systems, e.g., considering distinctive channel models, performance metrics, power constraints, and limited flexibility in resource management. In this paper, we adopt a metric, Offered Capacity to requested Traffic Ratio (OCTR), to measure the requested-offered data (or rate) mismatch in multi-beam satellite systems. In the considered system, NOMA is applied to mitigate intra-beam interference while precoding is implemented to reduce inter-beam interference. We jointly optimize power, decoding orders, and terminal-timeslot assignment to improve the max-min fairness of OCTR. The problem is inherently difficult due to the presence of combinatorial and non-convex aspects. We first fix the terminal-timeslot assignment and develop an optimal fast-convergence algorithmic framework based on the Perron-Frobenius theory (PF) for the remaining joint power-allocation and decoding-order optimization problem. Under this framework, we propose a heuristic algorithm for the original problem, which iteratively updates the terminal-timeslot assignment and improves the overall OCTR performance. Numerical results verify that max-min OCTR is a suitable metric to address the mismatch issue, and is able to improve the fairness among terminals. On average, the proposed algorithm improves the max-min OCTR by 40.2% over Orthogonal Multiple Access (OMA).
Fifth-generation (5G) telecommunication systems are expected to meet world market demands of accessing and delivering services anywhere and anytime. Non-Terrestrial Networks (NTN) are able to satisfy requests of anywhere and anytime connection by offering wide-area coverage and ensuring service availability, continuity, and scalability. In this paper, we review 3GPP NTN features and their potential in satisfying user expectations in 5G & beyond networks. State of the art, current 3GPP research activities, and open issues are investigated to highlight the importance of NTN in wireless communication networks. Finally, future research directions are identified to assess the role of NTN in 5G and beyond systems. INDEX TERMS Non-Terrestrial Networks, Satellite communication, New Radio, 5G systems, 6G.
The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.
Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.
Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.
Device positioning has generally been recognized as an enabling technology for numerous vehicular applications in intelligent transportation systems (ITS). The downlink time difference of arrival (DL-TDOA) technique in cellular networks requires range information of geographically diverse base stations (BSs) to be measured by user equipment (UE) through the positioning reference signal (PRS). However, inter-cell interference from surrounding BSs can be particularly serious under poor network planning or dense deployments. This may lead to a relatively longer measurement time to locate the UE, causing an unacceptable location update rate to time-sensitive applications. In this case, PRS muting of certain wireless resources has been envisioned as a promising solution to increase the detectability of a weak BS. In this paper, to reduce UE measurement latency while ensuring high location accuracy, we propose a muting strategy managed by positioning functions that utilizes a combination of optimized pseudo-random sequences (CO-PRS) for multiple BSs to coordinate the muting of PRS resources. The original sequence is first truncated according to the muting period, and a modified greedy selection is performed to form a set of control sequences as the muting configurations (MC) with balance and concurrency constraints. Moreover, efficient information exchange can be achieved with the seeds used for regenerating the MC. Extensive simulations demonstrate that the proposed scheme outperforms the conventional random and ideal muting benchmarks in terms of measurement latency by about 30%, especially when dealing with severe near-far problems in cellular networks.
Integrating terrestrial and non-terrestrial networks has the potential of connecting the unconnected and enhancing the user experience for the already-connected, with technological and societal implications of the greatest long-term significance. A convergence of ground, air, and space wireless communications also represents a formidable endeavor for the mobile and satellite communications industries alike, as it entails defining and intelligently orchestrating a new 3D wireless network architecture. In this article, we present the key opportunities and challenges arising from this revolution by presenting some of its disruptive use cases and key building blocks, reviewing the relevant standardization activities, and pointing to open research problems. By considering two multi-operator paradigms, we also showcase how terrestrial networks could be efficiently re-engineered to cater for aerial services, or opportunistically complemented by nonterrestrial infrastructure to augment their current capabilities.
The 3rd generation partnership project (3GPP) radio access network (RAN) plenary recently approved a work package for its Release 18, which represents a major evolution and is branded as the first release of 5G Advanced. The work package includes diverse study and work items which will further boost 5G performance significantly and address a wide variety of new use cases. In this article, we provide an overview of the 5G Advanced evolution in 3GPP Release 18.
5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.
The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions, but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine-learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.
Fifth-generation (5G) wireless networks are projected to support a large number of low-power devices, which are an essential component of Internet of Things (IoT) technologies. The existing network design is expected to face a number of challenges in serving such massive additional devices. Furthermore, these devices are bound by limited power processing and storage capacity, despite the fact that they are supposed to analyze massive volumes of data. In the last decade, the concepts of cloud and edge computing have received significant attention in order to increase network performance. Furthermore, wireless power transfer (WPT) is integrated with the cloud for energy transmission to low-power devices. This work presents a mathematical model for scheduling tasks that are being executed at low-power devices and concurrently on the cloud edge. The proposed mathematical model takes into account practical constraints and is intended to distribute resources among devices optimally while minimizing network overhead. To deal with the conflicting constraints, the model is further transformed into an unconstrained one. To solve the proposed model, evolutionary methods are being explored. Finally, a Monte Carlo simulation is used to validate the model. The simulation results show that partial offloading outperforms the edge-only computation scheme, with the partial offloading scheme demonstrating a 20% improvement in network overhead.
Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.
We consider the problem of task offloading and resource allocation in mobile edge computing (MEC). To maintain satisfactory quality of experience (QoE) of end-users, mobile devices (MDs) may offload their tasks to edge servers based on the allocated computation (e.g., CPU/GPU cycles and storage) and wireless resources (e.g., bandwidth). However, these resources could not be effectively utilized unless an encouraging resource allocation scheme can be proposed. What's worse, task offloading incurs additional MEC energy consumption, which inevitably violate the long-term MEC energy budget. Considering these two challenges, we propose an online joint offloading and resource allocation (JORA) framework under the long-term MEC energy constraint, aiming at guaranteeing the end-users' QoE. To achieve this, we leverage Lyapunov optimization to exploit the optimality of the long-term QoE maximization problem. By constructing an energy deficit queue to guide energy consumption, the problem can be solved in a real-time manner. On this basis, we propose online JORA methods in both centralized and distributed manners. Furthermore, we prove that our proposed methods enable the achievement of the close-to-optimal performance while satisfying the long-term MEC energy constraint. In addition, we conduct extensive simulations and the results show superiority in performance over other methods.
A hybrid satellite-terrestrial relay network (HSTRN) has been envisioned as a promising communication architecture for future wireless communications, which can prominently reduce the impact of shadow fading and expand service coverage. In this paper, we conduct theoretical performance analysis for a dual-hop communication system, consisting of multiple relays and multiple users in the HSTRN with the amplify-and-forward protocol. For the shadowing effect and the multipath effect, we introduce the shadowed-Rician distribution and Nakagami-
$m$
fading to characterize the channel models for the satellite-relay and the relay-user links, respectively. Additionally, the opportunistic scheduling scheme is employed, where the optimal relay and the optimal user are selected from the alternative nodes with the maximum instantaneous signal-to-noise ratio. Thereafter, the analytical expressions including ergodic capacity and average symbol error rate (SER) are derived. More specifically, we also present the asymptotic behavior of the average SER at the high signal-to-noise ratio (SRN) regime. The theoretical analysis is validated by the numerical results. Moreover, the results also reveal that the performance of the system can be improved by increasing the number of relays and users under different channel parameters and various modulation schemes.
This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms.
Wireless powered communication (WPC) has been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we study a wireless powered time-division duplex (TDD) multiuser multiple-input multiple-output (MU-MIMO) system, where the base station (BS) has its own power supply and all users can harvest radio frequency (RF) energy from the BS. We aim to maximize the users’ information rates by jointly optimizing the duration of users’ time slots and the signal covariance matrices of the BS and users. Different to the commonly used sum rate and max-min rate criteria, the proportional fairness of users’ rates is considered in the objective function. We first study the ideal case with the perfect channel state information (CSI), and show that the non-convex proportionally fair rate optimization problem can be transformed into an equivalent convex optimization problem. Then we consider practical systems with imperfect CSI, where the CSI mismatch follows a Gaussian distribution. A chance-constrained robust system design is proposed for this scenario, where the Bernstein inequality is applied to convert the chance constraints into the convex constraints. Finally, we consider a more general case where only partial knowledge of the CSI mismatch is available. In this case, the conditional value-at-risk (CVaR) method is applied to solve the distributionally robust system rate optimization problem. Simulation results are presented to show the effectiveness of the proposed algorithms.
Intelligent transport systems demand the provision of a continuous high-accuracy positioning service. However, a vehicle positioning system typically has to operate in dense urban areas where conventional satellite-based positioning systems suffer severe performance degradation. 5G technology presents a new paradigm to provide ubiquitous connectivity, where the vehicle-to-everything (V2X) communication turns out to be highly conducive to enable both accurate positioning and the emerging Internet of Vehicles (IoV). Due to the high probability of Line-of-Sight (LoS) communication, as well as the diversity and number of reference stations, the application of ultradense networks (UDN) in the vehicle-to-infrastructure (V2I) subsystem is envisaged to complement the existing positioning technologies. Moreover, the cooperative determination of location information could be enhanced by the vehicle-to-vehicle (V2V) subsystem. In this article, we propose a V2X-integrated positioning methodology in UDN, in which the V2I, V2V, and inertial navigation systems (INSs) are unified for data fusion. This formulation is an iterative high-dimensional estimation problem, and an efficient multiple particle filter (MPF)-based method is proposed for solving it. In order to mitigate the non-LoS (NLoS) impact and provide a relatively accurate input to the MPF, we introduce an advanced anchor selection method using the geometry-based
${K}$
-means clustering (GK) algorithm based on the characteristics of network densification. Numerical results demonstrate that utilizing the GK algorithm in the proposed integrated positioning system could achieve 18.7% performance gains in accuracy, as compared with a state-of-the-art approach.
Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this paper, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a Mobility-Aware Computational Efficiency based Task Offloading and Resource Allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the best solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.
We provide an overview of the 3rd Generation Partnership Project (3GPP) work on evolving the 5G wireless technology to support non-terrestrial satellite networks. Adapting 5G to support non-terrestrial networks entails a holistic design spanning multiple areas from radio access network to services and system aspects to core and terminals. In this article, we describe the main topics of non-terrestrial networks, explain in detail the design aspects, and share various design rationales influencing standardization.
The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.
Beam scanning antenna brings new opportunities to reduce the data collision of multinode communication in the Internet of Things (IoT). This article proposes a new type antenna design scheme that can be used for IoT relay communication. A programmable beam scanning antenna without phase shifters operating in the X-band is designed to evaluate the feasibility of this scheme. The integrated design of excitation source and phase-modulated structure greatly reduces the profile of the antenna and improves the integrated degrees of freedom with other equipment. By switching the state of the PIN diode loaded on each element, different radiators can be selected and the direction of current polarization on elements can be adjusted to change the radiation phase. Furthermore, the diode states are encoded by the holographic antenna theory and controlled by the FPGA controller to realize programmable beam scanning. One-element, two-element, and 1-D array containing 32 elements are fabricated and measured. The experimental results are in good agreement with the simulation data. The operating bandwidth is 7.5% at 10 GHz while the beam scanning range of the array is
$- 60{^\circ }$
–60°, with a good scanning accuracy and stability. The proposed low-profile beam scanning antenna can provide stable communication services to multiple users and smart devices as a new type of beam scanning array antenna solution for the IoT application, which is beneficial to this field.
In isolated regions, utilizing the unmanned aerial vehicle (UAV) as an aerial anchor node is a promising technique to enable location awareness of ground terminals (GTs). In this letter, considering a UAV swarm-enabled localization for a group of distributed GTs, we aim to minimize the maximum Cramer-Rao lower bound (CRLB) for position estimates with anchor uncertainty. Then, an efficient differential evolution (DE)-based method is proposed to find a sub-optimal solution. In particular, the rigidity of the UAV swarm is recognized as a critical constraint in the problem formulation to provide a unique swarm coordinate configuration and to maintain a prescribed flight formation. A gradient-based local optimization for rigidity is then proposed and embedded in the DE algorithm. Numerical results demonstrate that our proposed designs can reach better performance in localization accuracy while ensuring the rigidity of the UAV swarm, as compared with a random approach.
In this paper, we consider non-orthogonal multiple access (NOMA) applying into massive multiple input multiple output (mMIMO) low earth orbit (LEO) satellite communication system (SCS) to improve spectral efficiency. Specifically, we investigate sum rate maximization problem with transmit power, quality of service (QoS) constraints, imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI) considered. We decouple the original problem into two parts: precoding vectors design and transmit power optimization. Precoding vectors are derived for maximizing the average signal power in each beam and eliminating inter-beam interference (IBI). Then, transmit power optimization problem is transformed into a convex optimization problem by utilizing the first order Taylor expansion, an iterative algorithm is proposed to get the optimal sum rate. Finally, simulation results verify the convergence of the proposed algorithm and our proposed mMIMO NOMA approach is better than other approaches.
The space-air-ground integrated network (SAGIN) can enhance the performance of the Internet of Vehicles (IoV). However, the basic hardware differences among communication systems are large, which leads to communication difficulties between different communication systems. To effectively manage multiple communications networks (satellite networks, air networks and terrestrial networks) and computing resources in IoV, this paper proposes a SAGIN-IoV edge-cloud architecture based on software defined networking (SDN) and network function virtualization (NFV). In addition, we construct an optimization model based on SAGIN-IoV’s service requirements, and propose an improved algorithm. Experimental results show that the improved algorithm can effectively optimize the resource scheduling problem of SAGIN-IoV.
Drone-assisted camera networks can be used in many applications. However, different application requirements lead to different deployment scenarios. In this paper, based on a 3D terrain environment represented by triangular mesh data, a many-objective optimization model for the deployment of multiple onboard cameras is constructed. We propose an improved version of the constrained two-archive evolutionary algorithm. A selection operator based on Gaussian process regression is used for enhancement. Additionally, we quantize the polynomial mutation operator. The improved algorithm is applied to optimize drone-assisted camera deployment, and the experimental results show that the improved algorithm is superior to state-of-the-art algorithms.
In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee the low latency requirements of the current intelligent transportation system (ITS). As a supplement to cloud computing, fog computing can effectively alleviate the bottlenecks of cloud computing bandwidth and computing resources and improve the quality of service (QoS) of the IoV. However, as a distributed system that operates near users, fog computing has a complicated network structure. In the complex and dynamic IoV environment, to effectively manage these computing resources with different attributes and provide high-quality services, it is necessary to design an efficient architecture and a resource allocation algorithm. Therefore, on the basis of fog-cloud computing and software-defined networking (SDN), a novel 5G IoV architecture is designed. In addition, after fully considering the service requirements of the IoV, a model of four objectives is constructed, and a many-objective optimization algorithm is proposed. The experiment results show that the proposed algorithm outperforms the other state-of-the-art algorithms.
Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terrestrial base stations (NT-BSs) however leads to a dynamic environment, which imposes unique challenges for handover and throughput optimization particularly in multi-user access control for NTNs. To achieve performance optimization, each terrestrial user equipment (UE) should autonomously estimate the dynamics of moving NT-BSs, which is different from the existing user access control schemes in terrestrial wireless networks. Consequently, new learning schemes for optimum multi-user access control are desired. In this paper, we therefore propose a UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN. With the proposed scheme, each UE is able to access a proper NT-BS intelligently to enhance the long-term system throughput and avoid frequent handovers among NT-BSs. Through comprehensive simulation studies, we justify the performance of the proposed scheme, and show its effectiveness in addressing the fundamental issues in the NTNs deployment.
Many organizations recognize non-terrestrial networks (NTNs) as a key component to provide cost-effective and high-capacity connectivity in future 6th generation (6G) wireless networks. Despite this premise, there are still many questions to be answered for proper network design, including those associated to latency and coverage constraints. In this article, after reviewing research activities on NTNs, we present the characteristics and enabling technologies of NTNs in the 6G landscape and shed light on the challenges in the field that are still open for future research. As a case study, we evaluate the performance of an NTN scenario in which aerial/space vehicles use millimeter wave (mmWave) frequencies to provide access connectivity to on-the-ground mobile terminals as a function of different networking configurations.
Non-orthogonal multiple access (NOMA) is a rapidly emerging paradigm with the capability to improve the spectral efficiency of data-driven, intelligence inspired, and highly digitized sixth-generation (6G) wireless networks. In the backdrop of ever-evolving NOMA techniques, this article presents a novel resource optimization framework for maximizing the spectral efficiency (SE) of the Internet-of-things (IoT) networks using power domain NOMA. The proposed framework considers a limited number of frequency blocks in the IoT network and provides an optimal power and frequency block allocation method. Different practical constraints like successive interference cancellation (SIC) complexity, ensuring the minimum gap of received power among different IoT equipment over the same frequency block for successful SIC operation, quality of services requirements, and IoT equipment's transmit powers have also been taken into account. Accordingly, a non-convex optimization problem has been formulated for resource management where the objective of spectral efficiency is coupled by both frequency block and power allocation. To effectively solve this problem, the resource optimization problem is decoupled into two subproblems for frequency block assignment and power allocation. A suboptimal algorithm has been designed for frequency block assignment and a new optimal sequential quadratic programming (SQP) approach is employed to solve the non-convex power control subproblem. For the sake of fair comparison, a low complexity suboptimal NOMA power allocation scheme, based on Karush-Kuhn-Tucker (KKT) conditions, and conventional orthogonal multiple access (OMA) scheme are also provided. The results demonstrate that the proposed optimal resource management scheme significantly improves the system performance compared to other schemes.
Centralized radio resource management method puts all of the computational burdens in an agent, which is unbearable with the increasing of data dimensionality. This paper focuses on how to schedule limited satellite-based radio resources efficiently to enhance transmission efficiency and extend broadband coverage with low complexity. We propose a cooperative multi-agent deep reinforcement learning (CMDRL) framework to achieve the radio resources management strategy. The bandwidth allocation problem is taken as an example to analyze the proposed novel method in simulation. The experimental results show that this approach is capable of enhancing transmission efficiency and be flexible to achieve the desired goal with low complexity.
This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA). The delivery of a large volume of multimedia contents for ground users is assisted by a mobile UAV base station, which caches some popular contents for wireless backhaul link traffic offloading. In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance. To cope with the dynamic UAV locations and content requests in practical scenarios, we formulate the long-term caching placement and resource allocation optimization problem for content delivery delay minimization as a Markov decision process (MDP). The UAV acts as an agent to take actions for caching placement and resource allocation, which includes the user scheduling of content requests and the power allocation of NOMA users. In order to tackle the MDP, we propose a Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with
soft ${\varepsilon }$ -greedy
strategy to search for the optimal match between actions and states. Since the action-state table size of Q-learning grows with the number of states in the dynamic networks, we propose a function approximation based algorithm with combination of stochastic gradient descent and deep neural networks, which is suitable for large-scale networks. Finally, the numerical results show that the proposed algorithms provide considerable performance compared to benchmark algorithms, and obtain a trade-off between network performance and calculation complexity.
To enhance the efficient content dissemination in terrestrial-satellite networks, we investigate the joint content placement and multi-hop delivery problem to reduce the average path length for content delivery. In the considered architecture, contents can be shared in multi-hop terrestrial networks while each content is offloaded to only one terrestrial user via the satellite. Considering both content popularity and channel conditions, content placement is performed by optimizing the
satellite downlink resource allocation where a distance-sensitive popularity parameter is introduced. Moreover, we formulate a path length minimization problem for content delivery in terrestrial networks, and a relay-aided multi-hop routing algorithm is proposed. Our simulation results show that the proposed scheme forcefully strikes a balance between terrestrial network backlogs and satellite downlink throughput, and achieves a better multihop delivery performance.
Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multiantenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network.