This paper aims to tackle the complex challenge of channel assignment and joint power-energy allocation within a cellular-vehicle-to-everything (C-V2X) network, which is deployed to manage vehicular dynamics at an urban traffic intersection. The primary function of the C-V2X network is to facilitate the coordination of multiple vehicle platoons formed by closely spaced same-lane vehicles. This coordination involves two critical communication tasks including the timely update of platoon states to a roadside unit (RSU) and the reliable exchange of cooperative awareness messages (CAMs) among vehicles within the same platoon. The main objective of this paper is to minimise the average age of information (AoI) to ensure the timely update between vehicle platoons and the RSU, maximise the CAM delivery probability (CDP) to guarantee the successful exchange of CAMs among vehicles, and promote sustainable, green communication practices through the implementation of our optimal power-energy management strategies. Recognising the intricate and dynamic nature of this challenge, we adopt a multi-agent deep reinforcement learning (MADRL) approach based on the Markov decision process (MDP). Two innovative algorithms based on the multi-agent deep deterministic policy gradient (MADDPG) and twin delayed deep deterministic policy gradient (TD3) algorithms are proposed to address this optimisation problem effectively. Finally, comprehensive simulation results are presented, which demonstrate the remarkable performance of our proposed schemes, particularly in terms of energy efficiency when compared to existing research. Importantly, these gains in energy efficiency are achieved while maintaining competitive algorithm convergence speeds, low AoI levels, and high CDP, showcasing the practical viability of the developed methods.

This paper investigates the integration of drone (aka UAV)-assisted networks with a reconfigurable intelligent surface (RIS) to enhance energy efficiency in near-and far-field communication scenarios. The coexistence of near-field and far-field communications introduces unique challenges in ensuring efficient resource allocation, managing interference, and meeting quality of service requirements for users. Primary users in the near-field areas have stronger signal links, while secondary users and primary far-field users face increased path loss and interference, necessitating sophisticated optimisation strategies to balance their performance. To address these challenges, we propose a joint optimisation framework for transmission power allocation and RIS phase-shift design. The framework aims to maximise energy efficiency while maintaining reliable communication for all user groups, leveraging the complementary characteristics of UAV and RIS technologies. The low-complexity optimisation approach is developed, leveraging advanced successive convex approximation techniques and iterative algorithms. The framework consists of the Dinkelbach algorithm for the outer loop and a combination of linear and convex optimisation algorithms for the inner loop. Linear programming is employed to handle the large number of variables, such as phase-reflecting coefficients, while convex programming is used to optimise power allocation in UAVs, with convergence guaranteed. Simulation results reveal significant energy efficiency gains compared to baseline methods, demonstrating the effectiveness of the proposed framework in managing the coexistence of near-and far-field communications. The findings underscore the importance of energy-efficient design in enabling scalable and sustainable UAV-assisted networks, offering valuable insights for the development of high-performance next-generation communication systems.

Maritime transportation is essential for global trade but presents significant environmental challenges due to its greenhouse gas emissions. Existing studies have addressed these challenges through integrated routing and speed optimization frameworks, yet frequently lack explicit quantification of environmental impacts and exhibit limited scalability for large-scale ship routing operations. Conversely, existing quantum optimization research in vehicle routing predominantly targets land-based transportation scenarios, restricting its direct applicability to maritime logistics. Maritime logistics inherently involve distinct operational complexities, such as nonlinear interactions among speed, payload, fuel consumption, and numerous operational uncertainties. These combined limitations underscore the critical need for quantum optimization methods explicitly designed for green maritime supply chains. To bridge this gap, this paper proposes an efficient quantum-centric optimization framework that uses the quantum approximate optimization algorithm (QAOA) to jointly optimize ship routing and speed management within sustainable maritime supply chains. Specifically, we formulate an NP-hard cost minimization problem integrating critical maritime parameters, including fuel consumption, payload constraints, and operational speeds. We further develop a hybrid quantum-classical alternating optimization approach that iteratively addresses routing decisions through quantum computing techniques and optimizes ship speed using an analytical solution. Simulation results and real quantum hardware experiments demonstrate that our quantum-centric methodology achieves substantial cost reductions and highlights the potential for practical applicability in realistic maritime operations, significantly outperforming classical optimization benchmarks.

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones.

In this paper, we propose a joint large-scale resource allocation and optimal multibeam design for satellite-enabled Internet-of-Things (IoT) networks. To overcome the long latency issue in satellite communications, a new gaming optimisation framework is proposed, which is solved in real-time scenario. Firstly, IoT devices are clustered using coalition game that is designed for considering simultaneously transmission time minimisation and channel gain maximisation. Then, bisection search which is very low-complexity procedure is used for maximising the network energy efficiency in closed-form power allocation. The numerical results prove that our method outperforms conventional approaches and is applicable to large-scale networks with real-time IoT scenario.

In this article, we consider a new unmanned ariel vehicles (UAV)-aided intelligent wireless sensing scheme, where the UAVs are deployed for smart sensing and collecting data from Internet-of-Things (IoT) devices. In particular, we propose optimal UAVs’ path planing approaches for minimizing the completion time and total energy consumption of the UAVs’ deployment for data collection. Two optimal schemes, namely, optimal energy consumption by peer-to-peer UAV-IoT sensing networks and optimal energy consumption by clustering UAV-IoT sensing networks, are considered. The low-complexity procedures of our advanced optimization techniques are suitably applied to disaster relief networks when the solving time must be strictly adhered to. Our real-time optimization algorithms result in low computational complexity with fast deployment and low processing time for solving the problem of tracking and gathering sensor data, i.e., in very short time (milliseconds). Through simulations results we demonstrate that our proposed approaches in UAV-aided intelligent IoT wireless sensing are suitable for time-critical mission applications such as emergency communications, public safety, and disaster relief networks.

In this paper, we propose to use multiple mobile robots as relay terminals to assist the wireless connectivity between the base stations and industrial Internet-of-Things (IIoT) devices. Under the strict latency constraint via short blocklength, we propose an optimal resource allocation scheme to minimise the error probability at the IIoT devices. For fast deployment, we propose a deep neural network to optimise the positions of the mobile robots. Then, a joint blocklength and power allocation optimisation of the base stations and relay robots is considered. Due to non-convexity of such optimization problem, we propose a sub-problem with an effective iterative algorithm for solving the reliability maximisation. Representative numerical results are provided to demonstrate the advantages of our proposed scheme over the conventional approach.

In this work, we consider a joint optimisation of real-time deployment and resource allocation scheme for UAV-aided relay systems in emergency scenarios such as disaster relief and public safety missions. In particular, to recover the network within a disaster area, we propose a fast K-means-based user clustering model and jointly optimal power and time transferring allocation which can be applied in the real system by using UAVs as flying base stations for real-time recovering and maintaining network connectivity during and after disasters. Under the stringent QoS constraints, we then provide centralised and distributed models to maximise the energy efficiency of the considered network. Numerical results are provided to illustrate the effectiveness of the proposed computational approaches in terms of network energy efficiency and execution time for solving the resource allocation problem in real-time scenarios. We demonstrate that our proposed algorithm outperforms other benchmark schemes.

The capability of massive multiple-input multiple-output (mMIMO) systems supporting the throughput requirement of as many users as possible is investigated. The bottleneck of serving small numbers of users by a large number of transmit antennas in conventional mMIMO is unblocked by a new time-fraction-wise beamforming technique, which focuses signal transmission in fractions of a time slot. Based on this time-fraction-wise signal transmission, a new user service scheduling scheme for multi-cell mMIMO, whose cell-edge users suffer not only poor channel conditions but also multi-cell interference, is proposed to support a large user-population. We demonstrate that the numbers of users served by our multi-cell mMIMO within a time-slot may be as high as twice the number of its transmit antennas.

In this article, we address the social-awareness property and unmanned-aerial-vehicle (UAV)-assisted information diffusion in emergency scenarios, where UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with the worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertices, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties, including global stability, convergence, and complexity. Finally, simulation results show that our proposed approach can achieve a better tradeoff among the information diffusion speed, channel switch cost, and complexity.

This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

As the Internet of Things (IoT) is maturing and acquires its social flavor, the Social IoT enables smart devices to build inter-thing social networks without human intervention. As a new form of smart devices, unmanned aerial vehicles (UAVs) are finding their way into IoT applications. The integrated Social Internet of Flying Things (SIoFT) can provide the social-aware UAV-assisted services. However, the broadcast nature of air-to-ground (A2G) channels makes them vulnerable to being eavesdropped by terrestrial malicious users due to their strong line-of-sight (LoS) links. In this paper, we investigate to ensure the security of A2G communications when the location information of multiple potential eavesdroppers cannot be perfectly estimated. Following the “no pain no gain” principle, the terrestrial users who reuse the UAV cellular spectrum will act as friendly jammers to realize “win-win” situation. Hence, joint trajectory design, power control, and channel allocation optimization problem is formulated to maximize the average secrecy rate of UAVs in worst case. In the first stage, we utilize the block coordinate descent method and successive convex optimization method to solve the trajectory design and power control problems in an iterative manner. In the second stage, we convert the user pairing problem into a popular matching problem with externalities. Two distributed algorithms are proposed to maintain the popular matching under dynamics. Moreover, we conduct detailed analysis of the popularity, convergence, and computational complexity. Simulation results demonstrate the superiority of our proposed method in terms of different performance metrics.

In this work, we propose efficient optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real- time optimisation in embedded UAV-CRN communication involved in recovering wireless communication services. Particularly, by conceiving advanced optimisation techniques and training deep neural networks, our solutions become capable of supporting real-time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real- time optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.

In this paper, the secrecy performance of a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network is studied in the presence of a multiple-antenna eavesdropper. The ergodic secrecy rates for the downlink transmission in the considered system are derived to provide important insights. Then, by using these results, a joint power allocation scheme is proposed for both uplink training and downlink data transmission phases to maximize the sum ergodic secrecy rates. Because the utility function of interest is non-concave and the involved constraints are non-convex, a new iterative algorithm is proposed, which can find at least a local optimum. The obtained results reveal that the secrecy performance of NOMA networks benefits from deploying massive MIMO techniques. They also indicate that the proposed optimization algorithm enhances the secrecy performance of the considered system.

In this work, we propose efficient optimisation methods for embedded relay-assisted unmanned ariel vehicles (UAVs) in wireless sensor networks (WSNs) to cope with the hazardous effect of natural disaster. Particularly, by using advanced optimisation techniques, our low-complexity procedures are suitable applied to internet-of-things (IoT) applications when the execution time is strictly governed in disaster scenarios. Our model considers real-time optimisation in embedded UAV-WSN communication for tracking and gathering sensor data. Our algorithms are low computational complexity with fast deployment and low execution time for solving our problem in milliseconds. Numerical results are shown to demonstrate the benefit of our proposed approaches for UAV-WSN.

This paper considers a decode-and-forward (DF) multi-way massive multiple-input multiple-output (MIMO) relay system where many users exchange their data with the aid of a relay station equipped with a massive antenna array. We propose a new transmission protocol which leverages successive cancelation decoding and zero-forcing (ZF) at the users. By using properties of massive MIMO, a tight analytical approximation of the spectral efficiency is derived. We show that our proposed scheme uses only half of the time-slots required in the conventional scheme (in which the number of time-slots is equal to the number of users [1]), to exchange data across different users. As a result, the sum spectral efficiency of our proposed scheme is nearly double the one of the conventional scheme, thereby boosting the performance of multi-way massive MIMO to unprecedented levels. To improve the network energy efficiency, we also propose a power allocation scheme which maximizes the energy efficiency under a given peak power constraint at each user and the relay.

In this study, the secrecy performance of cooperative half‐duplex cognitive relay networks (CRNs) comprised of multiple primary users (PUs) and multiple eavesdroppers under the effect of unreliable wireless backhauls is studied. The authors consider relay networks with single relay assisting multiple sources under power constraints at PUs and secondary transmitters, and based on the availability of channel‐state information at the sources, they propose two best source selection schemes, namely: (i) optimal source selection (OSS) and (ii) sub‐optimal source selection (SoSS). For both scenarios, they obtain exact closed‐form and asymptotic expressions for the system's outage probability and carry out numerical simulations to justify their analyses. From the results, source selection is proven to be capable of counteracting the negative impact of unreliable backhaul on CRNs. The secrecy performance limitations are also verified to be influenced by the backhaul reliability. Furthermore, OSS is more desirable compared to SoSS in low signal‐to‐noise ratio (SNR) regime, while the secrecy performance for both schemes converge to an asymptotic limit in high‐SNR ranges.

We consider a multi-way decode-and-forward relaying network with very large antenna arrays at the relay station. In this system, each user and the relay operate in half-duplex and time-division duplexing modes. To exchange information among all users, we propose a new transmission protocol which combines massive multiple-input multiple-output technology with linear processing, self-interference cancelation, and successive cancelation decoding. Our proposed transmission protocol reduces the number of time-slots for data exchange among users by approximately 2 times, compared with the conventional data transmission protocol. For this new topology, we derive a very tight approximation of the spectral efficiency in closed-form assuming perfect channel state information (CSI). Then, a CSI acquisition method at the relay and the users is provided and analyzed. We show via numerical simulations, that the performance gap between imperfect and perfect CSI cases is small. The closed-form expression of the spectral efficiency enables us to design two power allocation schemes. In the first power allocation scheme, we choose the transmit powers at the users and the relay to maximize the sum spectral efficiency, subject to a given quality-of-service requirement for each user. In the second power allocation scheme, the objective is the energy efficiency taking into account the hardware power consumption. Both power allocation schemes can be efficiently executed by iteratively solving a sequence of convex problems. Numerical results verify the effectiveness of the proposed transmission protocol and the power allocation schemes compared with the state of the art.

A heterogeneous network (HetNet) of a macrocell base station equipped with a large-scale massive multi-in multi-out (MIMO) antenna array overlaying a number of small cell base stations (small cells) can provide high quality of service (QoS) to multiple users under low transmit power budget. However, the circuit power for operating such a network, which is proportional to the number of transmit antennas, poses a problem in terms of its energy efficiency (EE). This paper addresses the beamforming design at the base stations to optimize the network EE under the QoS constraints and a transmit power budget. Beamforming tailored for weak, strong, and medium cross-tier interference HetNets is proposed. In contrast to the conventional transmit strategy for power efficiency in meeting the users' QoS requirements, which suggest the use of a few hundred antennas, it is found out that the overall network EE quickly drops if this number exceeds 50. It is found that, for a given number of antennas, HetNet is more energy efficient than massive MIMO when considering the overall energy consumption.