Currently, LEO satellite networks enable global connectivity and emergency services, yet face dynamic topology changes and resource constraints. Traditional routing struggles with orbital dynamics and latency-sensitive demands. To address challenges related to data processing and transmission efficiency in dynamic satellite networks, this study proposes a hierarchical satellite routing architecture that decouples the roles of computing and communication satellites, enabling resource specialization and self-organized management. Additionally, an active edge caching mechanism is introduced, significantly reducing redundant data transmissions and processing delays across the network. Furthermore, the paper develops a deep reinforcement learning framework that integrates intelligent weight analysis to optimize packet routing decisions. Experimental results demonstrate that the proposed method achieves superior performance in terms of average delay and task completion rate compared to traditional approaches, with even greater advantages observed upon the integration of mobile edge computing.

This paper proposes Quantum Neural Networks (QNNs) as a data-driven approach for predicting fuel consumption. We utilize various layer architecture designs available in the Torchquantum framework, including both entangled and non-entangled circuit designs. In general, QNNs can achieve comparable Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with signifi-cantly fewer trainable parameters. Neither pure QNNs nor hybrid QNN models exhibit the underfitting tendencies seen in classical neural networks (CNNs). Notably, one of the most significant findings of this work is that hybridizing or "dressing" the quantum circuit leads to substantial improvements in RMSE and MAPE for pure QNNs. These promising results suggest potential optimizations for reducing emissions in green shipping.

Edge computing is near end users and provides them with fast computing services. Compared to the cloud, edge provides services with low communication delays but can only service limited users due to constrained storage and computing resources. Thus, allocating more computing resources to some jobs will block the execution of others, and the edge sends them either to the cloud or back to the users. This article focuses on minimizing the average system time by exploring blocked job scheduling (BJS) and redundant computing resource allocation (RCRA) in the cloud–edge–user system. Since edge nodes often operate in highly unpredictable environments, we adopt the replication redundancy to use the resources to shorten the job execution time. First, we propose an approximate model to evaluate the average system time theoretically. Second, we analyze the optimal scheduling for the blocked jobs and the optimal resource allocation for the redundant computing resources. Finally, we propose an algorithm combining BJS and RCRA. Simulation results show that the proposed model approximates the cloud–edge–user system well, and the combined algorithm significantly outperforms the other algorithms under different service time distributions.

This letter proposes a computing resource sharing strategy based on blocked job offloading in the low-Earth orbit (LEO) satellite network. The proposed strategy allows a satellite to share all or part of its computing resources with other satellites, and each satellite offloads or receives the blocked jobs from the adjacent satellites on the same meridian and latitude lines. Furthermore, we analyze the job execution probability, which evaluates the likelihood of the job being executed in the satellite network, for resource sharing strategies with different blocked job offloading hops. The numerical results validate the performance advantages of the computing resource sharing strategies and indicate a way to select the proper strategy.

In navigating the challenges of real-time semantic communication (SC) codec updates in the 6G-era non-terrestrial network (NTN)-assisted vehicular networks (NTN-VNs), a crucial component of intelligent transportation systems (ITS), this article introduces a novel approach inspired by human society. Facing complexities like 3-dimensional updating, network dynamism, and updating costs, NTN-VNs are treated as social networks. The proposed NTN-VN federated learning (NTN-VN-FL) framework asynchronously addresses challenges such as uplink and downlink SC codec updates, device decentralization, and asynchronous updating. By viewing device behaviors during updating as social behaviors with economic costs, an NTN-VN social management system ensures the proper functioning of the social network in the context of NTN-VN-FL. An economical social behavior selection mechanism, based on the reverse auction game for NTN-VN-FL, minimizes training delay and device energy costs, considering social relationships. The article also presents a two-stage Stackelberg game with the Vickrey auction rule to maximize social welfare in the auction. Simulation results highlight the superiority of NTN-VN-FL over existing potential application algorithms, effectively addressing the unique challenges of SC codec updating in NTN-VN. The efficacy of the social management system and social behavior selection mechanism is demonstrated in achieving optimal outcomes.

A recent study showcased the efficacy of Long Short-Term Memory (LSTM) in significantly reducing average indoor localization Root Mean Square Error (RMSE). Motivated by the superior performance of quantum algorithms, we explore Quantum LSTM (QLSTM) for indoor localization, leveraging a variational quantum circuit (VQC). QLSTM benefits from diverse gate sequences and increased variational parameters, enhancing learning capabilities. As QLSTM is a relatively recent concept, it is essential to conduct a comprehensive investigation into the impact of hyperparameters, including learning rate, the quantity of hidden layers, and the number of quantum neurons, to ascertain their influence on achieving the necessary RMSE during the training process. The results show that QLSTM is highly sensitive to the choice of optimizer and is capable of producing comparable low RMSE values with significantly fewer neurons than classical LSTM. In a scenario where a two-hidden-layer LSTM architecture is utilized, featuring 35 neurons in each layer, 6 input features, and generating 2 outputs, the LSTM configuration has a total of 15,892 parameters. In contrast, the QLSTM configuration is more streamlined, with only 7,562 parameters. Additionally, it is noteworthy that the RMSE for QLSTM is comparable to its classical counterpart, standing at 0.895 as opposed to 0.8705.

Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.

The rapid emergence of 5G technology brings new cybersecurity challenges that hold significant implications for our economy, society, and environment. Among these challenges, ensuring the effectiveness of Intrusion Detection Mechanisms (IDMs) in monitoring networks and detecting 5G-related cyberattacks is of utmost importance. However, optimizing cybersecurity levels and selecting appropriate IDMs remain as critical and ongoing challenges. This work considers multiple pre-deployed distributed Security Agents (SAs) across the network, each capable of running various IDMs, where they differ by their effectiveness in detecting the attacks (referred to as security term) and the consumption of resources (referred to as Quality of Service (QoS) costs). We formulate a joint security and QoS utility function leveraging the Cobb–Douglas production utility function. There are several parameters that impact the joint objective problem, including the set of elasticity parameters, that reflect the importance of the two objectives. We derive an optimal set of elasticity parameters in closed form to identify the balancing point where both objectives have equal utility values. Through comprehensive simulations, we demonstrate that increasing the detection level of SAs enhances the security utility while simultaneously diminishing the QoS utility, as more computational, bandwidth, and monetary resources are utilized for IDM processing. After optimization, our mechanism can strike an effective balance between cybersecurity and QoS overhead while demonstrating the importance of different parameters in the joint problem.

The advent of 5G technology introduces new - and potentially undiscovered - cybersecurity challenges, with unforeseen impacts on our economy, society, and environment. Interestingly, Intrusion Detection Mechanisms (IDMs) can provide the necessary network monitoring to ensure - to a big extent - the detection of 5G-related cyberattacks. Yet, how to realize the attack surface of 5G networks with respect to the detected risks, and, consequently, how to optimize the cybersecurity levels of the network, remains an open critical challenge. In respect, this work focuses on deploying multiple distributed Security Agents (SAs) that can run different IDMs over various network components and proposes a cybersecurity mechanism for optimizing the network’s attack surface with respect to the Quality of Service (QoS). The proposed approach relies on a new closed-form utility function to describe the trade-off between cybersecurity and QoS and uses multi-objective optimization to improve the selection of each SA detection level. We demonstrate via simulations that before optimization, an increase in the detection level of SAs brings a direct decrease in QoS as more computational, bandwidth and monetary resources are utilized for IDM processing. Thereby, after optimization, we demonstrate that our mechanism can strike a balance between cybersecurity and QoS while showcasing the impact of the importance of different objectives of the joint optimization.

Abstract: Security, Trust and Reliability are crucial issues in mobile 5G networks from both hardware and software perspectives. These issues are of significant importance when considering implementations over distributed environments, i.e., corporate Cloud environment over massively virtualized infrastructures as envisioned in the 5G service provision paradigm. The SANCUS1 solution intends providing a modular framework integrating different engines in order to enable next‐generation 5G system networks to perform automated and intelligent analysis of their firmware images at massive scale, as well as the validation of applications and services. SANCUS also proposes a proactive risk assessment of network applications and services by means of maximising the overall system resilience in terms of security, privacy and reliability. This paper presents an overview of the SANCUS architecture in its current release as well as the pilots use cases that will be demonstrated at the end of the project and used for validating the concepts.

To improve data speed and reliability, hybrid wireless networks combine two different Radio Access Technologies (RATs), such as Visible Light Communications (VLC), millimetre wave (mmWave), Wireless Fidelity (WiFi), 4G Long Term Evolution (LTE), etc. The Internet of Radio Light (IoRL) is a cutting-edge system paradigm to combine three RATs for taking advantage the vast VLC and mmWave spectrum with the ubiquitous coverage of WiFi. In this respect, this work introduces a new convex optimisation-based solution method to optimise the three-dimensional (3D) Access Point Assignment (APA) problem of the IoRL system under individual user positioning, priority and minimum Quality-of-Service (QoS) constraints. We use both the IoRL real-world testbed and large-scale Maltab simulations to evaluate that our solution converges in linear time, and attains higher throughput-vs-fairness trade-off than existing efforts.

The development of modellings and analytical tools to structurise and study the allocation of resources through noble user competitions become essential, especially considering the increased degree of heterogeneity in application and service demands that will be cornerstone in future communication systems. Stochastic asymmetric Blotto games appear promising to modelling such problems, and devising their Nash equilibrium (NE) strategies by anticipating the potential outcomes of user competitions. In this regard, this paper approaches the generic energy efficiency problem with a new stochastic asymmetric Blotto game paradigm to enable the derivation of joint optimal bandwidth and transmit power allocations by setting multiple users to compete in multiple auction-like contests for their individual resource demands. The proposed modelling innovates by abstracting the notion of fairness from centrally-imposed to distributed-competitive, where each user's pay-off probability is expressed as quantitative bidding metric, so as, all users' actions can be interdependent, i.e., each user attains its utility given the allocations of other users, which eliminates the chance of low-valued carriers not being claimed by any user, and, in principle, enables the full utilisation of wireless resources. We also contribute by resolving the allocation problem with low complexity using new mathematical techniques based on Charnes-Cooper transformation, which eliminate the additional coefficients and multipliers that typically appear during optimisation analysis, and derive the joint optimal strategy as a set of linear single-variable functions for each user. We prove that our strategy converges towards a unique, monotonous and scalable NE, and examine its optimality, positivity and feasibility properties in detail. Simulation comparisons with relevant studies confirm the superiority of our approach in terms of higher energy efficiency performance, fairness index and quality-of-service provision.

Modern applications involve green communication technologies motivating well optimization in the power-limited regime. In comparison with most of the existing related work that assumes perfect channel state information (CSI) is always available, which is unfortunately not true in reality, this paper focuses on an optimal energy-efficient solution for resource allocation in multiuser orthogonal frequency division multiple access networks in the presence of imperfect CSI and data outage conditions. In particular, in view that wireless channel conditions, circuit power consumptions, and users' quality-of-service (QoS) requirements are heterogeneous in nature, we enable attractive tuning options by letting energy efficiency optimization objective to assign weights to each allocation link. In addition, we interpret the effects of data outage due to imperfect CSI using a profound insight on the monotonicity of noncentral chi-squared inverse distribution function, which reveals that our design complies with expected physics and mechanics of conventional energy efficiency approach and that it can be successfully degenerated to the energy-efficiency model with perfect CSI. Furthermore, we formulate a mixed combinatorial problem toward maximizing the energy efficiency subject to a minimum QoS requirement, channel interference, and transmitting power constraints. The problem is transformed into an equivalent quasi-concave problem with respect to power, and concave problem with respect to the subcarrier indexing coefficients using the concept of subcarrier time sharing. We optimize through a simple and versatile methodology, which uses standard-Lagrangian optimization technique to obtain joint dynamic subcarrier and adaptive power allocations by means of final formulas. We also examine key properties of the introduced optimal solution in terms of implementation convergence and complexity, level of optimality, and impact of imperfect CSI coefficients and circuit power on network performance. The simulation results demonstrate the effectiveness of our allocation scheme for achieving higher energy efficiency performance with the guaranteed QoS support and lower complexity than the existing approaches especially when perfect CSI is not available.

This paper proposes a new Nash bargaining solution (NBS) based cooperative game-theoretic scheduling framework for joint channel and power allocation in orthogonal frequency division multiple access cognitive radio (CR) systems. Our objectives are to maximize the overall throughput of the CR system with the protection of primary users' transmission, while guaranteeing each CR user's minimum rate requirement and the proportional fairness and efficient power distribution among CR users. Using time-sharing variable transformation, we introduce a novel method that involves Lambert-W function properties and obtain closed-form analytical solutions. A low-complexity algorithm is also developed which does not require iterative processes as usual to search the optimal solution numerically. Simulation results demonstrate that our optimal policies outperform the existing maximal rate, fixed assignment and max-min fairness, while achieving the 99.985% in average of the optimal capacity.

This paper proposes a novel game-theoretic cross-layer design for orthogonal frequency division multiple access (OFDMA) wireless networks, which operates optimal subcarrier, power and rate allocation. Based on the Nash bargaining solution (NBS) and coalitions, the proposed scheme not only maximizes the system's effective data rate but also supports proportional fairness among the users by considering the heterogeneity of their requirements, as well as the rate outage due to imperfect channel state information (CSI) available at the transmitter (CSIT). The simulation results confirm that the proposed scheme achieves an optimum tradeoff between effective data rate and proportional fairness, while it guarantees the quality of service (QoS) requirements, and outperforms the existing solutions in terms of power consumption, resilience to CSIT errors and stability.

In this paper we investigate the potential benefits of a selective delayed channel access algorithm (SDCA) for the future IEEE 802.11n based high-throughput networks. The proposed solution aims to resolve the poor channel utilization and the low efficiency that EDCA's high priority stations adhere due to shorter waiting times and consequently to the network's degrading overall end performance. The algorithm functions at the MAC level where it delays the packets from being transmitted by postponing the channel access request, based on their traffic characteristics. As a result, the flow's average aggregate size increases and consequently so is the channel efficiency. However, in some situations we notice that further deferring has a negative impact with TCP applications, thus we further introduce a traffic awareness feature that allows the algorithm to distinguish which flows are using the TCP protocol and override any additional MAC delay. We validate through various simulations that SDCA improves throughput significantly and maximizes channel utilization.

This paper proposes a novel cross-layer scheduling scheme for a single-cell orthogonal frequency division multiple access (OFDMA) wireless system with partial channel state information (CSI) at transmitter (CSIT) and heterogeneous user delay requirements. Previous research efforts on OFDMA resource allocation are typically based on the availability of perfect CSI or imperfect CSI but with small error variance. Either case consists to typify a non tangible system as the potential facts of channel feedback delay or large channel estimation errors have not been considered. Thus, to attain a more realistic resolution our cross-layer design determines optimal subcarrier and power allocation policies based on partial CSIT and individual user's quality of service (QoS) requirements. The simulation results show that the proposed cross-layer scheduler can maximize the system's throughput and at the same time satisfy heterogeneous delay requirements of various users with significant low power consumption.