查看更多>>摘要：Cooperative unmanned aerial vehicles (UAVs) cluster technology is considered a prospective solution for area coverage problems, enabling network access and emergency communications in remote areas. In this paper, we investigate how to control UAV cluster to achieve long-term and stable regional coverage while maintaining link connectivity and minimizing energy consumption, given the limited communication range and energy consumption of the UAVs themselves. To this end, we propose a cooperative UAV cluster strategy based on multi-agent deep reinforcement learning (MADRL) to achieve fair coverage of communication regions, which we call MADRL-based cooperative UAV cluster strategy (MADRL-CUCS). Our solution is a centralized training distributed execution architecture and defines a cluster structure for leader UAVs and follower UAVs. Under the premise of comprehensively considering the maximum coverage, we use a new energy efficiency function to minimize energy consumption, so as to extend the network lifetime of the UAVs cluster networks. The new fairness index and collision avoidance factor are used to ensure that the UAV cluster achieve effective and secure regional coverage. We adopt depth first search algorithm to check the link connectivity of the UAVs during the coverage process. Experiments show that the MADRL-CUCS algorithm outperforms the benchmark algorithm.
查看更多>>摘要：With advancement in digitalization and simulation technology, digital twin (DT) technology has emerged as a focal point of research in various industries. In response to the demands for high-quality and efficient wireless communication, it is crucial to conduct an in-depth study of core aspects and key technologies of digital twin technology. This will facilitate a better understanding and exploration of the future development direction of related digital simulation technology within the communication field. This paper provides a systematic summary and analysis of the concept of digital twins, their key technologies, and the current research landscape. Additionally, it explores the research and application fields, as well as the development prospects of digital twin technology in communications. The paper also examines diverse applications of digital twin technology in future 6th generation (6G) networks, including an end-to-end digital twin network architecture framework for non-terrestrial networks (NTNs) in the context of 6G. Finally, it discusses the challenges and opportunities for the widespread implementation of digital twins in future wireless communication networks.
查看更多>>摘要：In the field of wireless body area networks (WBANs), for solving the complex interference problem of inter-WBANs, a density-based adaptive optimization strategy (DAOS) is proposed in this paper. Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. We simulate the scenario on MATLAB simulator. Experimental results show that compared with the traditional scheme in high-density healthcare Internet of Things (IoT) scenarios, DAOS has obvious advantages compared with three comparison strategies of faster convergence rate of 48.94%, 60.76%, and 96.82%, and higher throughput of 5.60%, 8.08%, and 8.05% in traffic priorities 7 to 4.
查看更多>>摘要：Reconfigurable Intelligent Surfaces (RISs) have emerged as a pivotal technology for the Sixth-Generation (6G) communication system, showcasing the ability to configure wireless environment dynamically. Acknowledged as a breakthrough in enhancing network coverage, augmenting system capacity, and facilitating advanced applications such as Integrated Communication and Sensing (ISAC), RISs present a concrete approach to molding the future network evolution. The advancement of RIS technology necessitates a departure from idealistic assumptions and oversimplifications, compelling a progression towards models that more accurately reflect the physical attributes of hardware and the characteristics of propagation. In this paper, we delve into the practical constraints and limitations of current RIS design methodologies, conducting a comprehensive analysis based on the latest technological research advancements and product realizations. Our exploration is broad-ranging, encompassing the engineering challenges of single-point RISs, such as hardware impairments, intricacies of algorithm design, frequency spectrum-specific difficulties. A concentrated discourse is presented on novel near-field channel designs, the restrictions imposed by low-bit quantization, and the intricacies of amplitude-phase correlation constraints. This discussion aims to unearth the challenges, opportunities, and paradigmatic shifts induced by the practical deployment of RISs. The deployment challenges, networking dilemmas, simulation, and product evaluation is provided for RISs in large-scale networks from a broader system perspective. Furthermore, this paper highlights the critical need for accelerated efforts towards the commercialization of RISs. We explore the practical application revolution of RISs, encompassing engineering aspects and standardization processes. Our discussion aims to establish a foundational framework for introducing RISs into the market, acknowledging their significant potential as a game-changing technology in 6G communications.
Waqar A. AzizIacovos I. IoannouMarios LestasVasos Vassiliou...
82-101页
查看更多>>摘要：Traffic modeling and prediction are indispensable to future extensive data-driven automated intelligent cellular networks. It contributes to proactive and autonomic network control operations within cellular networks. Current methodologies typically rely on established prediction models designed for univariate and multivariate time series forecasting. However, these approaches often demand a substantial volume of training data and extensive computational resources for prediction model training. In this study, we introduce a dual-step transfer learning (DSTL)-based prediction model specifically designed for the prediction of multivariate spatio-temporal cellular traffic. This technique involves the categorization of gNodeBs (gNBs) into distinct clusters based on their traffic pattern correlations. Instead of training the prediction model individually on each gNB, a base model is trained on the aggregated dataset of all the gNBs within a base cluster using a combination of recurrent neural network (RNN) and bidirectional long-short term memory (RNN-BLSTM) network. In the first-step transfer learning (TL), the base model is provided to the gNBs within the base cluster and to the other clusters, where it undergoes the process of fine-tuning the intra-cluster aggregated dataset. Once the model is trained on the aggregated dataset within each cluster, it is provided to the gNBs within the respective cluster in the second-step TL. The model received by each gNB through the proposed DSTL technique either necessitates minimal fine-tuning or, in some cases, requires no further adjustment. We conduct extensive experiments on a real-world Telecom Italia cellular traffic dataset. The results demonstrate that the proposed DSTL-based prediction model achieves a mean absolute percentage error of 2.97%, 9.85%, and 9.73% in predicting spatio-temporal Internet, calling, and messaging traffic, respectively, while utilizing less computational resources and requiring less training time than traditional model training and TL techniques.
查看更多>>摘要：With the continuous progress of communication technology, traditional encryption algorithms cannot meet the demands of modern wireless communication security. Secure communication based on physical layer encryption emerges as a solution. To meet the low Key Disagreement Rate (KDR) and high Key Generation Rate (KGR) requirements for physical layer key generation, this paper proposes two quantization algorithms, Improve-CQG and Interpolate-CQG, based on the Channel Quantization with Guard band (CQG) algorithm. The former divides the characteristic quantization into two phases: threshold filtering and guard band quantization, while the latter adds a step after these two phases: interpolation quantization. Compared to the CQG algorithm, the Improve-CQG algorithm enhances the granularity of filtered quantization values. The core concept of the Interpolate-CQG algorithm is to utilize threshold filtering and the rounded-off quantization values from the guard band quantization phase. The symbol information corresponding to these index values is replaced by a new interpolated symbol and inserted into the key by the agreed quantized coordinates. Simulation proves that the Interpolate-CQG is an effective quantization algorithm for the key generation with lower KDR and higher KGR than the Improve-CQA and Improve-CQG.
NETL
NSTL