The regenerative capabilities of next-generation satellite systems offer a novel approach to design low earth orbit (LEO) satellite communication systems, enabling full flexibility in bandwidth and spot beam management, power control, and onboard data processing. These advancements allow the implementation of intelligent spatial multiplexing techniques, addressing the ever-increasing demand for future broadband data traffic. Existing satellite resource management solutions, however, do not fully exploit these capabilities. To address this issue, a novel framework called flexible resource management algorithm for LEO satellites (FLARE-LEO) is proposed to jointly design bandwidth, power, and spot beam coverage optimized for the geographic distribution of users. It incorporates multi-spot beam multicasting, spatial multiplexing, caching, and handover (HO). In particular, the spot beam coverage is optimized by using the unsupervised K-means algorithm applied to the realistic geographical user demands, followed by a proposed successive convex approximation (SCA)-based iterative algorithm for optimizing the radio resources. Furthermore, we propose two joint transmission architectures during the HO period, which jointly estimate the downlink channel state information (CSI) using deep learning and optimize the transmit power of the LEOs involved in the HO process to improve the overall system throughput. Simulations demonstrate superior performance in terms of delivery time reduction of the proposed algorithm over the existing solutions.

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

Proactive caching of the most popular contents in the cache memory of fog-access points (F-APs) is regarded as a promising solution for the 5G and beyond cellular communication to address latency-related issues caused by the unprecedented demand of multimedia data traffic. However, it is still challenging to correctly predict the user’s content and store it in the cache memory of the F-APs efficiently as the user preference is dynamic. In this article, to solve this issue to some extent, the deep learning-based content caching (DLCC) method is proposed due to recent advances in deep learning. In DLCC, a 2D CNN-based method is exploited to formulate the caching model. The simulation results in terms of deep learning (DL) accuracy, mean square error (MSE), the cache hit ratio, and the overall system delay is displayed to show that the proposed method outperforms the performance of known DL-based caching strategies, as well as transfer learning-based cooperative caching (LECC) strategy, randomized replacement (RR), and the Zipf’s probability distribution.

Traffic congestion is a significant problem that adversely affects the economy, environment, and public health in urban areas worldwide. One promising solution is to forecast road-level congestion levels in the short-term and long-term, enabling commuters to avoid congested areas and allowing traffic agencies to take appropriate action. In this study, we propose a hybrid deep neural network algorithm based on High-Resolution Network (HRNet) and ConvLSTM decoder for 10, 30, and 60-min traffic congestion prediction. Our model utilizes the HRNet’s multi-scale feature extraction capability to capture rich spatial features from a sequence of past traffic input images. The ConvLSTM module learns temporal information from each HRNet multi-scale output and aggregates all feature maps to generate accurate traffic forecasts. Our experiments demonstrate that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion and outperforms four other state-of-the-art architectures (PredNet, UNet, ConvLSTM, and Autoencoder) in terms of accuracy, precision, and recall. A case study was conducted on the dataset from Seoul, South Korea.

In recent years, the governments in many countries have recognized the importance of data in boosting their economies. As a result, they are implementing the philosophy of open government data (OGD) to make public data easily and freely available to everyone in standardized formats. Because good quality OGD can boost a country’s economy, whereas poor quality can jeopardize its efficient use and reuse, it is very important to maintain the quality of data stored in open government data portals (OGDP). However, most OGDPs do not have a feature that indicates the quality of the data stored there, and even if they do, they do not provide real-time service. Moreover, most recent studies focused on developing approaches to quantify the quality of OGD, either qualitatively or quantitatively, but did not offer an approach to automatically calculate and visualize it in real-time. To address this problem to some extent, this paper proposes a framework that can automatically assess the quality of data in the form of a data completeness ratio (DCR) and visualize it in real-time. The framework is validated using the OGD of South Korea, whose DCR is displayed in real-time using the Django-based dashboard.

Mega-constellations of low Earth orbit (LEO) satellites are envisioned to deliver global broadband and direct-to-cell services, requiring seamless handovers (HOs) to ensure uninterrupted connectivity. Conventional break-before-make HO protocols, although supported by inter-satellite links, suffer from beamforming delays due to the high orbital velocities of LEO satellites. To address these limitations, we propose an integrated sensing and communication (ISAC)-assisted HO (ISAC-HO) protocol that enables make-before-break HOs via ISAC-enabled ground terminals (GTs). A novel ISAC design capable of generating a three-dimensional (3D) beampattern under realistic conditions allows GTs to sense approaching LEO satellites without significantly compromising communication with the currently serving LEO satellite. The design problem is highly challenging due to its non-convexity and mixed-integer nature. To tackle these challenges, we propose an approach that leverages Riemannian manifold optimization and closed-form solutions. For multi-GT scenarios, we introduce a multi-agent deep reinforcement learning framework that mitigates sensing collisions and ensures quality-of-service under shared spectrum constraints. Numerical results confirm that the proposed ISAC design significantly improves the communication–sensing trade-off, enables smooth HO, and remains robust in the presence of imperfect channel state information.