Next-generation (NextG) networks will adopt cloud-based, disaggregated designs with open interfaces, thereby enabling tailored data-driven control. The open radio access network (ORAN) paradigm enhances RAN optimizati...
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Next-generation (NextG) networks will adopt cloud-based, disaggregated designs with open interfaces, thereby enabling tailored data-driven control. The open radio access network (ORAN) paradigm enhances RAN optimization by enabling intelligent and flexible network management. The ORAN Alliance standardizes open and virtualized architectures, supporting services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (uRLLC), and massive machine-type communication (mMTC). Traditionally, eMBB, uRLLC, and mMTC services are tied to specific use equipment (UE) optimized for one service. NextG networks, however, aim to enable multiple services on a single device, crucial for applications like the metaverse. To fill this, in this paper, an artificial intelligence (AI)driven efficient RAN management framework is proposed. This framework introduces the concept of the multi-service-modal UE (MSMU) system, which allows a single UE to handle both eMBB and uRLLC services. The proposed framework integrates traffic demand prediction, route optimization, RAN slicing, service identification, and radio resource management under uncertainty. The challenge of dynamic environments in such a system is addressed by decomposing the optimization problem into long-term (L-SP) and short-term (S-SP) subproblems. Using a long short-term memory (LSTM) model, the proposed approach allows the prediction of eMBB and uRLLC traffic demands and optimal routes for RAN slicing in the L-SP. For the S-SP, another LSTM model is employed to handle real-time service type identification and resource management based on long-term predictions. To support continuous adaptation, continual learning is incorporated into the S-SP framework, allowing the model to learn new service types while retaining prior knowledge. Experimental results show that the proposed framework efficiently manages dual-mode UEs, achieving low mean square error for traffic demand (0.003), resource block prediction
Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns. Instead, this paper considers a r...
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Graph Transformers (GTs) have demonstrated their advantages across a wide range of tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases, particularly biases related to structu...
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We derive a one-dimensional (1d) model for the analysis of bulging or necking in an inflated hyperelastic tube of finite wall thickness from the three-dimensional (3d) finite elasticity theory by applying the dimensio...
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Cyber Physical Social Intelligence (CPSI) integrates the social intelligence and cyber-physical systems, enabling machines to better interact and respond to human social behaviors. Under CPSI, the Internet of Vehicles...
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Cyber Physical Social Intelligence (CPSI) integrates the social intelligence and cyber-physical systems, enabling machines to better interact and respond to human social behaviors. Under CPSI, the Internet of Vehicles (IoV) has given rise to an increasing number of latency-sensitive services. Edge computing, as a distributed computing paradigm, enhances data processing capabilities, reduces data transmission latency, and minimizes bandwidth consumption. However, due to the limited resources of edge servers, striking a balance between service latency and deployment costs remains a highly challenging issue in the process of service deployment. In this paper, we propose a heterogeneous edge service deployment method for CPSI in IoV. Firstly, considering the heterogeneity of IoV services and edge servers, communication model, computational model, and heterogeneous service deployment cost model are constructed. Secondly, to maximize service deployment efficiency and minimize communication latency, a distance and workload-based edge server cluster division method is proposed. Subsequently, heterogeneous service deployment is performed in different clusters based on service category prioritization and minimal deployment quantity prioritization principles. Furthermore, an Analytic hierarchy process-based Heterogeneous edge Service dePloyment algorithm for CPSI in IoV, named AHSP, has been designed to determine optimal service deployment strategies. Finally, extensive numerical experimental results demonstrate the effectiveness of AHSP. IEEE
Compared to conventional cameras, the new type of vision camera-neuromorphic cameras, which can avoid motion blur and have the advantages of high spatiotemporal resolution, high dynamic range, low latency, etc. In thi...
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Streaming feature selection techniques have become essential in processing real-time data streams, as they facilitate the identification of the most relevant attributes from continuously updating information. Despite ...
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Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an...
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Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.
The procedure of computer-aided diagnosis for Alzheimer’s disease forecast has gotten better using deep learning principles. Such techniques rely on deep spatial features for classification. Nevertheless, the systems...
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Topic taxonomy discovery aims at uncovering topics of different abstraction levels and constructing hierarchical relations between them. Unfortunately, most of prior work can hardly model semantic scopes of words and ...
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