Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
Semantic communications for future internet: Fundamentals, applications, and challenges
With the increasing demand for intelligent services, the sixth-generation (6G) wireless
networks will shift from a traditional architecture that focuses solely on a high transmission …
networks will shift from a traditional architecture that focuses solely on a high transmission …
Holistic network virtualization and pervasive network intelligence for 6G
In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications
Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial
degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth …
degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth …
Split learning over wireless networks: Parallel design and resource management
Split learning (SL) is a collaborative learning framework, which can train an artificial
intelligence (AI) model between a device and an edge server by splitting the AI model into a …
intelligence (AI) model between a device and an edge server by splitting the AI model into a …
Heterogeneous computation and resource allocation for wireless powered federated edge learning systems
Federated learning (FL) is a popular edge learning approach that utilizes local data and
computing resources of network edge devices to train machine learning (ML) models while …
computing resources of network edge devices to train machine learning (ML) models while …
FRUIT: A blockchain-based efficient and privacy-preserving quality-aware incentive scheme
Incentive plays an important role in knowledge discovery, as it impels users to provide high-
quality knowledge. To promise incentive schemes with transparency, blockchain technology …
quality knowledge. To promise incentive schemes with transparency, blockchain technology …
DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks
In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …
Efficient federated learning with spike neural networks for traffic sign recognition
With the gradual popularization of self-driving, it is becoming increasingly important for
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …
Load-aware continuous-time optimization for multi-agent systems: Toward dynamic resource allocation and real-time adaptability
Q Wang, W Li, A Mohajer - Computer Networks, 2024 - Elsevier
In the realm of next-generation mobile communication networks, characterized by dynamic
and evolving workloads, the efficient resource allocation becomes paramount for achieving …
and evolving workloads, the efficient resource allocation becomes paramount for achieving …