Related papers: LLM-Slice: Dedicated Wireless Network Slicing for …
Large language models (LLMs) have demonstrated remarkable success across various application domains, but their enormous sizes and computational demands pose significant challenges for deployment on resource-constrained edge devices. To…
Network slicing has been advocated by both academia and industry as a cost-efficient way to enable operators to provide networks on an as-a-service basis and meet the wide range of use cases that the fifth generation wireless network will…
This paper addresses joint admission control and per-user equipment (UE) bandwidth allocation to maximize weighted sum-rate in network slicing-enabled user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems when…
Through network slicing, different requirements of different applications and services can be met. These requirements can be in terms of latency, bandwidth, mobility support, defining service area, as well as security. Through fine and…
The expanding scale of neural networks poses a major challenge for distributed machine learning, particularly under limited communication resources. While split learning (SL) alleviates client computational burden by distributing model…
The fifth generation (5G) and beyond wireless networks are foreseen to operate in a fully automated manner, in order to fulfill the promise of ultra-short latency, meet the exponentially increasing resource requirements, and offer the…
Wireless industry nowadays is facing two major challenges: 1) how to support the vertical industry applications so that to expand the wireless industry market and 2) how to further enhance device capability and user experience. In this…
Network slicing allows 5G network operators to provide service to multiple tenants with diverging service requirements. This paper considers network slicing aware optimal resource allocation in terms of throughput and energy efficiency. We…
Large language models (LLMs) exhibit impressive capabilities across a wide range of tasks, yet the choice of which model to use often involves a trade-off between performance and cost. More powerful models, though effective, come with…
In the rapidly evolving landscape of digital twins (DT) and 6G networks, the integration of large language models (LLMs) presents a novel approach to network management. This paper explores the application of LLMs in managing 6G-empowered…
Network slicing is a key enabler for providing a differentiated service support to heterogeneous use cases and applications in 5G and beyond networks through creating multiple logical slices. Resource allocation for satisfying diverse…
Large language models (LLMs) have been a disruptive innovation in recent years, and they play a crucial role in our daily lives due to their ability to understand and generate human-like text. Their capabilities include natural language…
Effective network slicing requires an infrastructure/network provider to deal with the uncertain demand and real-time dynamics of network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g.,…
Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture…
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. While cloud platforms could provide the needed resources, data…
Training large language models (LLMs) at the network edge faces fundamental challenges arising from device resource constraints, severe data heterogeneity, and heightened privacy risks. To address these challenges, we propose ELSA…
The rapid evolution of network technologies and the growing complexity of network tasks necessitate a paradigm shift in how networks are designed, configured, and managed. With a wealth of knowledge and expertise, large language models…
While network slicing has become a prevalent approach to service differentiation, radio access network (RAN) slicing remains challenging due to the need of substantial adaptivity and flexibility to cope with the highly dynamic network…
Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband…
Fifth Generation (5G) networks are envisioned to be fully autonomous in accordance to the ETSI-defined Zero touch network and Service Management (ZSM) concept. To this end, purpose-specific Machine Learning (ML) models can be used to manage…