Related papers: SLA-Aware Distributed LLM Inference Across Device-…
Due to the high costs of optical fiber deployment in Low-Density and Rural Areas (LDRAs), 5G Fixed Wireless Access (5G FWA) recently emerged as an affordable solution. A widely adopted deployment scenario of 5G FWA includes edge cloud that…
Real-time energy forecasting on edge devices represents a major challenge for smart grid optimization and intelligent buildings. We present LAD-BNet (Lag-Aware Dual-Branch Network), an innovative neural architecture optimized for edge…
Emerging intelligent service scenarios in 6G communication impose stringent requirements for low latency, high reliability, and privacy preservation. Generative large language models (LLMs) are gradually becoming key enablers for the…
When output token counts can be predicted at submission time (Gan et al., 2026), client-side scheduling against a black-box LLM API becomes semi-clairvoyant: decisions condition on coarse token priors even though the provider's internals…
Connected robotics is one of the principal use cases driving the transition towards more intelligent and capable 6G mobile cellular networks. Replacing wired connections with highly reliable, high-throughput, and low-latency 5G/6G radio…
A system can satisfy accuracy-based validation, maintain output stability (Safety-Threshold Exceedance Rate, STER, equal to zero), and still violate timing constraints under deployment load. These are structurally independent properties…
To support on-device inference, the next-generation mobile networks are expected to support real-time model downloading services to mobile users. However, powerful AI models typically have large model sizes, resulting in excessive…
With the rapid advancement of artificial intelligence, generative artificial intelligence (GAI) has taken a leading role in transforming data processing methods. However, the high computational demands of GAI present challenges for devices…
Edge deployment of LLM agents on IoT hardware introduces attack surfaces absent from cloud-hosted orchestration. We present an empirical security analysis of three architectures (cloud-hosted, edge-local swarm, and hybrid) using a…
Physical Layer Authentication (PLA) exploits the spatial uniqueness of wireless channel characteristics in order to authenticate devices without recourse to higher-layer cryptographic protocols, which remain vulnerable to key compromise.…
Artificial Intelligence/Machine Learning (AI/ML) has become the most certain and prominent feature of 6G mobile networks. Unlike 5G, where AI/ML was not natively integrated but rather an add-on feature over existing architecture, 6G shall…
5G presents a unique set of challenges for cellular network architecture. The architecture needs to be versatile in order to handle a variety of use cases. While network slicing has been proposed as a way to provide such versatility, it is…
Fifth-generation (5G) systems are increasingly studied as shared communication and computing infrastructure for connected vehicles, roadside edge platforms, and future unmanned-system applications. Yet results from simulators, host-OS…
In this paper, we design a new smart softwaredefined radio access network (RAN) architecture with important properties like flexibility and traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a…
This paper presents the design, fabrication, and characterization of broadband liquid crystal (LC) reconfigurable intelligent surfaces (RIS) operating around 60 GHz and scaling up to 750 radiating elements. The RISs employ a delay line…
In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative…
A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The…
Adapting large AI models (LAMs) to personalized edge data is challenging because wireless devices have limited memory, computation, and uplink capacity. Federated fine-tuning preserves data privacy but still requires each device to host the…
The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming latency-sensitive…
Although the complete scope of the sixth generation of mobile technologies (6G) is still unclear, the prominence of the Internet of Things (IoT) and Artificial Intelligence (AI) / Machine Learning (ML) in the networking field is undeniable.…