相关论文: Fundamental Limits on Delivery Time in Cloud- and …

Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected…

The processing, computation and memory requirements posed by emerging mobile broadband services require adaptive memory management and prefetching techniques at the mobile terminals for satisfactory application performance and sustained…

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…

Coded caching can create coded multicasting thus significantly accelerates content delivery in broadcast channels with receiver caches. While the original delivery scheme in coded caching multicasts each coded message sequentially, it is…

Delay Tolerant Networking (DTN) has been proposed as a new architecture to provide efficient store-carry-and-forward data transport in satellite networks. Since these networks relay on scheduled contact plans, the Contact Graph Routing…

This paper surveys and unifies a number of recent contributions that have collectively developed a metric for decentralized wireless network analysis known as transmission capacity. Although it is notoriously difficult to derive general…

The piling up storage and compute stacks in cloud data center are expected to accommodate the majority of internet traffic in the future. However, as the number of mobile devices significantly increases, getting massive data into and out of…

Cloud radio access network (CRAN), in which remote radio heads (RRHs) are deployed to serve users in a target area, and connected to a central processor (CP) via limited-capacity links termed the fronthaul, is a promising candidate for the…

Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work…

In the wake of growth in intelligent mobile devices and wide usage of bandwidth-hungry applications of mobile Internet, the demand of wireless data traffic and ubiquitous mobile broadband is rapidly increasing. On account of these…

Current Distributed Radio Access Networks~(D-RANs), which are characterized by a static configuration and deployment of Base Stations~(BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity…

Content Delivery Networks (CDNs) deliver a majority of the user-requested content on the Internet, including web pages, videos, and software downloads. A CDN server caches and serves the content requested by users. Designing caching…

This paper considers the uplink of a cloud radio access network (C-RAN) comprised of several multi-antenna remote radio units (RUs) which compress the signals that they receive from multiple mobile users (MUs) and forward them to a CU via…

This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach…

This paper presents a content-centric transmission design in a cloud radio access network (cloud RAN) by incorporating multicasting and caching. Users requesting a same content form a multicast group and are served by a same cluster of base…

Delay and Disruption Tolerant Networks (DTN) are critical for reliable communications in challenged network environments, particularly for space systems where end-to-end connectivity cannot be guaranteed. We present an open-source,…

6G networks are envisioned to support on-demand AI model downloading to accommodate diverse inference requirements of end users. By proactively caching models at edge nodes, users can retrieve the requested models with low latency for…

Artificial intelligence approaches for base-band processing for radio receivers have demonstrated significant performance gains. Most of the proposed methods are characterized by high compute and memory requirements, hindering their…

Edge computing is deemed a promising technique to execute latency-sensitive applications by offloading computation-intensive tasks to edge servers. Extensive research has been conducted in the field of end-device to edge server task…

Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. To mitigate this…