Related papers: Energy Efficient Resource Allocation Optimization …

Fog networks offer computing resources with varying capacities at different distances from end users. A Fog Node (FN) closer to the network edge may have less powerful computing resources compared to the cloud, but processing of…

In this paper, we design an association scheme to maximize the sum energy efficiency for massive multiple-input and multiple-output (MIMO) enabled heterogeneous cellular networks (HCNs). Considering that the final formulated problem is in a…

In this paper, we investigate the problem of resource allocation for fluid antenna relay (FAR) system with antenna location optimization. In the considered model, each user transmits information to a base station (BS) with help of FAR. The…

Intelligent techniques are urged to achieve automatic allocation of the computing resource in Open Radio Access Network (O-RAN), to save computing resource, increase utilization rate of them and decrease the delay. However, the existing…

In this paper, we consider a UAV-assisted small-cell having heterogeneous users with different data rates and coverage demands. Specifically, we propose a novel utility-aware resource-allocation protocol to maximize the utility of UAV by…

Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology…

Federated Learning (FL) over wireless network enables data-conscious services by leveraging the ubiquitous intelligence at network edge for privacy-preserving model training. As the proliferation of context-aware services, the diversified…

In cellular wireless networks, user association refers to the problem of assigning mobile users to base station cells -- a critical, but challenging, problem in many emerging small cell and heterogeneous networks. This paper considers a…

6G facilitates deployment of Federated Learning (FL) in the Space-Air-Ground Integrated Network (SAGIN), yet FL confronts challenges such as resource constrained and unbalanced data distribution. To address these issues, this paper proposes…

With the explosive growth of smart devices and mobile data traffic, limited fronthaul capacity has become a notable bottleneck of green communication access networks, such as cloud radio access networks(C-RANs). In this paper, we proposed a…

Artificial intelligence-generated traffic is changing the shape of wireless networks. Specifically, as the amount of data generated to train machine learning models is massive, network resources must be carefully allocated to continue…

Fog computing is a promising computing paradigm for time-sensitive Internet of Things (IoT) applications. It helps to process data close to the users, in order to deliver faster processing outcomes than the Cloud; it also helps to reduce…

This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of…

This paper introduces a new resource allocation framework for integrated sensing and communication (ISAC) systems, which are expected to be fundamental aspects of sixth-generation networks. In particular, we develop an augmented Lagrangian…

Cloud-based Radio Access Network (C-RAN) is a promising architecture for future cellular networks, in which Baseband Units (BBUs) are placed at a centralized location, with capacity-constrained fronthaul connected to multiple distributed…

This paper proposes energy-efficient coordinated beamforming strategies for multi-cell multi-user multiple-input single-output system. We consider a practical power consumption model, where part of the consumed power depends on the base…

We develop a joint offloading and resource allocation framework for a multi-layer cooperative fog computing network, aiming to minimize the total energy consumption of multiple mobile devices subject to their service delay requirements. The…

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in…

Future sixth generation (6G) wireless communication networks face the need to similarly meet unprecedented quality of service (QoS) demands while also providing a larger energy efficiency (EE) to minimize their carbon footprint. Moreover,…

Next-generation mobile networks require evolved radio access network (RAN) architectures to meet the demands of high capacity, massive connectivity, reduced costs, and energy efficiency, and to realize communication with ultra-low latency…