Related papers: Fast and Scalable Network Slicing by Integrating D…
The evolution of 5G wireless technology has revolutionized connectivity, enabling a diverse range of applications. Among these are critical use cases such as real time teleoperation, which demands ultra reliable low latency communications…
Networks should connect communicating peers, supporting vertical services requirements. The network evolution towards 6G requires native network slicing techniques. Some literature approaches claim network slice realization, but they do not…
Advances in network programmability enable operators to 'slice' the physical infrastructure into independent logical networks. By this approach, each network slice aims to accommodate the demands of increasingly diverse services. However,…
In the ensuing ultra-dense and diverse environment in future \ac{6G} communication networks, it will be critical to optimize network resources via mechanisms that recognize and cater to the diversity, density, and dynamicity of system…
Widespread deployment of relays can yield a significant boost in the throughput of forthcoming wireless networks. However, the optimal operation of large relay networks is still infeasible. This paper presents two approaches for the…
With network slicing in 5G networks, Mobile Network Operators can create various slices for Service Providers (SPs) to accommodate customized services. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on…
The emerging 5G technology needs to support simultaneously running incompatible service types on a common infrastructure. Network slicing is a solution that corresponds a slice of the network to each service type. Ensuring that user…
Enabling video-haptic radio resource slicing in the Tactile Internet requires a sophisticated strategy to meet the distinct requirements of video and haptic data, ensure their synchronized transmission, and address the stringent latency…
The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible…
Modern network architectures have shaped market segments, governments, and communities with intelligent and pervasive applications. Ongoing digital transformation through technologies such as softwarization, network slicing, and AI drives…
This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment…
Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in…
Network slicing aims to enhance flexibility and efficiency in next-generation wireless networks by allocating the right resources to meet the diverse requirements of various applications. Managing these slices with machine learning (ML)…
Logistics is a key economic sector where any optimization that reduces costs or improves service has a great impact on society at large. In this paper, the role of two 5G Network Slicing (NS) strategies in Smart Logistics is studied: the…
Neural network pruning is an important step in design process of efficient neural networks for edge devices with limited computational power. Pruning is a form of knowledge transfer from the weights of the original network to a smaller…
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…
This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. To ensure heterogeneous service requirements for different vehicles, we propose a network…
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 device-side model and a server-side model at a cut layer.…
Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously. However,…
The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here. In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands,…