Advanced Wireless Network Optimization
251 papers on this topic
Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks
Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals. However, the...
TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning
The selection of base station sites is a critical challenge in 5G network planning, which requires efficient optimization of coverage, cost, user satisfaction, and practical constraints....
Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional...
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for...
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks
The rapid development of Industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower...
Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting
For the 6G mobile networks, in-situ model downloading has emerged as an important use case to enable real-time adaptive artificial intelligence on edge devices. However, the simultaneous...
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution....
Toward a 6G AI-Native Air Interface
Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Massive...
PDU-set Scheduling Algorithm for XR Traffic in Multi-Service 5G-Advanced Networks
In this paper, we investigate a dynamic packet scheduling algorithm designed to enhance the eXtended Reality (XR) capacity of fifth-generation (5G)-Advanced networks with multiple cells, multiple...
Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication
In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate...
Knowledge Transfer and Reuse: A Case Study of AI-enabled Resource Management in RAN Slicing
An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promising solutions....
Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dimensional...
Multi-Cell Mobile Edge Computing: Joint Service Migration and Resource Allocation
Mobile-edge computing (MEC) enhances the capacities and features of mobile devices by offloading computation-intensive tasks over wireless networks to edge servers. One challenge faced by the...
Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning
Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated...
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for...
Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN
The increasing complexity of modern applications demands wireless networks capable of real time adaptability and efficient resource management. The Open Radio Access Network (O-RAN) architecture,...
On the Prediction of Wi-Fi Performance through Deep Learning
Ensuring reliable and predictable communications is one of the main goals in modern industrial systems that rely on Wi-Fi networks, especially in scenarios where continuity of operation and low...
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet
In heterogeneous networks (HetNets), the overlap of small cells and the macro cell causes severe cross-tier interference. Although there exist some approaches to address this problem, they usually...