Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G
Intent-driven network management is critical for managing the complexity of 5G and 6G networks. It enables adaptive, on-demand management of the network based on the objectives of the network...
Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN
This paper introduces an innovative method for predicting wireless network traffic in concise temporal intervals for Open Radio Access Networks (O-RAN) using a transformer architecture, which is...
Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach
Traffic Steering is a crucial technology for wireless networks, and multiple efforts have been put into developing efficient Machine Learning (ML)-enabled traffic steering schemes for Open Radio...
Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model
With the emergence of 6G, mobile networks are becoming increasingly heterogeneous and dynamic, necessitating advanced automation for efficient management. Intent-Driven Networks (IDNs) address...
Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning
rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-vendor...
Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing
Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In...
AI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues
A plethora of demanding services and use cases mandate a revolutionary shift in the management of future wireless network resources. Indeed, when tight quality of service demands of applications...
Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments
In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring smooth user experience by selecting the best radio access technology (RAT) from a multi-RAT...
The Internet of Senses: Building on Semantic Communications and Edge Intelligence
The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human `receptors' and therefore blurs the difference of virtual and real environments. We...
Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning
In 5G non-standalone mode, traffic steering is a critical technique to take full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE networks in multiple radio access...
Self-Play Ensemble Q-learning enabled Resource Allocation for Network Slicing
In 5G networks, network slicing has emerged as a pivotal paradigm to address diverse user demands and service requirements. To meet the requirements, reinforcement learning (RL) algorithms have...
On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering
Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base stations (BSs)....
Intelligent Attacks and Defense Methods in Federated Learning-enabled Energy-Efficient Wireless Networks
Federated learning (FL) is a promising technique for learning-based functions in wireless networks, thanks to its distributed implementation capability. On the other hand, distributed learning may...
Explainable Multi-Agent Reinforcement Learning for Extended Reality Codec Adaptation
Extended Reality (XR) services are set to transform applications over 5th and 6th generation wireless networks, delivering immersive experiences. Concurrently, Artificial Intelligence (AI)...
Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning
The growing adoption of mmWave frequency bands to realize the full potential of 5G, turns beamforming into a key enabler for current and next-generation wireless technologies. Many mmWave networks...
Cooperative Hierarchical Deep Reinforcement Learning based Joint Sleep and Power Control in RIS-aided Energy-Efficient RAN
Energy efficiency (EE) is one of the most important metrics for envisioned 6G networks, and sleep control, as a cost-efficient approach, can significantly lower power consumption by switching off...
RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learning
5G is regarded as a revolutionary mobile network, which is expected to satisfy a vast number of novel services, ranging from remote health care to smart cities. However, heterogeneous Quality of...
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity
5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE's existent maximum data rates. However, the effective size of 5G antennas and consequently its repercussions in the...