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Found 14 papers, 1 papers with code
Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks
To this end, we introduce a new metric, i. e., throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators.
Two-Hop Age of Information Scheduling for Multi-UAV Assisted Mobile Edge Computing: FRL vs MADDPG
Using UAVs and BS jointly with MEC can reduce AoI on the network.
A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective
As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands.
Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?
In this paper, our aim is to make the network more scalable and more flexible in terms of achievable data rate, overhead, and complexity indicators.
Multicasting in NOMA-Based UAV Networks: Path Design and Throughput Maximization
We adopt power domain non-orthogonal multiple access (PD-NOMA) as the transmission technology using which the dedicated signals of multicast groups are superimposed and transmitted simultaneously as the UAV passes over the communication area for fixed and mobile users.
Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks
UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs.
Learning based E2E Energy Efficient in Joint Radio and NFV Resource Allocation for 5G and Beyond Networks
To this end, we formulate an optimization problem in which power and spectrum resources are allocated in the radio part.
AoI Minimization in Energy Harvesting and Spectrum Sharing Enabled 6G Networks
The average user access improved from 30% in the overlay mode to 45% in the DQN and 48% in the D3QN.
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning
Hence, we exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy.
Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach
In this paper, we propose a Zero-Touch, deep reinforcement learning (DRL)-based Proactive Failure Recovery framework called ZT-PFR for stateful network function virtualization (NFV)-enabled networks.
Robust Energy-Efficient Resource Management, SIC Ordering, and Beamforming Design for MC MISO-NOMA Enabled 6G
This paper studies a novel approach for successive interference cancellation (SIC) ordering and beamforming in a multiple antennas non-orthogonal multiple access (NOMA) network with multi-carrier multi-user setup.
A Unified Framework for Joint Energy and AoI Optimization via Deep Reinforcement Learning for NOMA MEC-based Networks
To further improve the system capacity, non-orthogonal multiple access (NOMA) is proposed as a candidate for multiple access schemes for future cellular networks.
Signal Processing
Joint Radio Resource Allocation and Cooperative Caching in PD-NOMA-Based HetNets
Caching phase where the contents are saved in the storage of the BSs, and 2.
Cost-Effective Radio and NFV Resource Allocation: E2E QoS Provision
Simulation results demonstrate that the proposed heuristic algorithm and framework outperforms the existing ones by 34% on average in cost reduction.
