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Found 23 papers, 3 papers with code
AI-enabled Priority and Auction-Based Spectrum Management for 6G
In this paper, we present a quality of service (QoS)-aware priority-based spectrum management scheme to guarantee the minimum required bit rate of vertical sector players (VSPs) in the 5G and beyond generation, including the 6th generation (6G).
Dynamic Fairness-Aware Spectrum Auction for Enhanced Licensed Shared Access in 6G Networks
We first propose a novel greedy algorithm, named market share based weighted greedy algorithm (MSWGA) to achieve better fairness compared to the traditional auction methods and as the second approach, we exploit deep reinforcement learning (DRL) algorithms, to optimize the auction policy and demonstrate its superiority over other methods.
AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty
In particular, the problem is formulated to minimize the average AoI of users with elastic tasks, by adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP.
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.
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties
Then, we compare the RDPG method in different scenarios with soft actor-critic (SAC), deep deterministic policy gradient (DDPG), distributed, and greedy algorithms.
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.
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks
In this paper, we consider that the unmanned aerial vehicles (UAVs) with attached intelligent reflecting surfaces (IRSs) play the role of flying reflectors that reflect the signal of users to the destination, and utilize the power-domain non-orthogonal multiple access (PD-NOMA) scheme in the uplink.
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning
In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20 %.
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.
AI-Based Secure NOMA and Cognitive Radio enabled Green Communications: Channel State Information and Battery Value Uncertainties
Using power-domain non-orthogonal multiple access technique, the SAP helps the primary network to improve the security of data transmission by using the frequency band of the primary network.
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.
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning
In this paper, we first utilize single agent low-complex compound action actor-critic RL to cover both discrete and continuous actions and jointly minimize VNF cost and AoI in terms of network resources under end-to end Quality of Service constraints.
Optimal SIC Ordering and Power Allocation in Downlink Multi-Cell NOMA Systems
In this work, we propose a globally optimal joint successive interference cancellation (SIC) ordering and power allocation (JSPA) algorithm for the sum-rate maximization problem in downlink multi-cell non-orthogonal multiple access (NOMA) systems.
Information Theory Information Theory 90C11 G.1.6
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.
Joint Resource and Admission Management for Slice-enabled Networks
Network slicing is a crucial part of the 5G networks that communication service providers (CSPs) seek to deploy.
Networking and Internet Architecture Systems and Control Systems and Control Optimization and Control
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.
