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Found 23 papers, 7 papers with code
Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks
Our aim is to achieve a less complex, distributed and scalable solution.
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.
RISnet: A Scalable Approach for Reconfigurable Intelligent Surface Optimization with Partial CSI
Two major challenges of this problem are the high dimensionality due to the large number of RIS elements and the difficulty to obtain the full channel state information (CSI), which is assumed known in many algorithms proposed in the literature.
Non-Orthogonal Multiple Access Assisted by Reconfigurable Intelligent Surface Using Unsupervised Machine Learning
Nonorthogonal multiple access (NOMA) with multi-antenna base station (BS) is a promising technology for next-generation wireless communication, which has high potential in performance and user fairness.
Energy Efficient Operation of Adaptive Massive MIMO 5G HetNets
However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet.
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.
RISnet: a Dedicated Scalable Neural Network Architecture for Optimization of Reconfigurable Intelligent Surfaces
The RIS optimization is designed according to the RIS property of product and direct channel and homogeneous RIS antennas.
Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning
This optimization problem is non-convex and its global optimum is difficult to find.
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.
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.
Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network
The rectangular shape of the RIS and the spatial correlation of channels with adjacent RIS antennas due to the short distance between them encourage us to apply it for the RIS configuration.
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.
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.
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.
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
On Fading Channel Dependency Structures with a Positive Zero-Outage Capacity
Based on a systematic copula approach, we show that there exists a set of dependency structures for which positive ZOCs can be achieved for both maximum ratio combining (MRC) and selection combining (SC).
Information Theory Information Theory
Effective Energy Efficiency of Ultra-reliable Low Latency Communication
Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption.
Information Theory Information Theory
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.
A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks
Specifically, thanks to its reduced complexity, the proposed method can be used to train an artificial neural network to predict the optimal resource allocation.
