Sum rate maximization in muti-cell muti-user networks: An inverse reinforcement learning-based...

[Free GPT-4]
[DeepSeek]
[PDF] arxiv.org

Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization

H Du, R Zhang, Y Liu, J Wang, Y Lin, Z Li… - arxiv preprint arxiv …, 2023 - arxiv.org
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …
保存 引用 被引用数: 77 関連記事 全 2 バージョン HTMLバージョン

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

H Du, R Zhang, Y Liu, J Wang, Y Lin… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across …
保存 引用 被引用数: 52 関連記事

Resource Management in Multi-Cell Collaborative Transmission for Long-Term URLLC Services

L Shan, Y Wang, Y Cang, F Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Ultra-reliable low-latency communication (URLLC) is a critical type of service that imposes
stringent latency requirements. Considering the random and burst URLLC packets arrival …
保存 引用 関連記事
[Free GPT-4]
[DeepSeek]
[PDF] arxiv.org

Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning

SS Hassan, YM Park, YK Tun, W Saad, Z Han… - arxiv preprint arxiv …, 2024 - arxiv.org
In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy
learning approach is proposed for optimizing beamforming, spectrum allocation, and remote …
保存 引用 関連記事 HTMLバージョン

Deployment Optimization of Multi-Cell Multi-User System with Hard Core Poisson Point Distribution

Y **n, L Ma, J Wang, X Wan, W Zhao… - 2024 IEEE/CIC …, 2024 - ieeexplore.ieee.org
With the continuous development of information and communication technology, the scale of
communication systems and the randomness of wireless signals increase. Hence it is urgent …
保存 引用 被引用数: 1 関連記事
[Free GPT-4]
[DeepSeek]
[PDF] arxiv.org

Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking

Y Liu, R Zhang, H Du, D Niyato, J Kang, Z **ong… - arxiv preprint arxiv …, 2024 - arxiv.org
Performance optimization is a critical concern in networking, on which Deep Reinforcement
Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely …
保存 引用 関連記事 全 2 バージョン HTMLバージョン
[Free GPT-4]
[DeepSeek]
[HTML] nih.gov

[HTML][HTML] Energy-Efficient Dynamic Enhanced Inter-Cell Interference Coordination Scheme Based on Deep Reinforcement Learning in H-CRAN

H Choi, T Kim, S Lee, HS Choi… - Sensors (Basel …, 2024 - pmc.ncbi.nlm.nih.gov
The proliferation of 5G networks has revolutionized wireless communication by delivering
enhanced speeds, ultra-low latency, and widespread connectivity. However, in …
保存 引用 関連記事 全 8 バージョン
[Free GPT-4]
[DeepSeek]
[PDF] researchsquare.com

Multi-timescale Scheme for Cooperative User Association and Hybrid Beamforming in mmWave MIMO Systems

M Heydarian, D Colle, M Pickavet, W Tavernier - 2024 - researchsquare.com
Hybrid beamforming has received significant attention as a solution to the thermal issues,
costs, and implementation complexities associated with fully digital mmWave Extremely …
保存 引用 関連記事 全 2 バージョン HTMLバージョン