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As we progress from 5G to emerging 6G wireless, the spectrum of cellular communication services is set to broaden significantly, encompassing real-time remote healthcare applications and sophisticated smart infrastructure solutions, among others. This expansion brings to the forefront a diverse set of service requirements, underscoring the challenges and complexities inherent in next-generation networks. In the realm of 5G, Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) have been pivotal service categories. As we venture into the 6G era, these foundational use cases will evolve and embody additional performance criteria, further diversifying the network service portfolio. This evolution amplifies the necessity for dynamic and efficient resource allocation strategies capable of balancing the diverse service demands. In response to this need, we introduce the Intelligent Dynamic Resource Allocation and Puncturing (IDRAP) framework. Leveraging Deep Reinforcement Learning (DRL), IDRAP is designed to balance between the bandwidth-intensive requirements of eMBB services and the latency and reliability needs of URLLC users. The performance of IDRAP is evaluated and compared against other resource management solutions, including Intelligent Dynamic Resource Slicing (IDRS), Policy Gradient Actor-Critic Learning (PGACL), System-Wide Tradeoff Scheduling (SWTS), Sum-Log, and Sum-Rate.The results show an improved Service Satisfaction Level (SSL) for eMBB users while maintaining the essential SSL threshold for URLLC services.
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This content will become publicly available on June 9, 2025
RIS-Assisted ABS for Mobile Multi-User MISO Wireless Communications: A Deep Reinforcement Learning Approach
In response to the evolving landscape of wireless communication networks and the escalating demand for unprecedented wireless connectivity performance in the forthcoming 6G era, this paper proposes a new 6G architecture to enhance the wireless network's sum rate performance. Therefore, we introduce an aerial base station (ABS) network with reconfigurable intelligent surfaces (RISs) while leveraging the multi-users multiple-input single-output (MU-MISO) antenna technology. The motivation behind our proposal stems from the imperative to address critical challenges in contemporary wireless networks and harness emerging technologies for substantial performance gains. We employ deep reinforcement learning (DRL) to jointly optimize the ABS trajectories, the active beamforming weights, and the RIS phase shifts. Simulation results show that this joint optimization effectively improves the system's sum rate while meeting minimum quality of service (QoS) requirements for diverse mobile users.
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- Award ID(s):
- 2030291
- PAR ID:
- 10566850
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 1938-1883
- ISBN:
- 978-1-7281-9054-9
- Page Range / eLocation ID:
- 648 to 653
- Subject(s) / Keyword(s):
- 6G wireless deep reinforcement learning eavesdropping RIS sum rate QoS UAV ABS DDPG
- Format(s):
- Medium: X
- Location:
- Denver, CO, USA
- Sponsoring Org:
- National Science Foundation
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