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Deep Learning Driven Buffer-Aided Cooperative Networks for B5G/6G: Challenges, Solutions, and Future Opportunities

Peng Xu, Gaojie Chen, Jianping Quan, Chong Huang, Ioannis Krikidis, Kai-Kit Wong, Chan-Byoung Chae

TL;DR

The paper surveys buffer-aided cooperative networks (BACNs) in the context of B5G/6G and identifies key challenges such as low latency, ultra-high reliability, imperfect CSI, physical-layer security, and heterogeneous networks. It argues for deep learning-based solutions, including deep reinforcement learning (DRL), graph neural networks (GNNs), meta-learning, and Transformers, implemented in both centralized and decentralized (MAPPO) settings to optimize buffer-aided relay decisions. The authors present two case studies in non-terrestrial networks to demonstrate the effectiveness of centralized DRL and decentralized MAPPO for throughput optimization under strict delay and secrecy constraints. They also discuss integration with reconfigurable intelligent surfaces (RIS), SAGIN, and privacy-preserving frameworks as future directions.

Abstract

Buffer-aided cooperative networks (BACNs) have garnered significant attention due to their potential applications in beyond fifth generation (B5G) or sixth generation (6G) critical scenarios. This article explores various typical application scenarios of buffer-aided relaying in B5G/6G networks to emphasize the importance of incorporating BACN. Additionally, we delve into the crucial technical challenges in BACN, including stringent delay constraints, high reliability, imperfect channel state information (CSI), transmission security, and integrated network architecture. To address the challenges, we propose leveraging deep learning-based methods for the design and operation of B5G/6G networks with BACN, deviating from conventional buffer-aided relay selection approaches. In particular, we present two case studies to demonstrate the efficacy of centralized deep reinforcement learning (DRL) and decentralized DRL in buffer-aided non-terrestrial networks. Finally, we outline future research directions in B5G/6G that pertain to the utilization of BACN.

Deep Learning Driven Buffer-Aided Cooperative Networks for B5G/6G: Challenges, Solutions, and Future Opportunities

TL;DR

The paper surveys buffer-aided cooperative networks (BACNs) in the context of B5G/6G and identifies key challenges such as low latency, ultra-high reliability, imperfect CSI, physical-layer security, and heterogeneous networks. It argues for deep learning-based solutions, including deep reinforcement learning (DRL), graph neural networks (GNNs), meta-learning, and Transformers, implemented in both centralized and decentralized (MAPPO) settings to optimize buffer-aided relay decisions. The authors present two case studies in non-terrestrial networks to demonstrate the effectiveness of centralized DRL and decentralized MAPPO for throughput optimization under strict delay and secrecy constraints. They also discuss integration with reconfigurable intelligent surfaces (RIS), SAGIN, and privacy-preserving frameworks as future directions.

Abstract

Buffer-aided cooperative networks (BACNs) have garnered significant attention due to their potential applications in beyond fifth generation (B5G) or sixth generation (6G) critical scenarios. This article explores various typical application scenarios of buffer-aided relaying in B5G/6G networks to emphasize the importance of incorporating BACN. Additionally, we delve into the crucial technical challenges in BACN, including stringent delay constraints, high reliability, imperfect channel state information (CSI), transmission security, and integrated network architecture. To address the challenges, we propose leveraging deep learning-based methods for the design and operation of B5G/6G networks with BACN, deviating from conventional buffer-aided relay selection approaches. In particular, we present two case studies to demonstrate the efficacy of centralized deep reinforcement learning (DRL) and decentralized DRL in buffer-aided non-terrestrial networks. Finally, we outline future research directions in B5G/6G that pertain to the utilization of BACN.
Paper Structure (24 sections, 6 figures)

This paper contains 24 sections, 6 figures.

Table of Contents

  1. Introduction
  2. B5G/6G Vision and Background of BACN
  3. Typical Applications
  4. Relay Selection Schemes in BACNs
  5. Technical Challenges Behind BACNs
  6. Low Latency
  7. High Reliability
  8. Transmission with Imperfect CSI
  9. Physical Layer Secure Transmission
  10. Heterogeneous Network
  11. Potential Deep Learning Techniques
  12. Deep Reinforcement Learning
  13. Graph Neural Network
  14. Meta Learning
  15. Transformer
  16. ...and 9 more sections

Figures (6)

  • Figure 1: Communication scenarios of B5G/6G networks with buffer-aided relaying.
  • Figure 2: The structure of deep learning algorithms.
  • Figure 3: Deep learning framework for BACNs.
  • Figure 4: The structure of decentralized DRL and centralized DRL in non-terrestrial networks (NTNs) with buffer relaying.
  • Figure 5: Throughput vs. target delay in decentralized NTN with buffer relaying.
  • ...and 1 more figures