Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN

Zhou, Hao; Kong, Long; Elsayed, Medhat; Gaigalas, Raimundas; Furr, Steve; Erol‐Kantarci, Melike

doi:10.1109/globecom48099.2022.10001554

Cited by 4 publications

(3 citation statements)

References 39 publications

(58 reference statements)

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“…Dinkelbach method is applied in [48], [49], but these conventional methods can not be directly applied to sumratio problems. Note that the power consumption definition 4) [65].…”

Section: Energy Efficiency Maximizationmentioning

confidence: 99%

“…Based on the shortterm performance of the sub-controller, the meta-controller can adjust the goal dynamically to guarantee the long-term performance of the whole system. Hierarchical learning can also be applied to optimization problems that include multiple control variables with different time scales [65]. For instance, in [65], Zhou et al considered a meta-controller for sleep control, and sub-controllers for transmission power and RIS control, enabling control variables with different time scales.…”

Section: G Hierarchical Learningmentioning

confidence: 99%

“…Hierarchical learning can also be applied to optimization problems that include multiple control variables with different time scales [65]. For instance, in [65], Zhou et al considered a meta-controller for sleep control, and sub-controllers for transmission power and RIS control, enabling control variables with different time scales.…”

Section: G Hierarchical Learningmentioning

confidence: 99%

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Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Zhang¹,

Erol‐Kantarci²,

Poor³

2023

IEEE Wireless Commun.

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Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RISaided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Modelbased, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RISaided applications towards 6G networks and identify future challenges.

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“…Dinkelbach method is applied in [48], [49], but these conventional methods can not be directly applied to sumratio problems. Note that the power consumption definition 4) [65].…”

Section: Energy Efficiency Maximizationmentioning

confidence: 99%

Section: G Hierarchical Learningmentioning

confidence: 99%