ML-Based Optimization of Large-Scale Systems: Case Study in Smart Microgrids and 5G RAN

Abstract

The recent advances in machine learning (ML) have brought revolutionary changes to every field. Many novel applications, such as face recognition and natural language processing, have demonstrated the great potential of ML techniques. Indeed, ML can significantly enhance the intelligence of many existing systems, including smart grid, wireless communications, mechanical engineering, and so on. For instance, microgrid (MG), a distribution-level power system, can exchange energy with the main grid or work in islanded mode, which enables higher flexibility for the smart grid. However, it suffers considerable management complexity by including multiple entities such as renewable energy resources, energy storage system (ESS), loads, etc. In addition, each entity may have unique observations and policies to make autonomous decisions. Similarly, 5G networks are designed to provide lower latency, higher throughput and reliability for a large number of user devices, but the evolving network architecture also leads to great complexity for network management. The 5G network management should jointly consider various user types and network resources in a dynamic wireless environment. In addition, the integration of new techniques, such as reconfigurable intelligent surfaces (RISs), requires more efficient algorithms for network optimization. Consequently, intelligent management schemes are crucial to schedule network resources. In this work, we aim to develop state-of-the-art ML techniques to improve the performance of large-scale systems. As case studies, we focus on MG energy management and 5G radio access network (RAN) management. Multi-agent reinforcement learning (MARL) is presumed to be an ideal solution for MG energy management by considering each entity as an independent agent. We further investigate how communication failures will affect MG energy trading by using Bayesian deep reinforcement learning (BA-DRL). On the 5G side, we use MARL, transfer reinforcement learning (TRL), and hierarchical reinforcement learning (HRL) to improve network performance. In particular, we study the performance of those algorithms under various scenarios, including radio resource allocation for network slicing, joint radio and computation resource for mobile edge computing (MEC), joint radio and cache resource allocation for edge caching. Additionally, we further investigate how HRL can improve the energy efficiency (EE) of RIS-aided heterogeneous networks. The findings of this research highlight the capabilities of various ML techniques under different application domains. Firstly, different MG entities can be well coordinated by applying MARL, enabling intelligent decision-making for each agent. Secondly, Bayesian theory can be used to solve partially observable Markov decision process (POMDP) problems caused by communication failures in MARL. Thirdly, MARL is capable of balancing the heterogeneous requirements of different slices in 5G networks, guaranteeing satisfactory overall network performance. Then, we find that TRL can significantly improve the convergence performance of conventional reinforcement learning or deep reinforcement learning by transferring the knowledge from experts to learners, which is demonstrated over a 5G network slicing case study. Finally, we find that long-term and short-term decisions are well coordinated by HRL, and the proposed cooperative hierarchical architecture achieves higher throughput and EE than conventional algorithms.