Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing

Nagib, Ahmad M.; Abou-zeid, Hatem; Hassanein, Hossam S.

doi:10.1109/lcn52139.2021.9524965

Cited by 24 publications

(24 citation statements)

References 39 publications

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“…Meanwhile, there are also some autonomous systems, such as autonomous driving and drone navigation, that require intelligent agents with computing and learning capabilities to perform real-time decision-making in dynamic environments based on deep reinforcement learning (DRL). However, DRL still faces some challenges in practical applications, such as slow convergence [97], overfitting problems [98], and poor exploration in complex environments [99]. Collaborative DRL (CDRL) is treated as a promising solution to the above issues, wherein the agents can share their experiences and collaboratively learn the optimal policy for their task [69].…”

Section: B Machine Learningmentioning

confidence: 99%

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemComempowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AIenabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semanticaware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction.

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Section: B Machine Learningmentioning

confidence: 99%

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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“…Some proposals address resource sharing and consider the algorithmic aspects of the resource allocation method. In [141] - [142], radio and power resources are allocated in a RAN. In [141], the convergence speed of DRL algorithms for the allocation of radio resources in different BSs has been investigated.…”

Section: ) Resource Sharing and Algorithmic Aspects Of Resource Alloc...mentioning

confidence: 99%

“…In [141] - [142], radio and power resources are allocated in a RAN. In [141], the convergence speed of DRL algorithms for the allocation of radio resources in different BSs has been investigated. To increase the convergence speed of agents in the system, a transfer learning-based method for DRL algorithms is proposed, in which expert agents in a BS transfer their knowledge to new BSs (which enter the system).…”

Section: ) Resource Sharing and Algorithmic Aspects Of Resource Alloc...mentioning

confidence: 99%

“…The authors in [157] address mobility management and dynamic slice creation and management, along with the listed challenges in Section V-D1. In [157], a distributed method [141], [143], [144], [145], [146], [147], [142], [153], [120], [40], [155], [157] ✓ called UC-PA-RA is provided to allocate power and radio blocks to user devices within clusters in multiple F-APs into a single F-RAN. In this method, users' devices in a specific cluster of a F-AP can receive their information through similar RBs.…”

Section: ) Mobility Management and Dynamic Slice Creation And Managementmentioning

confidence: 99%

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Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

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One of the key foundations of 5 th Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.

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“…Deep reinforcement learning (DRL) is viewed as a promising approach for training intelligent IoT agents to tackle complex tasks such as autonomous navigation. However, DRL methods can lead to slow convergence [2], overfitting problems [3], or sub-optimal performance due to poor exploration in complex environments [4]. These challenges limit the applications of DRL for real-time autonomous IoT services where convergence time, generalizability of the learning, and performance are all important factors.…”

Section: Introductionmentioning

confidence: 99%

Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks

Lotfi¹,

Semiari²,

Saad³

2021

Preprint

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Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach to enable future intelligent and autonomous systems that rely on real-time decision making in complex dynamic environments. Nonetheless, in practical scenarios, CDRL face many challenges due to heterogeneity of agents and their learning tasks, different environments, time constraints of the learning, and resource limitations of wireless networks. To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enable a group of heterogeneous untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network. To this end, a new heterogeneous federated DRL (HFDRL) algorithm is proposed to select the best subset of semantically relevant DRL agents for collaboration. The proposed approach then jointly optimizes the training loss and wireless bandwidth allocation for the cooperating selected agents in order to train each agent within the time limitation of its realtime task. Simulation results show the superior performance of the proposed algorithm compared to state-of-the-art baselines.

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Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing

Cited by 24 publications

References 39 publications

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks

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