6G Enabling Technologies

Polese, Michele; Giordani, Marco; Mezzavilla, Marco; Rangan, Sundeep; Zorzi, Michele

doi:10.1007/978-3-030-72777-2_3

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…We define Φ ′ N ×M = (ϕ ′ ij ) as a matrix storing task allocation after migration, defined similarly as Eqn. (1).…”

Section: A the Automated System For Dynamic Task Migrationmentioning

confidence: 99%

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Distributed Intelligence for Dynamic Task Migration in the 6G User Plane using Deep Reinforcement Learning

Majumdar,

Schwarzmann,

Trivisonno

et al. 2023

Preprint

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<p>In-Network Computing (INC) is a currently emerging paradigm. Realizing INC in 6G networks could mean that user plane entities (UPEs) carry out computations on packets while transmitting them. These computations may have specific requirements in terms of their completion time. In case of high compute pressure at one UPE, migrating computations to another UPE may be beneficial, in order to avoid exceeding the completion time requirement. Centralized migration approaches suffer from increased signaling and are prone to react too slow. Therefore, this paper investigates the applicability of distributed intelligence to tackle the problem of compute task migration in the 6G user plane. Each UPE is equipped with an intelligent agent, enabling autonomous decisions on whether computations should be migrated to another UPE. To enable the intelligent agents to learn and apply an optimal task migration policy, we investigate and compare of two state-of-the-art Deep Reinforcement Learning (DRL) approaches: Advantage Actor-Critic (A2C) and Double Deep Q-Network (DDQN). We show, via simulations, that the performance of both solutions, in terms of the percentage of tasks exceeding their completion time requirement, is near-optimal and training A2C is at least 60% faster. </p>

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“…We define Φ ′ N ×M = (ϕ ′ ij ) as a matrix storing task allocation after migration, defined similarly as Eqn. (1).…”

Section: A the Automated System For Dynamic Task Migrationmentioning

confidence: 99%

“…The Sixth Generation (6G) of communication networks is expected to provide services, with extremely stringent delay requirements [1], [2]. Edge Computing, which shifts computational resources closer to the user, may not be sufficient anymore to satisfy their requirements.…”

Section: Introductionmentioning

confidence: 99%

Distributed Intelligence for Dynamic Task Migration in the 6G User Plane using Deep Reinforcement Learning

Majumdar,

Schwarzmann,

Trivisonno

et al. 2023

Preprint

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“…Until now, many research efforts have been made on 5G and B5G networks, but still many areas are open for investigation. Since the desire to go towards 5G has been growing, a considerable number of papers have been made and extensive research activities are going on in global wise [1][2][3]. ese papers tend to highlight use cases, requirements, and performance as well as some of the important enabling technologies of next-generation wireless networks.…”

Section: Introductionmentioning

confidence: 99%

“…Various wireless transmission techniques are constantly being developed to be integrated into a new generation of wireless communication systems. ey should meet a large number of set requirements, such as high spectral efficiency, bandwidth, larger network capacity, higher energy efficiency, as little delay as possible, and also high data rates with high reliability, that is, less errors and cost as possible [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

Krstić

Petrović

Al-Azzoni

2022

Mathematical Problems in Engineering

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Efficient resource planning is recognized as one of the key enablers making the large-scale deployment of next-generation wireless networks available for mass usage. Modelling, planning, and software simulation tools reduce both the time needed and costs of their tuning and realization. In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. The predictions about service demand and energy consumption are taken into account. Also, the impact of degradations resulting from fading and cochannel interference (CCI) effects is also considered. The optimization task is treated as a component allocation problem (CAP) aiming to find the best possible base station allocation for the considered smart city locations with respect to performance and service demand constraints. The goal is to maximize Quality of Service (QoS) while keeping the costs and energy consumption as low as possible. The adoption of a model-driven approach in combination with model-to-model transformations and automated code generation does not only reduce the complexity, making experimentation more rapid and convenient at the same time, but also increase the overall reusability and expandability of the planning tool. According to the obtained results, the proposed solution seems to be promising not only due to achieved benefits but also regarding the execution time, which is shorter than that achieved in our previous works, especially for larger distances. Further, we adopt model-based representation of handover strategies within the planning tool, enabling examination of the dynamic behavior of user-created plan, which is not exploited in other similar works. The main contributions of the paper are (1) wireless network planning (WNP) metamodel, a modelling notation for network plans; (2) model-to-model transformation for conversion of WNP to generalized CAP metamodel; (3) prediction problem (PP) metamodel, high-level abstraction for representation of prediction-related regression and classification problems; (4) code generator that creates PyTorch neural network from PP representation; (5) service demand and energy consumption prediction modules performing regression; (6) multiobjective optimization model for base station allocation; (7) Handover Strategy (HS) metamodel used for description of dynamic aspects and adaptability relevant to network planning.

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6G Communication Networks: Introduction, Vision, Challenges, and Future Directions

Meena

Pal

Jain

et al. 2022

Wireless Pers Commun

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6G Enabling Technologies

Cited by 8 publications

References 32 publications

Distributed Intelligence for Dynamic Task Migration in the 6G User Plane using Deep Reinforcement Learning

Distributed Intelligence for Dynamic Task Migration in the 6G User Plane using Deep Reinforcement Learning

Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

6G Communication Networks: Introduction, Vision, Challenges, and Future Directions

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