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Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-grids

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Deep Learning for Unmanned Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 984))

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Abstract

In this chapter, we describe the design of controlling schemes for energy self-sustainable mobile networks through Deep Learning. The goal is to enable an intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energies. To achieve this goal, we formulate an on-line grid energy and network throughput optimization problem considering both centralized and distributed Deep Reinforcement Learning implementations. We provide an exhaustive discussion on the reference scenario, the techniques adopted, the achieved performance, the complexity and the feasibility of the proposed models, together with the energy and cost savings attained. Results demonstrate that Deep Q-Learning based algorithms represent a viable and economically convenient solution for enabling energy self-sustainability of mobile networks grouped in micro-grids.

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Abbreviations

ANN:

Artificial neural network

BB:

Baseband

BS:

Base station

DRL:

Deep reinforcement learning

DDRL:

Distributed deep reinforcement learning

DP:

Dynamic programming

DQL:

Deep Q-learning

EH:

Energy harvesting

FQL:

Fuzzy Q-learning

MBS:

Macro base stations

MDP:

Markov decision process

MEC:

Multi-access edge computing

MRL:

Multi-agent reinforcement learning

NFV:

Network function virtualization

RAN:

Radio access network

RL:

Reinforcement learning

SBS:

Small base station

SDN:

Software defined networking

SGD:

Stochastic gradient descent

vBS:

Virtual small base station

\(\boldsymbol{A}^t\) :

Operative states (control actions) of the SBSs in slot t

\(\boldsymbol{B}^t\) :

Energy stored in batteries at beginning of slot t

\(\boldsymbol{H}^t\) :

Energy harvested by SBSs in slot t

\({h}^t\) :

Hour of the day in slot t

\({m}^t\) :

Month in slot t

\(\boldsymbol{L}^t\) :

Traffic load generated inside coverage of vSBs in slot t

\(r_t\) :

Scalar reward signal

\(\boldsymbol{X}^t\) :

State of the vSBs in slot t

\(\alpha \) :

Learning rate

\(\varepsilon \) :

Exploration parameter

\(\gamma \) :

Discount factor

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Acknowledgements

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 675891 (SCAVENGE) and by Spanish MINECO grant TEC2017-88373-R (5G-REFINE).

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Authors and Affiliations

  1. CTTC/CERCA, Barcelona, Spain

    Marco Miozzo & Paolo Dini

  2. Huawei Technologies, Paris Research Center, Boulogne-Billancourt, France

    Nicola Piovesan

  3. Ericsson Research, Stockholm, Sweden

    Dagnachew Azene Temesgene

Authors
  1. Marco Miozzo
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  2. Nicola Piovesan
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  3. Dagnachew Azene Temesgene
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  4. Paolo Dini
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Corresponding author

Correspondence to Marco Miozzo .

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Editors and Affiliations

  1. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubaa

  2. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

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Miozzo, M., Piovesan, N., Temesgene, D.A., Dini, P. (2021). Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-grids. In: Koubaa, A., Azar, A.T. (eds) Deep Learning for Unmanned Systems. Studies in Computational Intelligence, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-77939-9_8

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