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Deep Reinforcement Learning in Communication Systems and Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 30 March 2024 | Viewed by 5370

Special Issue Editors

Department of Engineering, University of Campania “L. Vanvitelli”, 81031 Aversa, CE, Italy
Interests: signal processing; wireless communications; wireless sensor networks; 5G; MIMO; OFDM; software defined radio
Special Issues, Collections and Topics in MDPI journals
Department of Engineering, University of Campania “Luigi Vanvitelli”, via Roma, 29, 81031 Aversa, CE, Italy
Interests: machine learning; artificial neural network; deep learning; reinforcement learning; signal processing; image processing; time series analysis; natural language processing
ENEA - Department of Energy Technologies and Renewable Energy Sources, P.le E. Fermi, 1 (Loc. Granatello), 80055 Portici, NA, Italy
Interests: machine learning; artificial neural network; deep learning; reinforcement learning; signal processing; image processing; time series analysis; energy forecasting; smart-grids

Special Issue Information

Dear Colleagues,

Deep reinforcement learning (DRL), the combination of reinforcement learning and deep learning, has emerged as a viable solution for overcoming limitations due to large state–action spaces and improving the learning speed and performances. DRL algorithms have been developed to address issues and challenges arising in a variety of fields, such as robotics, computer vision, and speech recognition, their application potentially able to extend to solve any optimization problem in a general way by searching for a solution through interactions with the environment.

Recently, DRL algorithms have been developed to address communication system and network problems to tackle complex optimization tasks that cannot be solved efficiently with traditional optimization techniques. For example, wireless networks represent a complex dynamic environment, where the efficient use of spectrum utilization, power control, interference coordination and beamforming is needed to cope with the increasing demand of a large number of devices and higher data rates in future communication systems.

This Special Issue invites prospective authors to submit original contributions regarding applications of deep reinforcement learning algorithms, with a specific focus on communication systems and networks.

Dr. Gianmarco Romano
Dr. Giovanni Di Gennaro
Dr. Amedeo Buonanno
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep reinforcement learning
  • communications
  • wireless networks
  • 5G/6G
  • spectrum access
  • intelligent reflecting surface
  • Internet of Things (IoT)
  • heterogeneous networks (HetNets)
  • unmanned aerial vehicle (UAV)
  • vehicular ad hoc networks

Published Papers (3 papers)

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Research

16 pages, 467 KiB  
Article
Dynamic Spectrum Sharing Based on Deep Reinforcement Learning in Mobile Communication Systems
Sensors 2023, 23(5), 2622; https://doi.org/10.3390/s23052622 - 27 Feb 2023
Cited by 2 | Viewed by 1633
Abstract
The rapid development of mobile communication services in recent years has resulted in a scarcity of spectrum resources. This paper addresses the problem of multi-dimensional resource allocation in cognitive radio systems. Deep reinforcement learning (DRL) combines deep learning and reinforcement learning to enable [...] Read more.
The rapid development of mobile communication services in recent years has resulted in a scarcity of spectrum resources. This paper addresses the problem of multi-dimensional resource allocation in cognitive radio systems. Deep reinforcement learning (DRL) combines deep learning and reinforcement learning to enable agents to solve complex problems. In this study, we propose a training approach based on DRL to design a strategy for secondary users in the communication system to share the spectrum and control their transmission power. The neural networks are constructed using the Deep Q-Network and Deep Recurrent Q-Network structures. The results of the conducted simulation experiments demonstrate that the proposed method can effectively improve the user’s reward and reduce collisions. In terms of reward, the proposed method outperforms opportunistic multichannel ALOHA by about 10% and about 30% for the single SU scenario and the multi-SU scenario, respectively. Furthermore, we explore the complexity of the algorithm and the influence of parameters in the DRL algorithm on the training. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning in Communication Systems and Networks)
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11 pages, 1346 KiB  
Article
Reinforcement Learning with Side Information for the Uncertainties
Sensors 2022, 22(24), 9811; https://doi.org/10.3390/s22249811 - 14 Dec 2022
Viewed by 918
Abstract
Recently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the presence of uncertainties. While our previous [...] Read more.
Recently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the presence of uncertainties. While our previous study introduced SMC to the reinforcement learning (RL) based on approximate dynamic programming in the context of optimal control, SMC is introduced to a conventional RL framework in this work. As a specific realization, the modified twin delayed deep deterministic policy gradient (DDPG) for consensus was exploited to develop sliding mode RL. Numerical experiments show that the sliding mode RL outperforms existing state-of-the-art RL methods and model-based methods in terms of the mean square error (MSE) performance. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning in Communication Systems and Networks)
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19 pages, 2726 KiB  
Article
Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks
Sensors 2022, 22(23), 9459; https://doi.org/10.3390/s22239459 - 03 Dec 2022
Cited by 4 | Viewed by 1708
Abstract
In this paper, a resource allocation (RA) scheme based on deep reinforcement learning (DRL) is designed for device-to-device (D2D) communications underlay cellular networks. The goal of RA is to determine the transmission power and spectrum channel of D2D links to maximize the sum [...] Read more.
In this paper, a resource allocation (RA) scheme based on deep reinforcement learning (DRL) is designed for device-to-device (D2D) communications underlay cellular networks. The goal of RA is to determine the transmission power and spectrum channel of D2D links to maximize the sum of the average effective throughput of all cellular and D2D links in a cell accumulated over multiple time steps, where a cellular channel can be allocated to multiple D2D links. Allowing a cellular channel to be shared by multiple D2D links and considering performance over multiple time steps require a high level of system overhead and computational complexity so that optimal RA is practically infeasible in this scenario, especially when a large number of D2D links are involved. To mitigate the complexity, we propose a sub-optimal RA scheme based on a multi-agent DRL, which operates with shared information in participating devices, such as locations and allocated resources. Each agent corresponds to each D2D link and multiple agents perform learning in a staggered and cyclic manner. The proposed DRL-based RA scheme allocates resources to D2D devices promptly according to dynamically varying network set-ups, including device locations. The proposed sub-optimal RA scheme outperforms other schemes, where the performance gain becomes significant when the densities of devices in a cell are high. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning in Communication Systems and Networks)
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