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Deep Reinforcement Learning in Communication Systems and Networks
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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: closed (30 March 2024) | Viewed by 9186

Special Issue Editors

Dr. Gianmarco Romano

E-Mail Website
Guest Editor
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
Dr. Giovanni Di Gennaro

E-Mail Website
Guest Editor
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
Dr. Amedeo Buonanno

E-Mail Website
Guest Editor
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

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Published Papers (4 papers)

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Research

22 pages, 1013 KiB  
Article
Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
by Thomas Avé, Tom De Schepper and Kevin Mets
Sensors 2024, 24(15), 4876; https://doi.org/10.3390/s24154876 - 27 Jul 2024
Viewed by 681
Abstract
Interest in deploying deep reinforcement learning (DRL) models on low-power edge devices, such as Autonomous Mobile Robots (AMRs) and Internet of Things (IoT) devices, has seen a significant rise due to the potential of performing real-time inference by eliminating the latency and reliability [...] Read more.
Interest in deploying deep reinforcement learning (DRL) models on low-power edge devices, such as Autonomous Mobile Robots (AMRs) and Internet of Things (IoT) devices, has seen a significant rise due to the potential of performing real-time inference by eliminating the latency and reliability issues incurred from wireless communication and the privacy benefits of processing data locally. Deploying such energy-intensive models on power-constrained devices is not always feasible, however, which has led to the development of model compression techniques that can reduce the size and computational complexity of DRL policies. Policy distillation, the most popular of these methods, can be used to first lower the number of network parameters by transferring the behavior of a large teacher network to a smaller student model before deploying these students at the edge. This works well with deterministic policies that operate using discrete actions. However, many real-world tasks that are power constrained, such as in the field of robotics, are formulated using continuous action spaces, which are not supported. In this work, we improve the policy distillation method to support the compression of DRL models designed to solve these continuous control tasks, with an emphasis on maintaining the stochastic nature of continuous DRL algorithms. Experiments show that our methods can be used effectively to compress such policies up to 750% while maintaining or even exceeding their teacher’s performance by up to 41% in solving two popular continuous control tasks. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning in Communication Systems and Networks)
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16 pages, 467 KiB  
Article
Dynamic Spectrum Sharing Based on Deep Reinforcement Learning in Mobile Communication Systems
by Sizhuang Liu, Changyong Pan, Chao Zhang, Fang Yang and Jian Song
Sensors 2023, 23(5), 2622; https://doi.org/10.3390/s23052622 - 27 Feb 2023
Cited by 6 | Viewed by 2875
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
by Janghoon Yang
Sensors 2022, 22(24), 9811; https://doi.org/10.3390/s22249811 - 14 Dec 2022
Viewed by 1378
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
by Seoyoung Yu and Jeong Woo Lee
Sensors 2022, 22(23), 9459; https://doi.org/10.3390/s22239459 - 3 Dec 2022
Cited by 8 | Viewed by 2695
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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