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Entropy
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Information Theory and 5G/6G Mobile Communications
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Information Theory and 5G/6G Mobile Communications

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 20464

Special Issue Editors

Prof. Dr. Wonjong Noh

E-Mail Website
Guest Editor
School of Software, Hallym University, Chuncheon-si, Gangwon-do, Korea
Interests: network capacity; network optimization; stochastic QoS guarantee; machine learning; information theory
Special Issues, Collections and Topics in MDPI journals
Prof. Sung Hoon Lim

E-Mail Website
Guest Editor
School of Software, Hallym University, Gangwon-do, Chuncheon-si, Korea
Interests: network information theory; data compression; machine learning

Special Issue Information

Dear colleagues,

Information theory has been the theoretical background of modern communication technologies. At each critical stage of mobile communication evolution, information theory has provided innovative directions for future breakthroughs. The aim of this Special Issue is to encourage researchers to present original and recent developments on information theory to analyze what lies beyond 5G. The present Special Issue focuses on the fundamental theory, performance limits, design, and management issues in the context of 5G/6G communication systems. For this purpose, submissions of comprehensive overviews and surveys for future networks, as well as original papers related to these techniques, are proposed. Topics of interest include, but are not limited to the following areas:

  • Information theory applied to 5G/6G communication systems;
  • Capacity bounds, code designs, applications;
  • AI and machine learning technology applied to 5G and 6G wireless communications to tackle optimized physical layer design, complicated decision making, network management, and resource optimization;
  • New interference and resource controls for 5G/6G communication systems;
  • New system architectures stemming from the combination of computing, communication, and storage;
  • Integration of wireless information and energy transfer;
  • Big data technology for 5G/6G wireless networks;
  • Novel waveform design and multiple access methods;
  • Cell-free massive MIMO for 5G/6G communication systems;
  • Holographic beamforming;
  • Quantum communications, networks, and architecture;
  • Integration of access backhaul networks;
  • Breakthrough technologies and concepts.

We hope this Special Issue will function as a roadmap for information theory and 5G/6G communication systems.

Prof. Wonjong Noh
Prof. Sung Hoon Lim
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. Entropy is an international peer-reviewed open access monthly 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

  • information theory
  • fundamental limits of next-generation communication
  • machine learning- and artificial intelligence-based designs
  • antenna and beamforming designs
  • quantum information theory
  • quantum networks and architectures
  • cell-free communication
  • mmWave and THz communications
  • wirelessly powered communications
  • novel waveform design
  • channel and source code design
  • network architectures for 5G and beyond mobile systems

Published Papers (7 papers)

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Research

13 pages, 679 KiB  
Article
Energy-Efficient Trajectory Optimization for UAV-Based Hybrid FSO/RF Communications with Buffer Constraints
by Rong-Rong Lu, Yang Ma, Sheng-Hong Lin, Bingyuan Zhang, Qinglin Wang and Jin-Yuan Wang
Entropy 2021, 23(12), 1596; https://0-doi-org.brum.beds.ac.uk/10.3390/e23121596 - 28 Nov 2021
Cited by 3 | Viewed by 1846
Abstract
This paper focuses on an unmanned aerial vehicle (UAV) assisted hybrid free-space optical (FSO)/radio frequency (RF) communication system. Considering the rate imbalance between the FSO and RF links, a buffer is employed at the UAV. Initially, theoretical models of energy consumption and throughput [...] Read more.
This paper focuses on an unmanned aerial vehicle (UAV) assisted hybrid free-space optical (FSO)/radio frequency (RF) communication system. Considering the rate imbalance between the FSO and RF links, a buffer is employed at the UAV. Initially, theoretical models of energy consumption and throughput are obtained for the hybrid system. Based on these models, the theoretical expression of the energy efficiency is derived. Then, a nonconvex trajectory optimization problem is formulated by maximizing the energy efficiency of the hybrid system under the buffer constraint, velocity constraint, acceleration constraint, start–end position constraint, and start–end velocity constraint. By using the sequential convex optimization and first-order Taylor approximation, the nonconvex problem is transformed into a convex one. An iterative algorithm is proposed to solve the problem. Numerical results verify the efficiency of the proposed algorithm and also show the effects of buffer size on a UAV’s trajectory. Full article
(This article belongs to the Special Issue Information Theory and 5G/6G Mobile Communications)
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19 pages, 1467 KiB  
Article
Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks
by Zhirong Luan, Hongtao Jia, Ping Wang, Rong Jia and Badong Chen
Entropy 2021, 23(11), 1470; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111470 - 07 Nov 2021
Cited by 6 | Viewed by 1514
Abstract
Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV [...] Read more.
Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment strategies. In this work, we found that these two problems are related by the same performance metric, which makes it possible to optimize the two problems simultaneously. To solve this joint optimization problem, we propose a UAV diffusion deployment algorithm based on the virtual force field method. Firstly, according to the unique performance metric, we define two new virtual forces, which are the UAV-UAV force and UE-UAV force defined by FU and FV, respectively. FV is the main contributor to load balancing and UEs’ data rate fairness, and FU contributes to fine tuning the UEs’ data rate fairness performance. Secondly, we propose a diffusion control stratedy to the update UAV-UAV force, which optimizes FV in a distributed manner. In this diffusion strategy, each UAV optimizes the local parameter by exchanging information with neighbor UAVs, which achieve global load balancing in a distributed manner. Thirdly, we adopt the successive convex optimization method to update FU, which is a non-convex problem. The resultant force of FV and FU is used to control the UAVs’ motion. Simulation results show that the proposed algorithm outperforms the baseline algorithm on UAVs’ load balancing and UEs’ data rate fairness. Full article
(This article belongs to the Special Issue Information Theory and 5G/6G Mobile Communications)
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19 pages, 667 KiB  
Article
Novel Multi-AP Coordinated Transmission Scheme for 7th Generation WLAN 802.11be
by Woojin Ahn
Entropy 2020, 22(12), 1426; https://0-doi-org.brum.beds.ac.uk/10.3390/e22121426 - 17 Dec 2020
Cited by 14 | Viewed by 4364
Abstract
The demand for high-data-rate and time-sensitive applications, such as 4k/8k video streaming and real-time augmented reality (AR), virtual reality (VR), and gaming, has increased significantly. Addressing the inefficiency of distributed channel access and the fairness problem between uplink and downlink flows is crucial [...] Read more.
The demand for high-data-rate and time-sensitive applications, such as 4k/8k video streaming and real-time augmented reality (AR), virtual reality (VR), and gaming, has increased significantly. Addressing the inefficiency of distributed channel access and the fairness problem between uplink and downlink flows is crucial for the development of wireless local area network (WLAN) technologies. In this study, we propose a novel transmission scheme for IEEE 802.11be networks that addresses the fairness problem and improves the system throughput. Utilizing the concept of multi-AP coordinated OFDMA introduced in the 7th-generation WLAN IEEE 802.11be, the proposed transmission scheme allows an AP to share a granted transmission opportunity (TXOP) with nearby APs. A mathematically analysis of the throughput performance of the proposed schemes was performed using a Markov chain model. The simulation results verify that the scheme effectively improves the downlink fairness and the system throughput. Combined with the advanced multiuser (MU) features of IEEE 802.11ax, such as TUA, MU cascading sequence, and MU EDCA, the proposed scheme not only enhances downlink AP transmission, but also guarantees improved control over the medium. The scheme is carefully designed to be fully compatible with conventional IEEE 802.11 protocols, and is thus potentially universal. Full article
(This article belongs to the Special Issue Information Theory and 5G/6G Mobile Communications)
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13 pages, 860 KiB  
Article
Energy Efficiency and Spectral Efficiency Tradeoff in Massive MIMO Multicast Transmission with Statistical CSI
by Bin Jiang, Bowen Ren, Yufei Huang, Tingting Chen, Li You and Wenjin Wang
Entropy 2020, 22(9), 1045; https://0-doi-org.brum.beds.ac.uk/10.3390/e22091045 - 18 Sep 2020
Cited by 5 | Viewed by 3066
Abstract
As the core technology of 5G mobile communication systems, massive multi-input multi-output (MIMO) can dramatically enhance the energy efficiency (EE), as well as the spectral efficiency (SE), which meets the requirements of new applications. Meanwhile, physical layer multicast technology has gradually become the [...] Read more.
As the core technology of 5G mobile communication systems, massive multi-input multi-output (MIMO) can dramatically enhance the energy efficiency (EE), as well as the spectral efficiency (SE), which meets the requirements of new applications. Meanwhile, physical layer multicast technology has gradually become the focus of next-generation communication technology research due to its capacity to efficiently provide wireless transmission from point to multipoint. The availability of channel state information (CSI), to a large extent, determines the performance of massive MIMO. However, because obtaining the perfect instantaneous CSI in massive MIMO is quite challenging, it is reasonable and practical to design a massive MIMO multicast transmission strategy using statistical CSI. In this paper, in order to optimize the system resource efficiency (RE) to achieve EE-SE balance, the EE-SE trade-offs in the massive MIMO multicast transmission are investigated with statistical CSI. Firstly, we formulate the eigenvectors of the RE optimization multicast covariance matrices of different user terminals in closed form, which illustrates that in the massive MIMO downlink, optimal RE multicast precoding is supposed to be done in the beam domain. On the basis of this viewpoint, the optimal RE precoding design is simplified into a resource efficient power allocation problem. Via invoking the quadratic transform, we propose an iterative power allocation algorithm, which obtains an adjustable and reasonable EE-SE tradeoff. Numerical simulation results reveal the near-optimal performance and the effectiveness of our proposed statistical CSI-assisted RE maximization in massive MIMO. Full article
(This article belongs to the Special Issue Information Theory and 5G/6G Mobile Communications)
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16 pages, 685 KiB  
Article
Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
by Huicheol Shin, Yongjae Kim, Seungjae Baek and Yujae Song
Entropy 2020, 22(9), 992; https://0-doi-org.brum.beds.ac.uk/10.3390/e22090992 - 07 Sep 2020
Cited by 4 | Viewed by 2609
Abstract
In this study, the problem of dynamic channel access in distributed underwater acoustic sensor networks (UASNs) is considered. First, we formulate the dynamic channel access problem in UASNs as a multi-agent Markov decision process, wherein each underwater sensor is considered an agent whose [...] Read more.
In this study, the problem of dynamic channel access in distributed underwater acoustic sensor networks (UASNs) is considered. First, we formulate the dynamic channel access problem in UASNs as a multi-agent Markov decision process, wherein each underwater sensor is considered an agent whose objective is to maximize the total network throughput without coordinating with or exchanging messages among different underwater sensors. We then propose a distributed deep Q-learning-based algorithm that enables each underwater sensor to learn not only the behaviors (i.e., actions) of other sensors, but also the physical features (e.g., channel error probability) of its available acoustic channels, in order to maximize the network throughput. We conduct extensive numerical evaluations and verify that the performance of the proposed algorithm is similar to or even better than the performance of baseline algorithms, even when implemented in a distributed manner. Full article
(This article belongs to the Special Issue Information Theory and 5G/6G Mobile Communications)
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14 pages, 501 KiB  
Article
Layered Inter-Cluster Cooperation Scheme for Backhaul-Constrained C-RAN Uplink Systems in the Presence of Inter-Cluster Interference
by Junbeom Kim and Seok-Hwan Park
Entropy 2020, 22(5), 554; https://0-doi-org.brum.beds.ac.uk/10.3390/e22050554 - 15 May 2020
Cited by 1 | Viewed by 2067
Abstract
Despite the potential benefits of reducing system costs and improving spectral efficiency, it is challenging to implement cloud radio access network (C-RAN) systems due to the performance degradation caused by finite-capacity fronthaul links and inter-cluster interference signals. This work studies inter-cluster cooperative reception [...] Read more.
Despite the potential benefits of reducing system costs and improving spectral efficiency, it is challenging to implement cloud radio access network (C-RAN) systems due to the performance degradation caused by finite-capacity fronthaul links and inter-cluster interference signals. This work studies inter-cluster cooperative reception for the uplink of a two-cluster C-RAN system, where two nearby clusters interfere with each other on the uplink access channel. The radio units (RUs) of two clusters forward quantized and compressed version of the uplink received signals to the serving baseband processing units (BBUs) via finite-capacity fronthaul links. The BBUs of the clusters exchange the received fronthaul signals via finite-capacity backhaul links with the purpose of mitigating inter-cluster interference signals. Optimization of conventional cooperation scheme, in which each RU produces a single quantized signal, requires an exhaustive discrete search of exponentially increasing search size with respect to the number of RUs. To resolve this issue, we propose an improved inter-BBU, or inter-cluster, cooperation strategy based on layered compression, where each RU produces two descriptions, of which only one description is forwarded to the neighboring BBU on the backhaul links. We discuss the optimization of the proposed inter-cluster cooperation scheme, and validate the performance gains of the proposed scheme via numerical results. Full article
(This article belongs to the Special Issue Information Theory and 5G/6G Mobile Communications)
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15 pages, 669 KiB  
Article
A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems
by Mahmoud A. Albreem, Mohammed H. Alsharif and Sunghwan Kim
Entropy 2020, 22(4), 388; https://0-doi-org.brum.beds.ac.uk/10.3390/e22040388 - 28 Mar 2020
Cited by 18 | Viewed by 3424
Abstract
Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to [...] Read more.
Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to implement. Unfortunately, they sustain a considerable performance loss in high loaded systems. They also include a matrix inversion which is not hardware-friendly. In addition, if the channel matrix is singular or nearly singular, the system will be classified as an ill-conditioned and hence, the signal cannot be equalized. To defeat the inherent noise enhancement, iterative matrix inversion methods are used in the detectors’ design where approximate matrix inversion is replacing the exact computation. In this paper, we study a linear detector based on iterative matrix inversion methods in realistic radio channels called QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package. Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications. In the QuaDRiGA environment, iterative methods crave large n to obtain a pleasurable performance. This paper also shows that when the ratio between the user antennas and base station (BS) antennas ( β ) is close to 1, iterative matrix inversion methods are not attaining a good detector’s performance. Full article
(This article belongs to the Special Issue Information Theory and 5G/6G Mobile Communications)
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