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Emerging Wireless Communication Technologies in Industrial IoT Networks
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Emerging Wireless Communication Technologies in Industrial IoT Networks

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

Deadline for manuscript submissions: closed (15 August 2024) | Viewed by 13623

Special Issue Editors

Prof. Dr. Kai Niu

E-Mail Website
Guest Editor
The Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China
Interests: information theory and channel coding; signal processing based on machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yue Gao

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Surrey, Guildford, UK
Interests: smart antennas; signal processing; spectrum sharing; millimetre-wave ; Internet of Things technologies in mobile ;satellite systems
Prof. Dr. Yi Fang

E-Mail Website
Guest Editor
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: channel coding; coded modulation; wireless communication; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Yin

E-Mail Website
Guest Editor
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China
Interests: Bayesian learning and optimization; big data; distributed algorithms; data-driven wireless system; wireless localization and tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Recent years have witnessed exciting developments for Industrial Internet of Things (IoT) networks composed of numerous sensors. Wireless communication technologies play an important role in industrial IoT networks. The wireless transmission and the network organization among the large number of sensors become critical technique challenges. On one hand, in order to achieve a high compression ratio, low latency, and ultra-high reliability, new signal compression and transmission techniques, such as semantic compression and massive access, are emerging. On the other hand, in order to improve the system throughput and lower the energy consumption, new network architectures and design schemes based on machine learning have become popular. For this reason, it is necessary to investigate wireless communication technologies in industrial networks. This Special Issue will focus on the new wireless transmission and network techniques in industrial networks. We invite you to submit your original contributions on, but not limited to, semantic information compression, powerful channel coding/modulation, massive access, new network architectures, and optimization schemes based on machine learning/deep learning in industrial IoT networks.

Topics of interest include, but are not limited to, the following:

  • Joint source and channel coding in industrial IoT networks;
  • Semantic-based compression and communication in industrial IoT networks;
  • Coding and modulation with a short length in industrial IoT networks;
  • Massive access or large-scale access to industrial networks;
  • AI-based massive sensor network localization;
  • Performance analysis based on complex networks in industrial IoT networks;
  • mmWave techniques in industrial IoT networks;
  • Low power consumption architecture for industrial networks;
  • New optimization schemes based on machine learning for industrial networks.

Prof. Dr. Kai Niu
Prof. Dr. Yue Gao
Prof. Dr. Yi Fang
Dr. Feng Yin
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

  • joint source and channel coding
  • semantic-based compression and communication
  • LDPC codes
  • polar codes
  • massive access
  • massive network localization
  • coded slotted ALOHA
  • optimization of complex networks
  • AI-based optimization
  • green communications
  • low power consumption
  • mmWave

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Published Papers (6 papers)

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Research

24 pages, 12127 KiB  
Article
Energy-Efficient Dynamic Enhanced Inter-Cell Interference Coordination Scheme Based on Deep Reinforcement Learning in H-CRAN
by Hyungwoo Choi, Taehwa Kim, Seungjin Lee, Hoan-Suk Choi and Namhyun Yoo
Sensors 2024, 24(24), 7980; https://doi.org/10.3390/s24247980 - 13 Dec 2024
Viewed by 852
Abstract
The proliferation of 5G networks has revolutionized wireless communication by delivering enhanced speeds, ultra-low latency, and widespread connectivity. However, in heterogeneous cloud radio access networks (H-CRAN), efficiently managing inter-cell interference while ensuring energy conservation remains a critical challenge. This paper presents a novel [...] Read more.
The proliferation of 5G networks has revolutionized wireless communication by delivering enhanced speeds, ultra-low latency, and widespread connectivity. However, in heterogeneous cloud radio access networks (H-CRAN), efficiently managing inter-cell interference while ensuring energy conservation remains a critical challenge. This paper presents a novel energy-efficient, dynamic enhanced inter-cell interference coordination (eICIC) scheme based on deep reinforcement learning (DRL). Unlike conventional approaches that focus primarily on optimizing parameters such as almost blank subframe (ABS) ratios and bias offsets (BOs), our work introduces the transmission power during ABS subframes (TPA) and the channel quality indicator (CQI) threshold of victim user equipments (CTV) into the optimization process. Additionally, this approach uniquely integrates energy consumption into the scheme, addressing both performance and sustainability concerns. By modeling key factors such as signal-to-interference-plus-noise ratio (SINR) and service rates, we introduce the concept of energy-utility efficiency to balance energy savings with quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves up to 70% energy savings while enhancing QoS satisfaction, showcasing its potential to significantly improve the efficiency and sustainability of future 5G H-CRAN deployments. Full article
(This article belongs to the Special Issue Emerging Wireless Communication Technologies in Industrial IoT Networks)
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17 pages, 3980 KiB  
Article
Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
by Shirong Guo, Jielin Yao, Pingfan Wu, Jianjie Yang, Wenhao Wu and Zhijian Lin
Sensors 2023, 23(3), 1525; https://doi.org/10.3390/s23031525 - 30 Jan 2023
Cited by 1 | Viewed by 2770
Abstract
With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a [...] Read more.
With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types. Full article
(This article belongs to the Special Issue Emerging Wireless Communication Technologies in Industrial IoT Networks)
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17 pages, 807 KiB  
Article
Joint Intra/Inter-Slot Code Design for Unsourced Multiple Access in 6G Internet of Things
by Yuanjie Li, Kai Niu, Chao Dong, Shiqiang Suo and Jiaru Lin
Sensors 2023, 23(1), 242; https://doi.org/10.3390/s23010242 - 26 Dec 2022
Viewed by 2205
Abstract
Unsourced multiple access (UMA) is the technology for massive, low-power, and uncoordinated Internet-of-Things in the 6G wireless system, improving connectivity and energy efficiency on guaranteed reliability. The multi-user coding scheme design is a critical problem for UMA. This paper proposes a UMA coding [...] Read more.
Unsourced multiple access (UMA) is the technology for massive, low-power, and uncoordinated Internet-of-Things in the 6G wireless system, improving connectivity and energy efficiency on guaranteed reliability. The multi-user coding scheme design is a critical problem for UMA. This paper proposes a UMA coding scheme based on the T-Fold IRSA (irregular repetition slotted Aloha) paradigm by using joint Intra/inter-slot code design and optimization. Our scheme adopts interleave-division multiple access (IDMA) to enhance the intra-slot coding gain and the low-complexity joint intra/inter-slot SIC (successive interference cancellation) decoder structure to recover multi-user payloads. Based on the error event decomposition and density evolution analysis, we build a joint intra/inter-slot coding parameter optimization algorithm to minimize the SNR (signal-to-noise ratio) requirement at an expected system packet loss rate. Numerical results indicate that the proposed scheme achieves energy efficiency gain by balancing the intra/inter-slot coding gain while maintaining relatively low implementation complexity. Full article
(This article belongs to the Special Issue Emerging Wireless Communication Technologies in Industrial IoT Networks)
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10 pages, 383 KiB  
Article
rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
by Xiaoxiong Xiong, Chao Dong and Kai Niu
Sensors 2022, 22(21), 8291; https://doi.org/10.3390/s22218291 - 29 Oct 2022
Cited by 2 | Viewed by 1640
Abstract
In this paper, a new system entropy measure is used to optimize the routing algorithm in power consumption. We introduce the system entropy measure into the problem of industrial wireless sensor networks (iWSNs) routing and propose a high-performance routing algorithm guided by the [...] Read more.
In this paper, a new system entropy measure is used to optimize the routing algorithm in power consumption. We introduce the system entropy measure into the problem of industrial wireless sensor networks (iWSNs) routing and propose a high-performance routing algorithm guided by the system entropy measure (rSEM). Based on the cluster iWSNs architecture, the rSEM selects the cluster heads and cluster member nodes successively, according to the system entropy measure, and constructs the iWSNs with the minimum system entropy. The method of the cluster head selection is traversal, while the method of the cluster member selection is a greedy algorithm to reduce the complexity. The experiments show that the power consumption of the iWSNs generated by the rSEM is in the same order of magnitude as that of Dijkstra in both 2D and 3D scenarios. In addition, the delay of the rSEM is slightly higher than that of LEACH. Therefore, the rSEM is suitable for networks that are sensitive to both the delay and power consumption. The rSEM puts forward a new idea for the design of routing for the next-generation iWSNs, which improves the overall network performance according to the network topology, instead of relying on the power consumption or delay performance only. Full article
(This article belongs to the Special Issue Emerging Wireless Communication Technologies in Industrial IoT Networks)
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15 pages, 493 KiB  
Article
Constructing Grith Eight GC-LDPC Codes Based on the GCD FLRM Matrix with a New Lower Bound
by Kun Zhu and Hongwen Yang
Sensors 2022, 22(19), 7335; https://doi.org/10.3390/s22197335 - 27 Sep 2022
Cited by 5 | Viewed by 2090
Abstract
By connecting multiple short, local low-density parity-check (LDPC) codes with a global parity check, the globally coupled (GC) LDPC code can attain high performances with low complexities. The typical design of a local code is a quasi-cyclic (QC) LDPC for which the code [...] Read more.
By connecting multiple short, local low-density parity-check (LDPC) codes with a global parity check, the globally coupled (GC) LDPC code can attain high performances with low complexities. The typical design of a local code is a quasi-cyclic (QC) LDPC for which the code length is proportional to the size of circulant permutation matrix (CPM). The greatest common divisor (GCD)-based full-length row multiplier (FLRM) matrix is constrained by a lower bound of CPM size to avoid six length cycles. In this paper, we find a new lower bound for the CPM size and propose an algorithm to determine the minimum CPM size and the corresponding FLRM matrix. Based on the new lower bound, two methods are proposed to construct the GC-QC-LDPC code of grith 8 based on the GCD based FLRM matrix. With the proposed algorithm, the CPM size can be 45% less than that given by sufficient condition of girth 8. Compared with the conventional GC-LDPC construction, the codes constructed with the proposed method have improved performance and are more flexible in code length and code rate design. Full article
(This article belongs to the Special Issue Emerging Wireless Communication Technologies in Industrial IoT Networks)
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16 pages, 5984 KiB  
Article
High Throughput Priority-Based Layered QC-LDPC Decoder with Double Update Queues for Mitigating Pipeline Conflicts
by Yunfeng Li, Yingchun Li, Nan Ye, Tianyang Chen, Zhijie Wang and Junjie Zhang
Sensors 2022, 22(9), 3508; https://doi.org/10.3390/s22093508 - 5 May 2022
Cited by 6 | Viewed by 2679
Abstract
A high-throughput layered decoder for quasi-cyclic (QC) low-density parity-check (LDPC) codes is required for communication systems. The preferred way to improve the throughput is to insert pipeline stages and increase the operating frequency, which suffers from pipeline conflicts at the same time. A [...] Read more.
A high-throughput layered decoder for quasi-cyclic (QC) low-density parity-check (LDPC) codes is required for communication systems. The preferred way to improve the throughput is to insert pipeline stages and increase the operating frequency, which suffers from pipeline conflicts at the same time. A priority-based layered schedule is proposed to keep the updates of log-likelihood ratios (LLRs) as frequent as possible when pipeline conflicts happen. To reduce pipeline conflicts, we also propose double update queues for layered decoders. The proposed double update queues improve the percentage of updated LLRs per iteration. Benefitting from these, the performance loss of the proposed decoder for the fifth generation (5G) new radio (NR) is reduced from 0.6 dB to 0.2 dB using the same quantization compared with the state-of-the-art work. As a result, the throughput of the proposed decoder improved up to 2.85 times when the signal-to-noise ratio (SNR) was equal to 5.9 dB. Full article
(This article belongs to the Special Issue Emerging Wireless Communication Technologies in Industrial IoT Networks)
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