Special Issue "Innovative Technologies in Telecommunication"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editor

Prof. Dr. Seung-Hoon Hwang
E-Mail
Guest Editor
Div. of Electronics and Electrical Eng., Dongguk University, Seoul, Korea
Interests: wireless communication (5G/6G); internet of things (V2X, Positioning), spectrum engineering, optical wireless communication

Special Issue Information

Dear Colleagues,

5G wireless communication will become a core infrastructure for the fourth industrial revolution (4IR). One of the major objectives of 5G is to meet projected mobile traffic demand and to holistically address the communications needs of most sectors of the economy, including the automotive, manufacturing, media, retail, and consumer sectors. Therefore, innovations in telecommunication with 4IR drive new research opportunities in a variety of areas including artificial intelligence (AI), cloud computing, big data, Internet of Things (IoT), and mobile communications. In this Special Issue, we are particularly interested in describing, defining, and quantifying the potential problems in telecommunications and looking for innovative solutions, prototypes, and demonstrators which may be applied in economic sectors.

Topics of interests include but not limited to:

AI technologies such as machine/deep learning in telecommunication

IoT technologies such as cars, robots, drones, and wearable devices in telecommunication

5G/6G technologies for eMBB, URLLC, and mMTC in telecommunication

Positioning technologies in telecommunication

Spectrum-efficient technologies in telecommunication

Prof. Dr. Seung-Hoon Hwang
Guest Editor

Manuscript Submission Information

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Published Papers (6 papers)

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Research

Open AccessArticle
TCP Acknowledgment Optimization in Low Power and Embedded Devices
Electronics 2021, 10(6), 639; https://doi.org/10.3390/electronics10060639 - 10 Mar 2021
Viewed by 262
Abstract
Paper investigates transport control protocol (TCP) acknowledgment (ACK) optimization in low power or embedded devices to improve their performance on high-speed links by limiting the ACK rate. Today the dominant protocol for interconnecting network devices is the TCP and it has a great [...] Read more.
Paper investigates transport control protocol (TCP) acknowledgment (ACK) optimization in low power or embedded devices to improve their performance on high-speed links by limiting the ACK rate. Today the dominant protocol for interconnecting network devices is the TCP and it has a great influence on the entire network operation if the processing power of network devices is exhausted to the processing data from the TCP stack. Therefore, on high-speed not congested networks the bottleneck is no longer the network link but low-processing power network devices. A new ACK optimization algorithm has been developed and implemented in the Linux kernel. Proposed TCP stack modification minimizes the unneeded technical expenditure from TCP flow by reducing the number of ACKs. The results of performed experiments show that TCP ACK rate limiting leads to the noticeable decrease of CPU utilization on low power devices and an increase of TCP session throughput but does not impact other TCP QoS parameters, such as session stability, flow control, connection management, congestion control or compromises link security. Therefore, more resources of the low-power network devices could be allocated for high-speed data transfer. Full article
(This article belongs to the Special Issue Innovative Technologies in Telecommunication)
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Open AccessArticle
On Coding and Decoding Reconfigurable Radiation Pattern Modulation Symbols
Electronics 2021, 10(5), 614; https://doi.org/10.3390/electronics10050614 - 06 Mar 2021
Viewed by 334
Abstract
In this paper, we propose the theoretical framework for a reconfigurable radiation pattern modulation (RRPM) scheme, which is reminiscent of the index modulation technique. In the proposed scheme, information is encoded using far-field radiation patterns generated by a set of programmable radiating elements. [...] Read more.
In this paper, we propose the theoretical framework for a reconfigurable radiation pattern modulation (RRPM) scheme, which is reminiscent of the index modulation technique. In the proposed scheme, information is encoded using far-field radiation patterns generated by a set of programmable radiating elements. A considerable effort has been invested to allow for high transmission of the reconfigurable radiation pattern symbols; yet, the receiving system has received little attention and has always been considered ideal. Depending on the number of receivers and their respective positions, two variables are considered here for data transmission: the sampling resolution and the fraction of the covered space by the receiving antennas. Hence, we quantitatively investigate their effect on the bit-error-rate (BER) by making use of a limited number of measurements that approximate the behavior of the system under real-field conditions. Full article
(This article belongs to the Special Issue Innovative Technologies in Telecommunication)
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Open AccessArticle
Multi-Winner Spectrum Allocation in Cognitive Radio Networks: A Single-Sided Auction Theoretic Modelling Approach with Sequential Bidding
Electronics 2021, 10(5), 602; https://doi.org/10.3390/electronics10050602 - 05 Mar 2021
Viewed by 344
Abstract
Cognitive radio (CR) has evolved as a novel technology for overcoming the spectrum-scarcity problem in wireless communication networks. With its opportunistic behaviour for improving the spectrum-usage efficiency, CR enables the desired secondary users (SUs) to dynamically utilize the idle spectrum owned by primary [...] Read more.
Cognitive radio (CR) has evolved as a novel technology for overcoming the spectrum-scarcity problem in wireless communication networks. With its opportunistic behaviour for improving the spectrum-usage efficiency, CR enables the desired secondary users (SUs) to dynamically utilize the idle spectrum owned by primary users. On sensing the spectrum to identify the idle frequency bands, proper spectrum-allocation mechanisms need to be designed to provide an effectual use of the radio resource. In this paper, we propose a single-sided sealed-bid sequential-bidding-based auction framework that extends the channel-reuse property in a spectrum-allocation mechanism to efficiently redistribute the unused channels. Existing auction designs primarily aim at maximizing the auctioneer’s revenue, due to which certain CR constraints remain excluded in their models. We address two such constraints, viz. the dynamics in spectrum opportunities and varying availability time of vacant channels, and formulate an allocation problem that maximizes the utilization of the radio spectrum. The auctioneer strategises winner determination based on bids collected from SUs and sequentially leases the unused channels, while restricting the channel assignment to a single-channel-multi-user allocation. To model the spectrum-sharing mechanism, we initially developed a group-formation algorithm that enables the members of a group to access a common channel. Furthermore, the spectrum-allocation and pricing algorithms are operated under constrained circumstances, which guarantees truthfulness in the model. An analysis of the simulation results and comparison with existing auction models revealed the effectiveness of the proposed approach in assigning the unexploited spectrum. Full article
(This article belongs to the Special Issue Innovative Technologies in Telecommunication)
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Open AccessArticle
Side-Information-Aided Preprocessing Scheme for Deep-Learning Classifier in Fingerprint-Based Indoor Positioning
Electronics 2020, 9(6), 982; https://doi.org/10.3390/electronics9060982 - 12 Jun 2020
Cited by 1 | Viewed by 861
Abstract
Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding [...] Read more.
Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding specific conditions may help characterise the data features for the deep-learning classifier and improve the accuracy of indoor positioning. Herein, a side-information-aided preprocessing scheme for deep-learning classifiers is proposed in a dynamic environment, where several groups of different databases are constructed for training multiple classifiers. Therefore, appropriate databases can be employed to effectively improve positioning accuracies. Specifically, two kinds of side information, namely time (morning/afternoon) and direction (forward/backward), are considered when collecting the received signal strength indicator. Simulations and experiments are performed with the deep-learning classifier trained on four different databases. Moreover, these are compared with conventional results from the combined database. The results show that the side-information-aided preprocessing scheme allows better success probability than the conventional method. With two margins, the proposed scheme has 6.55% and 5.8% improved performances for simulations and experiments compared to the conventional scheme. Additionally, the proposed scheme, with time as the side information, obtains a higher success probability when the positioning accuracy requirement is loose with larger margin. With direction as the side information, the proposed scheme shows better performance for high positioning precision requirements. Thus, side information such as time or direction is advantageous for preprocessing data in deep-learning classifiers for fingerprint-based indoor positioning. Full article
(This article belongs to the Special Issue Innovative Technologies in Telecommunication)
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Open AccessArticle
Joint Scheduling and Power Allocation Using Non-Orthogonal Multiple Access in Multi-Cell Beamforming Networks
Electronics 2020, 9(6), 896; https://doi.org/10.3390/electronics9060896 - 28 May 2020
Viewed by 577
Abstract
The proliferation of smart devices has boosted the improvement of wireless network technologies. Herein, networking functions should be properly guaranteed even in highly dense environments in terms of service quality and data rate. In this paper, we present an efficient power allocation algorithm [...] Read more.
The proliferation of smart devices has boosted the improvement of wireless network technologies. Herein, networking functions should be properly guaranteed even in highly dense environments in terms of service quality and data rate. In this paper, we present an efficient power allocation algorithm using non-orthogonal multiple access and smart array antennas to increase the capacity in highly overlapped multi-cell environments. We evaluate the proposed algorithm and compare with the conventional orthogonal multiple access scheme with smart antennas. Through intensive simulations and experiments at the system level for performance evaluations, it is confirmed that the proposed scheme obtains a drastic throughput gain up to 50% in the overlapped region of highly dense networks. Full article
(This article belongs to the Special Issue Innovative Technologies in Telecommunication)
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Open AccessFeature PaperArticle
Improved RSSI-Based Data Augmentation Technique for Fingerprint Indoor Localisation
Electronics 2020, 9(5), 851; https://doi.org/10.3390/electronics9050851 - 21 May 2020
Cited by 2 | Viewed by 861
Abstract
Recently, deep-learning-based indoor localisation systems have attracted attention owing to their higher performance compared with traditional indoor localization systems. However, to achieve satisfactory performance, the former systems require large amounts of data to train deep learning models. Since obtaining the data is usually [...] Read more.
Recently, deep-learning-based indoor localisation systems have attracted attention owing to their higher performance compared with traditional indoor localization systems. However, to achieve satisfactory performance, the former systems require large amounts of data to train deep learning models. Since obtaining the data is usually a tedious task, this requirement deters the use of deep learning approaches. To address this problem, we propose an improved data augmentation technique based on received signal strength indication (RSSI) values for fingerprint indoor positioning systems. The technique is implemented using available RSSI values at one reference point, and unlike existing techniques, it mimics the constantly varying RSSI signals. With this technique, the proposed method achieves a test accuracy of 95.26% in the laboratory simulation and 94.59% in a real-time environment, and the average location error is as low as 1.45 and 1.60 m, respectively. The method exhibits higher performance compared with an existing augmentation method. In particular, the data augmentation technique can be applied irrespective of the positioning algorithm used. Full article
(This article belongs to the Special Issue Innovative Technologies in Telecommunication)
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