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22 pages, 680 KiB

Open AccessArticle

Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

by Gianmarco Baldini and Fausto Bonavitacola

Algorithms 2023, 16(6), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/a16060277 - 30 May 2023

Viewed by 1539

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification [...] Read more.

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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14 pages, 522 KiB

Open AccessArticle

BCCC Disjoint Path Construction Algorithm and Fault-Tolerant Routing Algorithm under Restricted Connectivity

by Jialiang Lu, Xiaoyu Du, Huiping Li and Zhijie Han

Algorithms 2022, 15(12), 481; https://0-doi-org.brum.beds.ac.uk/10.3390/a15120481 - 17 Dec 2022

Viewed by 1132

Abstract

Connectivity in large-scale data center networks is a critical indicator to evaluate network state. A feasible and performance-guaranteed algorithm enables us to find disjoint paths between network vertices to ensure effective data transfer and to maintain the normal operation of network in case [...] Read more.

Connectivity in large-scale data center networks is a critical indicator to evaluate network state. A feasible and performance-guaranteed algorithm enables us to find disjoint paths between network vertices to ensure effective data transfer and to maintain the normal operation of network in case of faulty nodes. As an important data center network, BCube Connected Crossbars (BCCC) has many excellent properties that have been widely studied. In this paper, we first propose a vertex disjoint path algorithm with the time complexity of

O (n k)

in BCCC, where n denotes a switch connected to n servers and k denotes dimension. Then, we prove that the restricted connectivity of

B C C C (n, k)

. Finally, we present an

O (k n κ^{1} (G))

fault-free algorithm in BCCC, where

κ^{1} (G)

is the restricted connectivity. This algorithm can obtain a fault-free path between any two distinct fault-free vertices under the condition that each vertex has at least one fault-free neighbor in the BCCC and a set of faulty vertices F with

| F | < κ^{1} (G)

. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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17 pages, 1176 KiB

Open AccessArticle

Fast Conflict Detection for Multi-Dimensional Packet Filters

by Chun-Liang Lee, Guan-Yu Lin and Yaw-Chung Chen

Algorithms 2022, 15(8), 285; https://0-doi-org.brum.beds.ac.uk/10.3390/a15080285 - 14 Aug 2022

Cited by 1 | Viewed by 1525

Abstract

To support advanced network services, Internet routers must perform packet classification based on a set of rules called packet filters. If two or more filters overlap, a filter conflict will occur and lead to ambiguity in packet classification. Further, it may affect network [...] Read more.

To support advanced network services, Internet routers must perform packet classification based on a set of rules called packet filters. If two or more filters overlap, a filter conflict will occur and lead to ambiguity in packet classification. Further, it may affect network security or even the correctness of packet routing. Hence, it is necessary to detect conflicts to avoid the above problems. In recent years, many conflict detection algorithms have been proposed, but most of them detect conflicts for only prefix fields (i.e., source/destination IP address fields) of filters. For greater practicality, conflict detection must include non-prefix fields such as source/destination IP port fields and the protocol field. In this study, we propose an efficient conflict detection algorithm for five-dimensional filters, which include both prefix and non-prefix fields. In the proposed algorithm, a tiny lookup table is created for quickly filtering out a large portion of non-conflicting filter pairs, thereby reducing the overall conflict detection time. Experimental results show that our algorithm reduces the detection time by 10% to 28% compared with other conflict detection algorithms for 20 K filter databases. More importantly, our algorithm can be used to extend any existing conflict detection algorithms for two-dimensional filters to support fast conflict detection for five-dimensional filters. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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23 pages, 4032 KiB

Open AccessArticle

Improving Traffic Load Distribution Fairness in Mobile Social Networks

by Bambang Soelistijanto and Vittalis Ayu

Algorithms 2022, 15(7), 222; https://0-doi-org.brum.beds.ac.uk/10.3390/a15070222 - 22 Jun 2022

Cited by 1 | Viewed by 1607

Abstract

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most [...] Read more.

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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15 pages, 1352 KiB

Open AccessArticle

Adaptive Authentication Protocol Based on Zero-Knowledge Proof

by Nikita Konstantinovich Chistousov, Igor Anatolyevich Kalmykov, Daniil Vyacheslavovich Dukhovnyj, Maksim Igorevich Kalmykov and Aleksandr Anatolyevich Olenev

Algorithms 2022, 15(2), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/a15020050 - 30 Jan 2022

Cited by 6 | Viewed by 3410

Abstract

Authentication protocols are expanding their application scope in wireless information systems, among which are low-orbit satellite communication systems (LOSCS) for the OneWeb space Internet, automatic object identification systems using RFID, the Internet of Things, intelligent transportation systems (ITS), Vehicular Ad Hoc Network (VANET). [...] Read more.

Authentication protocols are expanding their application scope in wireless information systems, among which are low-orbit satellite communication systems (LOSCS) for the OneWeb space Internet, automatic object identification systems using RFID, the Internet of Things, intelligent transportation systems (ITS), Vehicular Ad Hoc Network (VANET). This is due to the fact that authentication protocols effectively resist a number of attacks on wireless data transmission channels in these systems. The main disadvantage of most authentication protocols is the use of symmetric and asymmetric encryption systems to ensure high cryptographic strength. As a result, there is a problem in delivering keys to the sides of the prover and the verifier. At the same time, compromising of keys will lead to a decrease in the level of protection of the transmitted data. Zero-knowledge authentication protocols (ZKAP) are able to eliminate this disadvantage. However, most of these protocols use multiple rounds to authenticate the prover. Therefore, ZKAP, which has minimal time costs, is developed in the article. A scheme for adapting protocol parameters has been developed in this protocol to increase its efficiency. Reductions in the level of confidentiality allow us to reduce the time spent on the execution of the authentication protocol. This increases the volume of information traffic. At the same time, an increase in the confidentiality of the protocol entails an increase in the time needed for authentication of the prover, which reduces the volume of information traffic. The FPGA Artix-7 xc7a12ticsg325-1L was used to estimate the time spent implementing the adaptive ZKAP protocol. Testing was performed for 32- and 64-bit adaptive authentication protocols. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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13 pages, 374 KiB

Open AccessArticle

Resource Allocation for Intelligent Reflecting Surfaces Assisted Federated Learning System with Imperfect CSI

by Wei Huang, Zhiren Han, Li Zhao, Hongbo Xu, Zhongnian Li and Ze Wang

Algorithms 2021, 14(12), 363; https://0-doi-org.brum.beds.ac.uk/10.3390/a14120363 - 14 Dec 2021

Cited by 3 | Viewed by 2448

Abstract

Due to its ability to significantly improve the wireless communication efficiency, the intelligent reflective surface (IRS) has aroused widespread research interest. However, it is a challenge to obtain perfect channel state information (CSI) for IRS-related channels due to the lack of the ability [...] Read more.

Due to its ability to significantly improve the wireless communication efficiency, the intelligent reflective surface (IRS) has aroused widespread research interest. However, it is a challenge to obtain perfect channel state information (CSI) for IRS-related channels due to the lack of the ability to send, receive, and process signals at IRS. Since most of the existing channel estimation methods are developed to obtain cascaded base station (BS)-IRS-user devices (UDs) channel, this paper studies the problem of computation and communication resource allocation of the IRS-assisted federated learning (FL) system based on the imperfect CSI. Specifically, we take the statistical CSI error model into consideration and formulate the training time minimization problem subject to the rate outage probability constraints. In order to solve this issue, the semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP) are invoked to transform it into a convex problem. Subsequently, a low-complexity algorithm is proposed to minimize the delay of the FL system. Numerical results show that the proposed algorithm effectively reduces the training time of the FL system base on imperfect CSI. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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18 pages, 814 KiB

Open AccessArticle

Decomposition of Random Sequences into Mixtures of Simpler Ones and Its Application in Network Analysis

by András Faragó

Algorithms 2021, 14(11), 336; https://0-doi-org.brum.beds.ac.uk/10.3390/a14110336 - 19 Nov 2021

Cited by 1 | Viewed by 2023

Abstract

A classic and fundamental result about the decomposition of random sequences into a mixture of simpler ones is de Finetti’s Theorem. In its original form, it applies to infinite 0–1 valued sequences with the special property that the distribution is invariant to permutations [...] Read more.

A classic and fundamental result about the decomposition of random sequences into a mixture of simpler ones is de Finetti’s Theorem. In its original form, it applies to infinite 0–1 valued sequences with the special property that the distribution is invariant to permutations (called an exchangeable sequence). Later it was extended and generalized in numerous directions. After reviewing this line of development, we present our new decomposition theorem, covering cases that have not been previously considered. We also introduce a novel way of applying these types of results in the analysis of random networks. For self-containment, we provide the introductory exposition in more detail than usual, with the intent of making it also accessible to readers who may not be closely familiar with the subject. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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28 pages, 1344 KiB

Open AccessEditor’s ChoiceReview

Overview of Distributed Machine Learning Techniques for 6G Networks

by Eugenio Muscinelli, Swapnil Sadashiv Shinde and Daniele Tarchi

Algorithms 2022, 15(6), 210; https://0-doi-org.brum.beds.ac.uk/10.3390/a15060210 - 15 Jun 2022

Cited by 22 | Viewed by 3837

Abstract

The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to [...] Read more.

The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to serve many heterogeneous wireless devices. Various machine learning (ML) techniques are expected to be deployed over the intelligent 6G wireless network that provide solutions to highly complex networking problems. In order to do this, various 6G nodes and devices are expected to generate tons of data through external sensors, and data analysis will be needed. With such massive and distributed data, and various innovations in computing hardware, distributed ML techniques are expected to play an important role in 6G. Though they have several advantages over the centralized ML techniques, implementing the distributed ML algorithms over resource-constrained wireless environments can be challenging. Therefore, it is important to select a proper ML algorithm based upon the characteristics of the wireless environment and the resource requirements of the learning process. In this work, we survey the recently introduced distributed ML techniques with their characteristics and possible benefits by focusing our attention on the most influential papers in the area. We finally give our perspective on the main challenges and advantages for telecommunication networks, along with the main scenarios that could eventuate. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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