Applied Mathematics for 5th Generation (5G) and beyond Communication Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: closed (29 April 2022) | Viewed by 28024

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Guest Editor
Department of Information and Communication Engineering, Sejong University, 05006 209, Neungdong-ro, Gwangjin-gu, Seoul, Korea
Interests: embedded systems; wireless networks; artificial intelligence; Internet of Things
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Special Issue Information

Dear Colleagues,

Mobile communication systems have a great importance in today’s world. Since the introduction of the global system for mobile communications (GSM) in the early 1990s, till today’s more advanced 5th generation (5G) communications system, it has had an enormous impact on people’s social life. Its importance will continue to climb with new fields of application, for example, the Internet of things (IoT), Tactile Internet, ultra-reliable low latency communication (URLLC) with astonishingly high throughput. Such advanced communication systems are continually being introduced to live up to the great variety of applications and desires for new services. This development would have been impossible without the digitalization of communication systems, wherein mathematics played a vital supporting role. In fact, mathematics is the foundation of information and communication theory, the pathbreaker in the development of new transmission procedures, and an essential instrument in the planning and optimization of networks. For example, statistical theories and discrete mathematics are used in information theory; linear algebra, convex optimization and game theory are used in the development of new methods of transmission; and linear, combinatorial, and stochastic optimization are used in planning radio networks. Despite this immense progress, essential foundational issues and challenges for the implementation of machine intelligence in 5G and beyond communication systems are still to be determined. Examples include a network information theory based on machine learning (ML) techniques (such as supervised learning, unsupervised learning, federated learning, and reinforcement learning) for more complex multi-user 5G and beyond systems, a quantum information theory for miniaturized systems, a theory of self-organizing intelligent networks, and the development of new paradigms for the use of the cognitive radio networks. In all these cases it is the collaboration of applied mathematics and the communications engineering that must deliver the essential contributions to the potential solutions.

We propose in this Special Issue to focus on those applied mathematical models and methods that reflect the specific features of 5G and beyond communication systems that differ from past and present ones. We may deliberate on diverse mathematical approaches for machine intelligence based on stochastic processes, queueing theory, statistics, stochastic geometry, and others.

Dr. Rashid Ali
Prof. Dr. Hyung Seok Kim
Guest Editors

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Editorial

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2 pages, 169 KiB  
Editorial
Applied Mathematics for 5th Generation (5G) and beyond Communication Systems
by Rashid Ali and Hyung Seok Kim
Mathematics 2022, 10(16), 2946; https://doi.org/10.3390/math10162946 - 15 Aug 2022
Viewed by 1044
Abstract
Mobile communication systems have a great importance in today’s world [...] Full article

Research

Jump to: Editorial

22 pages, 1551 KiB  
Article
Collision-Based Window-Scaled Back-Off Mechanism for Dense Channel Resource Allocation in Future Wi-Fi
by Abdul Rehman, Faisal Bashir Hussain, Jawad Tanveer and Amir Haider
Mathematics 2022, 10(12), 2053; https://doi.org/10.3390/math10122053 - 14 Jun 2022
Cited by 2 | Viewed by 2170
Abstract
Wireless local area networks (WLANs), known as Wi-Fi, are widely deployed to meet the enhanced needs of data-centric internet applications, such as wireless docking, unified communications, cloud computing, interactive multimedia gaming, progressive streaming, support of wearable devices, up-link broadcasts and cellular offloading. Wi-Fi [...] Read more.
Wireless local area networks (WLANs), known as Wi-Fi, are widely deployed to meet the enhanced needs of data-centric internet applications, such as wireless docking, unified communications, cloud computing, interactive multimedia gaming, progressive streaming, support of wearable devices, up-link broadcasts and cellular offloading. Wi-Fi networks typically adopt the Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which uses the Binary Exponential Back-off (BEB) algorithm at the MAC layer mechanism to access channel resources. Currently deployed Wi-Fi networks face huge challenges towards efficient channel access for denser environments due to the blind exponential increase/decrease of a contention window (CW) procedure that is inefficient for a higher number of contending stations. Several modifications and amendments have been proposed to improve the performance of the MAC layer channel access based on a fixed or variable CW size. However, a more realistic network density-based channel resource allocation solution is still missing. An efficient channel resource allocation is one of the most critical challenges for future highly dense WLANs, such as High-Efficiency WLAN (HEW). In this paper, we propose a Channel Collision-based Window Scaled Back-off (CWSB) mechanism for channel resource allocation in HEW. In our proposed CWSB, all contending stations select an optimized CW size for each back-off stage for collided or successfully transmitted data frames. We affirm the performance of the proposed CWSB mechanism with the help of an Iterative Discrete Time Markov Chain (I-DTMC) model. This paper evaluates the performance of our proposed CWSB mechanism in HEW Wi-Fi networks using an NS3 simulator in terms of the normalized throughput and channel access delay compared to the state-of-the-art BEB and a recently proposed mechanism. Full article
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11 pages, 463 KiB  
Article
Performance Analysis of Regularized Convex Relaxation for Complex-Valued Data Detection
by Ayed M. Alrashdi and Houssem Sifaou
Mathematics 2022, 10(9), 1585; https://doi.org/10.3390/math10091585 - 7 May 2022
Cited by 1 | Viewed by 1169
Abstract
In this work, we study complex-valued data detection performance in massive multiple-input multiple-output (MIMO) systems. We focus on the problem of recovering an n-dimensional signal whose entries are drawn from an arbitrary constellation KC from m noisy linear measurements, with [...] Read more.
In this work, we study complex-valued data detection performance in massive multiple-input multiple-output (MIMO) systems. We focus on the problem of recovering an n-dimensional signal whose entries are drawn from an arbitrary constellation KC from m noisy linear measurements, with an independent and identically distributed (i.i.d.) complex Gaussian channel. Since the optimal maximum likelihood (ML) detector is computationally prohibitive for large dimensions, many convex relaxation heuristic methods have been proposed to solve the detection problem. In this paper, we consider a regularized version of this convex relaxation that we call the regularized convex relaxation (RCR) detector and sharply derive asymptotic expressions for its mean square error and symbol error probability. Monte-Carlo simulations are provided to validate the derived analytical results. Full article
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21 pages, 1420 KiB  
Article
Internet of Drones: Routing Algorithms, Techniques and Challenges
by Syed Kamran Haider, Ali Nauman, Muhammad Ali Jamshed, Aimin Jiang, Sahar Batool and Sung Won Kim
Mathematics 2022, 10(9), 1488; https://doi.org/10.3390/math10091488 - 29 Apr 2022
Cited by 18 | Viewed by 3418
Abstract
In the past decades, unmanned aerial vehicles (UAVs), also known as drones, have drawn more attention in the academic domain and exploration in the research fields of wireless sensor networks (WSNs). Moreover, applications of drones aid operations related to military support, agriculture industry, [...] Read more.
In the past decades, unmanned aerial vehicles (UAVs), also known as drones, have drawn more attention in the academic domain and exploration in the research fields of wireless sensor networks (WSNs). Moreover, applications of drones aid operations related to military support, agriculture industry, and smart Internet-of-Things (IoT). Currently, the use of drone based IoT, also known as Internet-of-Drones (IoD), and their design challenges and techniques are being probed by researchers around the globe. The placement of drones (nodes) is an important consideration in a IoD environment and is closely related to the properties of IoT. Given a base station (BS), sensor nodes (SNs) and IoT devices are designed to capture the signals transmitted by the BS and make use of internet connectivity in a manner to facilitate users. Mutual benefit can be achieved by integrating drones into IoT. The drone based cluster models are not free from challenges. Routing protocols have to be substantiated by key algorithms. Drones are designed to be specific to applications, but the underlying principles are the same. Optimization algorithms are the gateway to better accuracy, performance, and reliability. This article discusses some of these optimization algorithms, include genetic algorithm (GA), bee optimization algorithm, and Chicken Swarm Optimization Clustering Algorithm (CSOCA). Finally, the routing schemes, protocols, and challenges in the context of IoD are discussed. Full article
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15 pages, 6612 KiB  
Article
An Efficient Network Intrusion Detection and Classification System
by Iftikhar Ahmad, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi and Rayed A. AlGhamdi
Mathematics 2022, 10(3), 530; https://doi.org/10.3390/math10030530 - 8 Feb 2022
Cited by 28 | Viewed by 4242
Abstract
Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is [...] Read more.
Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. Full article
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15 pages, 782 KiB  
Article
Sliding Group Window with Rebacking off for Collision Avoidance in High-Efficiency Wireless Networks
by Alaa Omran Almagrabi, Rashid Ali, Yasser Difulah Al-Otaibi, Hadi Mohsen Oqaibi and Tahir Khurshaid
Mathematics 2021, 9(19), 2461; https://doi.org/10.3390/math9192461 - 2 Oct 2021
Cited by 1 | Viewed by 1500
Abstract
It is difficult for wireless local area networks (WLANs), IEEE 802.11ax high-efficiency WLAN (HEW), to join next-generation innovations such as 5th generation (5G) and Internet of Things (IoT) because they still have their conventional channel access mechanism as their essential medium access control [...] Read more.
It is difficult for wireless local area networks (WLANs), IEEE 802.11ax high-efficiency WLAN (HEW), to join next-generation innovations such as 5th generation (5G) and Internet of Things (IoT) because they still have their conventional channel access mechanism as their essential medium access control (MAC) protocol. The MAC protocol uses a traditional binary exponential backoff (BEB) algorithm to access channel resources that depend on the noncognitive increment of contention parameters for collision avoidance. In BEB, the collision issue increases with the increase in connected devices in the network due to a fixed contention window size. The larger the size of the network, the larger the collision in the network. To avoid such a circumstance, in this paper, we propose a sliding group window (sGW) mechanism dependent on collision-point assessment in order to improve the performance of MAC protocol for HEW. The proposed algorithm additionally presents a rebacking off for collision avoidance (ReBOCA) system for sGW, which combines the uniform dispersion of the contention parameters. This variation of an ordinary backoff algorithm permits the reasonable sliding of the user groups in the case of collision. The algorithm explicitly accounts for the peculiarities of dense environments and backward compatibility. Key aspects of the proposed solution include collision-point estimation, rebacking off for collision distribution convergence for fair treatment, and adaptive sliding of group windows to mitigate contention unfairness. We further formulated a closed-form Markov chain model for the performance analysis of our proposed sGW with ReBOCA scheme. Theoretical and practical results prove that our proposed scheme achieved maximal efficiency, even under dense environments. An increase in throughput with a lower packet collision probability was achieved with the proposed mechanism, and the efficiency increased as the number of contending stations increased than compared to traditional BEB performance. Our proposed ReBOCA mechanism enhanced network throughput by 38.18% than compared to the conventional BEB mechanism. Full article
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32 pages, 4630 KiB  
Article
On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
by Areeba Ayesha, MuhibUr Rahman, Amir Haider and Shabbir Majeed Chaudhry
Mathematics 2021, 9(12), 1434; https://doi.org/10.3390/math9121434 - 20 Jun 2021
Cited by 10 | Viewed by 3074
Abstract
One of the major impediments in the design and operation of a full-duplex radio transceiver is the presence of self-interference (SI), that is, the transceiver’s transmitted signal, 60–100 dB stronger than the desired signal of interest. To reduce the SI signal below the [...] Read more.
One of the major impediments in the design and operation of a full-duplex radio transceiver is the presence of self-interference (SI), that is, the transceiver’s transmitted signal, 60–100 dB stronger than the desired signal of interest. To reduce the SI signal below the receiver’s sensitivity before coupling it to the receiver, radio frequency (RF)/analog domain cancellation is carried out. Even after SI cancellation to the required level in the analog domain, the residual SI signal still exits and lowers the transceiver’s performance. For residual SI cancellation, a digital domain cancellation is carried out. RF impairments are the major obstacle in the residual SI cancellation path in the digital domain. Linearization of RF impairments such as IQ mixer imbalance in the transmitter and receiver chain, non-linear PA with memory, and non-linear LNA are also carried out. Performance evaluation of the proposed techniques is carried out based on SINR, the power of different SI signal components, PSD, output to input relationship, SNR vs. BER, spectrum analyzer, constellation diagram, and link budget analysis. The proposed techniques provide attractive RF/analog SI cancellation of up to 80–90 dB, digital residual SI cancellation of up to 35 to 40 dB, total SI cancellation of up to 110 to 130 dB, and an SINR improvement of up to 50 dB. Full article
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11 pages, 3227 KiB  
Article
Improving Spectral Efficiency in the SCMA Uplink Channel
by Sergio Vidal-Beltrán and José Luis López-Bonilla
Mathematics 2021, 9(6), 651; https://doi.org/10.3390/math9060651 - 18 Mar 2021
Cited by 3 | Viewed by 2622
Abstract
The Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) identified the technical requirements that the fifth generation of mobile communications networks (5G) had to meet; within these parameters are the following: an improved data rate and a greater number of [...] Read more.
The Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) identified the technical requirements that the fifth generation of mobile communications networks (5G) had to meet; within these parameters are the following: an improved data rate and a greater number of users connected simultaneously. 5G uses non-orthogonal multiple access (NOMA) to increase the number of simultaneously connected users, and by encoding data it is possible to increase the spectral efficiency (SE). In this work, eight codewords are used to transmit three bits simultaneously using Sparse Code Multiple Access (SCMA), and through singular value decomposition (SVD) the Euclidean distance between constellation points is optimized. On the other hand, applications of machine intelligence and machine intelligence in 5G and beyond communication systems are still developing; in this sense, in this work we propose to use machine learning for detecting and decoding the SCMA codewords using neural networks. This paper focuses on the Use Case of enhanced mobile broadband (eMBB), where higher data rates are required, with a large number of users connected and low mobility. The simulation results show that it is possible to transmit three bits simultaneously with a low bit error rate (BER) using SVD-SCMA in the uplink channel. Our simulation results were compared against recent methods that use spatial modulation (SM) and antenna arrays in order to increase spectral efficiency. In adverse Signal-to-Noise Ratio (SNR), our proposal performs better than SM, and antenna arrays are not needed for transmission or reception. Full article
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26 pages, 7393 KiB  
Article
Walsh–Hadamard Transform Based Non-Orthogonal Multiple Access (NOMA) and Interference Rejection Combining in Next-Generation HetNets
by M. Rehan Usman, M. Arslan Usman, Soo Young Shin, Gandeva Bayu Satrya, Rizwan A. Naqvi, Maria G. Martini and Christos Politis
Mathematics 2021, 9(4), 348; https://doi.org/10.3390/math9040348 - 9 Feb 2021
Cited by 6 | Viewed by 2776
Abstract
In heterogeneous networks (HetNets), non-orthogonal multiple access (NOMA) has recently been proposed for hybrid-access small-cells, promising a manifold network capacity compared to OMA. One of the major issues with the installation of a hybrid-access mechanism in small-cells is the cross-tier interference (intercell interference [...] Read more.
In heterogeneous networks (HetNets), non-orthogonal multiple access (NOMA) has recently been proposed for hybrid-access small-cells, promising a manifold network capacity compared to OMA. One of the major issues with the installation of a hybrid-access mechanism in small-cells is the cross-tier interference (intercell interference (ICI)) caused by the macrocell users (MUs) that are unable to establish a connection to the small-cell base station (SBS). In this paper, a joint strategy is proposed for hybrid-access small-cells using the Walsh–Hadamard transform (WHT) with NOMA and interference rejection combining (IRC) to achieve high performance gains and mitigate intercell interference (ICI), respectively. WHT is applied mathematically as an orthogonal variable spreading factor (OVSF) to achieve diversity in communication systems. When applied jointly with NOMA, it ensures better performance gains than the conventional NOMA. It reduces the bit error rate (BER) and enhances subsequent throughput performance of the system. IRC is used at the receiver side for managing the cross-tier interference caused by MUs that are unable to connect to the small-cell base station (SBS) for hybrid-access. The work considers both ideal and nonideal successive interference cancellation (SIC) conditions for NOMA. Mathematical modeling is provided for the proposed joint strategy for HetNets and the results validate it in terms of BER and subsequent user throughput performance, compared to the conventional NOMA approach. Full article
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11 pages, 2415 KiB  
Article
A Multi-Carrier Waveform Design for 5G and beyond Communication Systems
by Imran Baig, Umer Farooq, Najam Ul Hasan, Manaf Zghaibeh and Varun Jeoti
Mathematics 2020, 8(9), 1466; https://doi.org/10.3390/math8091466 - 1 Sep 2020
Cited by 13 | Viewed by 3097
Abstract
The next generation communication network (NGCN) is expected to provide higher spectral efficiency, low latency, large throughput and massive machine-to-machine type communications. In this regard, the design of the multi-carrier waveform (MCW) is posing a major research problem for the NGCN. To overcome [...] Read more.
The next generation communication network (NGCN) is expected to provide higher spectral efficiency, low latency, large throughput and massive machine-to-machine type communications. In this regard, the design of the multi-carrier waveform (MCW) is posing a major research problem for the NGCN. To overcome the stated problem, a lot of state-of-the-art work exists that proposes various MCW alternative to the standard orthogonal frequency division multiplexing (OFDM) waveform. It is true that OFDM was used in a number of real-time communication systems of fourth generation (4G) networks. However, their use in the upcoming fifth generation (5G) network is not very feasible. This is because of the strict requirements of 5G communication systems, which also extend beyond 5G systems; hence rendering the use of OFDM infeasible for newer communication standards. To satisfy the requirements of upcoming communication networks, there is a dire need for MCWs with better flexibility. In this regard, a precoding-based MCW has been proposed. The proposed MCW fulfills the requirements of the NGCN in terms of low peak-to-average power ratio (PAPR), high spectral efficiency and throughput. The MCW proposed in this work uses power-domain multiplexing such as non-orthogonal multiple access (NOMA) and phase rotation by using the selective mapping (SLM) and generalized chirp-like (GCL) precoding of the input signal to the universal filtered multi-carriers (UFMC) modulations. Statistical analysis of the PAPR is presented by using the complementary cumulative distribution function (CCDF). The MATLAB® simulations have been carried out to implement the CCDF of PAPR and results show that a PAPR gain of 5.4 dB is obtained when the proposed waveform is compared with the standard NOMA-UFMC waveform at clip rate of 10−3, using 4-QAM. Full article
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