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Mathematics
Special Issues
Mathematical Methods in Wireless Networks and IoT
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Mathematical Methods in Wireless Networks and IoT

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2696

Special Issue Editor

Dr. Lingfei Mo

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: wireless networks, RFID sensing, wireless sensor network, wearable sensors, machine learning and artificial intelligence, neuromorphic sensing and brain-like computing

Special Issue Information

Dear Colleagues,

Mathematics is a general means for human beings to accurately describe and deduce the abstract structures and modes of things. Therefore, it can be applied to any problem in the real world. For a long time, in order to solve various scientific problems encountered in engineering practice, many people created hosts of beautiful and amazing methods based on mathematics. With the increasing application of mathematical methods in wireless networks, wireless network technology has rapidly developed and shown great active vitality in various scenarios relying on wireless sensing, wireless positioning, low-power consumption, edge computing, data compression and coding, and other Internet of Things (IoT) technologies. It is believed that more technical fields that need wireless networks as a basic support will flourish in the future.

We believe that these future technical fields future cannot be separated from wireless network technology. Therefore, we need to explore more and more beautiful mathematical methods so that wireless network technology can continuously evolve and develop. At the same time, we should evaluate how to better apply mathematical methods in wireless networks.

Dr. Lingfei Mo
Guest Editor

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. Mathematics 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

  • wireless sensing
  • wireless localization
  • low-power wide-area network
  • edge computing in wireless network
  • data compression and coding for wireless network
  • machine learning for wireless network
  • wireless network and IoT applications
  • WSN and wireless mesh network
  • energy optimization in wireless networks
  • other mathematical problems in the wireless network

Published Papers (2 papers)

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Research

21 pages, 628 KiB  
Article
DQN-GNN-Based User Association Approach for Wireless Networks
by Ibtihal Alablani and Mohammed J. F. Alenazi
Mathematics 2023, 11(20), 4286; https://doi.org/10.3390/math11204286 - 14 Oct 2023
Cited by 1 | Viewed by 1101
Abstract
In the realm of advanced mobile networks, such as the fifth generation (5G) and beyond, the increasing complexity and proliferation of devices and unique applications present a substantial challenge for User Association (UA) in wireless systems. The problem of UA in wireless networks [...] Read more.
In the realm of advanced mobile networks, such as the fifth generation (5G) and beyond, the increasing complexity and proliferation of devices and unique applications present a substantial challenge for User Association (UA) in wireless systems. The problem of UA in wireless networks is multifaceted and requires comprehensive exploration. This paper presents a pioneering approach to the issue, integrating a Deep Q-Network (DQN) with a Graph Neural Network (GNN) to enhance user-base station association in wireless networks. This novel approach surpasses recent methodologies, including Q-learning and max average techniques, in terms of average rewards, returns, and success rate. This superiority is attributed to its capacity to encapsulate intricate relationships and spatial dependencies among users and base stations in wireless systems. The proposed methodology achieves a success rate of 95.2%, outperforming other methodologies by a margin of up to 5.9%. Full article
(This article belongs to the Special Issue Mathematical Methods in Wireless Networks and IoT)
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10 pages, 354 KiB  
Article
User Grouping, Precoding Design, and Power Allocation for MIMO-NOMA Systems
by Byungjo Kim and Jae-Mo Kang
Mathematics 2023, 11(4), 995; https://doi.org/10.3390/math11040995 - 15 Feb 2023
Viewed by 877
Abstract
In this paper, we study user grouping, precoding design, and power allocation for multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) systems. An optimization problem is formulated to the maximize the sum rate under a transmit power constraint at a base station and rate [...] Read more.
In this paper, we study user grouping, precoding design, and power allocation for multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) systems. An optimization problem is formulated to the maximize the sum rate under a transmit power constraint at a base station and rate constraints on users, which are nonconvex and combinatorial and thus very challenging to solve. To tackle this problem, we carry out the optimization in two steps. In the first step, exploiting the machine learning technique, we develop an efficient iterative algorithm for user grouping and precoding design. In the second step, we develop a power-allocation scheme in closed form by recasting the original problem into a useful and tractable convex form. The numerical results demonstrate that the proposed joint scheme, including user grouping, precoding design, and power allocation, considerably outperforms the existing schemes in terms of sum rate maximization, which increases the sum-rate up to 8–18%. In addition, the results show the larger the number of antennas or users, the bigger the performance gap, at the cost of less computational complexity. Full article
(This article belongs to the Special Issue Mathematical Methods in Wireless Networks and IoT)
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