Special Issue "Key Intelligent Technologies for Wireless Communications and Internet of Things"
Deadline for manuscript submissions: 31 March 2024 | Viewed by 6044
Interests: intelligent signal analysis, signal sensing and recognition, AI-based wireless techniques
Special Issues, Collections and Topics in MDPI journals
Interests: fault detection and recognition; machine learning and data analytics over wireless networks; signal processing and analysis; cognitive radio and software defined radio; artificial intelligence; pattern recognition
Special Issues, Collections and Topics in MDPI journals
The upcoming sixth-generation (6G) wireless communication technologies will provide higher data rates, more connections, and wider network coverage to meet the needs of various application domains, such as metaverse, intelligent transportation, industry 5.0, and so on. However, confronted with the highly dynamic wireless environment and the increasing demand for wireless communication, the modeling of wireless communication problems becomes more and more difficult, and the complexity of problem-solving increases exponentially. Due to the advantages of artificial intelligence (AI) technology in complex nonlinear problems, it is widely believed that various intelligent technologies can be applied into wireless communication systems and Internet of things systems for improving performance and efficiency.
The goal of this Special Issue is to provide an overview of the latest developments regarding key intelligent technologies for wireless communications and Internet of things. Both theoretical and technical aspects are of interest.
Topics of interest include, but are not limited to, the following:
- AI-based signal detection, signal classification and signal processing;
- AI-based channel modeling, channel estimation and feedback;
- AI-based positioning, sensing and localization;
- AI-based beamforming and resource allocation;
- AI-based non-orthogonal multiple-access (NOMA);
- AI for IoT and massive connectivity;
- AI for integrated sensing and communications;
- AI for reconfigurable intelligent surface (RIS)-aided wireless communication;
- AI for massive MIMO and cell-free massive MIMO;
- AI for mmWave and Terahertz communication;
- AI for semantic communication;
- AI for green communication;
- AI for UAV communication;
- AI for ultra-reliable and low latency communication;
- AI-enabled techniques for robustness, security, and privacy in wireless communications and Internet of things.
Prof. Dr. Guan Gui
Prof. Dr. Yun Lin
Prof. Dr. Haitao Zhao
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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.
- artificial intelligence
- wireless communication
- Internet of things
- signal processing
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Wireless control modules in distributed smart home systems - technical, quality and energy efficiency analysis
Authors: Andrzej Ożadowicz
Affiliation: AGH University of Science and Technology, Krakow, Poland
Abstract: In buildings and houses, simple control modules for switching on and off electrical devices with wireless communication are used more and more often. As a result, these modules become distributed elements in local data transmission networks. Usually, users do not pay attention to the quality of their operation and, in particular, energy consumption. Meanwhile, depending on the operating conditions (location, distance of WiFi access points, etc.), the quality of communication and the level of power consumption change and can be an important item in the energy balance of buildings. The article presents the results of a series of laboratory experiments verifying the quality of operation and energy efficiency of selected smart home modules available on the commercial market, along with a qualitative and technical analysis of the proposed solutions.
Title: Estimation of Transmitting Antennas and SNR Using Advanced Machine Learning Algorithms
Authors: Ebrahim Karami
Affiliation: Department of Engineering and Applied Sciences, Memorial University, St. John’s, NL AB 3X5, Canada
Abstract: Wireless network optimization relies on accurate estimation of key parameters such as the number of transmitting antennas and the Signal-to-Noise Ratio (SNR). Traditional methods for estimating these parameters often face challenges in dynamically changing communication environments. In this paper, we propose an innovative approach that leverages state-of-the-art learning algorithms to precisely estimate the number of transmitting antennas and SNR using simulated data. We compare our machine learning models against traditional estimation methods, highlighting significant improvements in accuracy and adaptability.