Telecom

Much research effort has been conducted to introduce intelligence into communication networks in order to enhance network performance. Communication networks, both wired and wireless, are ever-expanding as more devices are increasingly connected to the Internet. This survey introduces machine learning and the motivations behind it for creating cognitive networks. We then discuss machine learning and statistical techniques to predict future traffic and classify each into short-term or long-term applications. Furthermore, techniques are sub-categorized into their usability in Local or Wide Area Networks. This paper aims to consolidate and present an overview of existing techniques to stimulate further applications in real-world networks.

Aaron Chen

Jeffrey Law

Michal Aibin

A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

通信网络中利用人工智能进行交通预测技术综述

Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors.

Kaveripakam Sathish

Ravikumar Chinthaginjala Venkata

Rajesh Anbazhagan

Giovanni Pau

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

水下无线传感器网络中无人水面艇与自主水下机器人定位与聚类的综述

Efficient Virtual Machine (VM) placement is a critical challenge in optimizing resource utilization in cloud data centers. This paper explores both exact and approximate methods to address this problem. We begin by presenting an exact solution based on a Multi-Objective Integer Linear Programming (MOILP) model, which provides an optimal VM Placement (VMP) strategy. Given the NP-completeness of the MOILP model when handling large-scale problems, we then propose an approximate solution using a Tabu Search (TS) algorithm. The TS algorithm is designed as a practical alternative for addressing these complex scenarios. A key innovation of our approach is the simultaneous optimization of three performance metrics: the number of accepted VMs, resource wastage, and power consumption. To the best of our knowledge, this is the first application of a TS algorithm in the context of VMP. Furthermore, these three performance metrics are jointly optimized to ensure operational efficiency (OPEF) and minimal operational expenditure (OPEX). We rigorously evaluate the performance of the TS algorithm through extensive simulation scenarios and compare its results with those of the MOILP model, enabling us to assess the quality of the approximate solution relative to the optimal one. Additionally, we benchmark our approach against existing methods in the literature to emphasize its advantages. Our findings demonstrate that the TS algorithm strikes an effective balance between efficiency and practicality, making it a robust solution for VMP in cloud environments. The TS algorithm outperforms the other algorithms considered in the simulations, achieving a gain of 2% to 32% in OPEF, with a worst-case increase of up to 6% in OPEX.

Mohamed Koubàa

Rym Regaieg

Abdullah S. Karar

Muhammad Nadeem

Faouzi Bahloul

A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search

使用ILP和禁忌搜索优化虚拟机放置的多目标方法

This paper addresses the problem of side lobe interference in 5G networks by proposing a unique collaborative beamforming strategy based on Deep Q-Network (DQN) reinforcement learning. Our method, which operates in the sub-6 GHz band, maximizes beam steering and power management by using a two-antenna system with DQN-controlled phase shifters. We provide an OFDM cellular network environment where inter-cell interference is managed while many base stations serve randomly dispersed customers. In order to reduce interference strength and improve signal-to-interference-plus-noise ratio (SINR), the DQN agent learns to modify the interference angle. Our model integrates experience replay memory with a long short-term memory (LSTM) recurrent neural network for time series prediction to enhance learning stability. The outcomes of our simulations show that our suggested DQN approach works noticeably better than current DQN and Q-learning methods. In particular, our technique reaches a maximum of 29.18 dB and a minimum of 5.15 dB, whereas the other approaches only manage 0.77–27.04 dB. Additionally, we significantly decreased the average interference level to 5.42 dB compared to competing approaches of 38.84 dB and 34.12 dB. The average sum-rate capacity is also increased to 3.90 by the suggested strategy, outperforming previous approaches. These findings demonstrate how well our cooperative beamforming method reduces interference and improves overall network performance in 5G systems.

Alaelddin F. Y. Mohammed

Salman Md Sultan

Sakshi Patni

Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks

基于DQN的协作波束成形用于5G及未来网络中的干扰缓解

Digital terrestrial television is now implemented in many countries worldwide and is now mature. Digital Video Broadcasting-Terrestrial, second generation (DVB-T2) is the European standard adopted or deployed by European and African countries which uses Orthogonal Frequency-Division Multiplexing (OFDM) modulation to achieve good throughput performance. However, its main particularity is the number of subcarriers operated for OFDM modulation which varies from 1024 to 32,768 subcarriers. Also, mobile reception is planned in DVB-T2 in addition to rooftop antenna and portable receptions planned in DVB-T. However, the main challenge of DVB-T2 for mobile reception is the presence of a carrier frequency offset (CFO) which degrades the system performance by inducing an Intercarrier Interference (ICI) on the DVB-T2 signal. This paper evaluates the system performance in the presence of the CFO when Gaussian noise and a TU6 channel are applied. Universal Filtered Multicarrier (UFMC) and non-uniform constellations (NUCs) have previously demonstrated good performance in comparison with OFDM and Quadrature Amplitude Modulation (QAM) in DVB-T2. The impact of CFO on the UFMC- and NUC-based DVB-T2 system is additionally investigated in this work. The results demonstrate that the penalties induced by CFO insertion in UFMC- and NUC-based DVB-T2 are highly reduced in comparison to those for the native DVB-T2. At a bit error rate (BER) of 10−3, the CFO penalties induced by the native DVB-T2 are 0.96dB and 4 dB, respectively, when only Additive White Gaussian Noise (AWGN) is used and when TU6 is additionally considered. The penalties are equal to 0.84dB and 0.2dB for UFMC/NUC-based DVB-T2.

Sonia Zannou

Anne-Carole Honfoga

Michel Dossou

Véronique Moeyaert

Carrier Frequency Offset Impact on Universal Filtered Multicarrier/Non-Uniform Constellations Performance: A Digital Video Broadcasting—Terrestrial, Second Generation Case Study

载波频率偏移对通用滤波多载波/非均匀星座性能的影响：数字视频广播—地面，第二代案例研究

The proliferation of drones in civilian environments has raised growing concerns about their misuse, highlighting the need to develop efficient detection systems to protect public and private spaces. This article presents a hybrid approach for UAV detection that combines two artificial-intelligence-based methods to improve system accuracy. The first method uses a software-defined radio (SDR) to analyze the radio spectrum, employing autoencoders to detect drone control signals and identify the presence of these devices. The second method is a computer vision module consisting of fixed cameras and a PTZ camera, which uses the YOLOv10 object detection algorithm to identify UAVs in real time from video sequences. Additionally, this module integrates a localization and tracking algorithm, allowing the tracking of the intruding UAV’s position. Experimental results demonstrate high detection accuracy, a significant reduction in false positives for both methods, and remarkable effectiveness in UAV localization and tracking with the PTZ camera. These findings position the proposed system as a promising solution for security applications.

Pablo López-Muñoz

Luis Gimeno San Frutos

Christian Abarca

Francisco José Alegre

Jose Luis Calle

Jose F. Monserrat

Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques

基于混合人工智能的无人机检测、定位与跟踪系统：采用软件定义无线电与计算机视觉技术

This paper presents a unified performance metric for evaluating the chronological wild geese optimization (CWGO) algorithm in wireless sensor networks (WSNs). The metric combines key performance factors—energy consumption, delay, distance, and trust—into a single measure using Lagrange interpolation, providing a more comprehensive assessment of WSN algorithms. We evaluate CWGO against E-CERP, EECHIGWO, DUCISCA, and DE-SEP across static and dynamic sensor node configurations in various wireless technologies, including LoRa, Wi-Fi, Zigbee, and Bluetooth low energy (BLE). The results show that CWGO consistently outperforms the other algorithms, especially in larger node configurations, demonstrating its scalability and robustness in static and dynamic environments. Moreover, the unified metric reveals significant performance gaps with EECHIGWO, which underperforms all wireless technologies. DUCISCA and DE-SEP show moderate and fluctuating results, underscoring their limitations in larger networks. While E-CERP performs competitively, it generally lags behind CWGO. The unified metric offers a holistic view of algorithm performance, conveying clearer comparisons across multiple factors. This study emphasized the importance of a unified evaluation approach for WSN algorithms and positions CWGO as a superior solution for efficient cluster head selection and routing optimization in diverse WSN scenarios. While CWGO demonstrates superior performance in simulation, future research should validate these findings in real-world deployments, accounting for hardware limitations and in a highly dynamic environment. Further optimization of the unified metrics’ computational efficiency could enhance its real-time applicability in larger, energy-resource-constrained WSNs.

Zoren P. Mabunga

Jennifer C. Dela Cruz

Renato R. Maaliw

A Comparative Analysis of Advanced Routing and Cluster Head Selection Algorithm Using Lagrange Interpolation

基于拉格朗日插值的高级路由与簇头选择算法比较分析

The vision of the sixth generation of communication systems, commonly known as 6G, entails a connected world that provides ubiquitous connectivity and fosters the digital transformation of society. As the number of devices, services, and users continues to grow, intelligent solutions are expected to facilitate this transformation. This paper considers meta-learning as a pivotal paradigm for 6G systems, detailing its principles, algorithms, and theoretical underpinnings. The methodology involves integrating meta-learning with three potential 6G technologies: RF-based communication systems, optical communication systems, and molecular communication systems. The findings reveal the distinct characteristics of these technologies and demonstrate the potential benefits and challenges of incorporating meta-learning algorithms. Practical implications highlight how meta-learning can enhance the efficiency and adaptability of 6G systems, addressing the growing demand for intelligent and seamless communication networks.

Arooba Zeshan

Messaoud Ahmed Ouameur

Muhammad Zeshan Alam

Tuan-Anh D. Le

From Theory to Practice: Implementing Meta-Learning in 6G Wireless Infrastructure

从理论到实践：在6G无线基础设施中实施元学习

Abbreviation
Journal Impact	2.19
Quartiles(Global)	TELECOMMUNICATIONS(Q3)

ISSN	2673-4001
h-index	11

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2020，Proportion of original research papers: 0.00%，Self Citation Rate:4.80%， Gold OA Rate: 100.00%
Journal Fees	$Details
Average review cycle	网友分享经验：11 Weeks

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A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search

Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks

Carrier Frequency Offset Impact on Universal Filtered Multicarrier/Non-Uniform Constellations Performance: A Digital Video Broadcasting—Terrestrial, Second Generation Case Study

A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search

Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques

A Comparative Analysis of Advanced Routing and Cluster Head Selection Algorithm Using Lagrange Interpolation

From Theory to Practice: Implementing Meta-Learning in 6G Wireless Infrastructure

Carrier Frequency Offset Impact on Universal Filtered Multicarrier/Non-Uniform Constellations Performance: A Digital Video Broadcasting—Terrestrial, Second Generation Case Study