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19 pages, 9444 KiB

Open AccessFeature PaperArticle

Enhanced 3D Outdoor Positioning Method Based on Adaptive Kalman Filter and Kernel Density Estimation for 6G Wireless System

by Kyounghun Kim, Seongwoo Lee, Byungsun Hwang, Jinwook Kim, Joonho Seon, Soohyun Kim, Youngghyu Sun and Jinyoung Kim

Electronics 2024, 13(23), 4623; https://doi.org/10.3390/electronics13234623 - 23 Nov 2024

Viewed by 794

Abstract

The implementation of accurate positioning methods in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments has been emphasized for seamless 6G application services. In LOS environments with unobstructed paths between the transmitter and receiver, accurate tracking essential for seamless 6G services is achievable. However, [...] Read more.

The implementation of accurate positioning methods in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments has been emphasized for seamless 6G application services. In LOS environments with unobstructed paths between the transmitter and receiver, accurate tracking essential for seamless 6G services is achievable. However, accurate three-dimensional (3D) outdoor positioning has been challenging to achieve in NLOS environments where positioning accuracy may be severely degraded. In this paper, a novel 3D outdoor positioning method considering both LOS and NLOS environments is proposed. Considering the practical positioning systems, the data received from satellites often contain null values and outliers. Thus, a kernel density estimation (KDE)-based outlier removal method is used for effectively detecting the null values and outliers through temporal correlation analysis. A dilution of precision-based adaptive Kalman filter (DOP-AKF) is proposed to mitigate the effects of an NLOS environment. In the proposed method, the DOP-AKF can optimize the performance of the 3D positioning system that dynamically adapts to complex environments. Experimental results show that the proposed method can improve 3D positioning accuracy by up to 18.84% compared to conventional methods. Therefore, the proposed approach can be suggested as a promising solution for 3D outdoor positioning in 6G wireless systems. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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12 pages, 621 KiB

Open AccessArticle

Maximum Doppler Shift Identification Using Decision Feedback Channel Estimation

by Yudai Handa, Hiroya Hayakawa, Riku Tanaka, Kosuke Tamura, Jaesang Cha and Chang-Jun Ahn

Electronics 2024, 13(20), 4113; https://doi.org/10.3390/electronics13204113 - 18 Oct 2024

Viewed by 1230

Abstract

This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler [...] Read more.

This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler shifts. This leads to inter-carrier interference (ICI), significantly degrading communication quality. To mitigate this effect, systems that estimate the maximum Doppler shift and adaptively adjust communication parameters have been extensively studied. One of the most promising methods for maximum Doppler shift estimation involves inserting pilot signals at both the beginning and end of the packet. Although this method achieves high estimation accuracy, it introduces significant latency due to the insertion of the pilot signal at the packet’s end. To address this issue, this paper proposes a new method for rapid estimation using DFCE. The proposed approach compensates for faded signals using channel state information obtained from decision feedback. By treating the compensated signal as a reference, the Doppler shift can be accurately estimated without the need for pilot signals at the end of the packet. This method not only maintains high estimation accuracy but also significantly reduces the latency associated with conventional techniques, making it well-suited for the requirements of next-generation communication systems. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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14 pages, 1481 KiB

Open AccessArticle

Hybrid Beamforming Structure Using Grouping with Reduced Number of Phase Shifters in Multi-User MISO

by Hiroya Hayakawa, Yudai Handa, Riku Tanaka, Kosuke Tamura, Jaesang Cha and Chang-Jun Ahn

Electronics 2024, 13(20), 3994; https://doi.org/10.3390/electronics13203994 - 11 Oct 2024

Viewed by 1252

Abstract

This paper proposes a novel hybrid beamforming (HBF) structure for gain-aware grouping transmit antennas and users in multiuser multiple-input single-output (MU-MISO) systems. In the conventional HBF structure, all transmit antennas form a beam to each user. In this case, the gain of each [...] Read more.

This paper proposes a novel hybrid beamforming (HBF) structure for gain-aware grouping transmit antennas and users in multiuser multiple-input single-output (MU-MISO) systems. In the conventional HBF structure, all transmit antennas form a beam to each user. In this case, the gain of each antenna varies depending on the location of the base station and each user, and the transmit power after the digital beamformer is allocated to the antenna with the smallest gain. Signals transmitted from antennas with small gains are susceptible to noise and interference. Therefore, this paper proposes an HBF structure in which only the antenna with the highest gain forms the beam for each user. In the proposed scheme, the transmitting antennas are grouped and the beam is formed only by the group of antennas with the highest gain for each user. Simulation results show that the proposed scheme can reduce the number of phase shifters used on the transmit side compared to the conventional HBF scheme while maintaining sum-rate performance when the number of transmit antennas and users are the same. It was also shown that there is a trade-off between the reduction in the number of phase shifters used to form the beam and the improvement in performance as the number of transmit antennas increases. Furthermore, it is shown that when antenna selection is used, although there is a trade-off between the number of phase shifters and performance improvement, the number of phase shifters can be reduced while maintaining performance even when the number of transmit antennas increases. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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21 pages, 2816 KiB

Open AccessArticle

Reinforcement Learning-Based Resource Allocation and Energy Efficiency Optimization for a Space–Air–Ground-Integrated Network

by Zhiyu Chen, Hongxi Zhou, Siyuan Du, Jiayan Liu, Luyang Zhang and Qi Liu

Electronics 2024, 13(9), 1792; https://doi.org/10.3390/electronics13091792 - 6 May 2024

Cited by 2 | Viewed by 1842

Abstract

With the construction and development of the smart grid, the power business puts higher requirements on the communication capability of the network. In order to improve the energy efficiency of the space–air–ground-integrated power three-dimensional fusion communication network, we establish an optimization problem for [...] Read more.

With the construction and development of the smart grid, the power business puts higher requirements on the communication capability of the network. In order to improve the energy efficiency of the space–air–ground-integrated power three-dimensional fusion communication network, we establish an optimization problem for joint air platform (AP) flight path selection, ground power facility (GPF) association, and power control. In solving the problem, we decompose the problem into two subproblems, one is the AP flight path selection subproblem and the other is the GPF association and power control subproblem. Firstly, based on the GPF distribution and throughput weights, we model the AP flight path selection subproblem as a Markov Decision Process (MDP) and propose a multi-agent iterative optimization algorithm based on the comprehensive judgment of GPF positions and workload. Secondly, we model the GPF association and power control subproblem as a multi-agent, time-varying K-armed bandit model and propose an algorithm based on multi-agent Temporal Difference (TD) learning. Then, by alternately iterating between the two subproblems, we propose a reinforcement learning (RL)-based joint optimization algorithm. Finally, the simulation results indicate that compared to the three baseline algorithms (random path, average transmit power, and random device association), the proposed algorithm improves an overall energy efficiency of the system of 16.23%, 86.29%, and 5.11% under various conditions (including different noise power levels, GPF bandwidth, and GPF quantities), respectively. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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13 pages, 720 KiB

Open AccessArticle

High Accuracy Localization Scheme Using 1-Bit Side Information: Achievability from a GDoP Perspective

by Suah Park, Jiyoung Hwang, Ilmu Byun and Sang Won Choi

Electronics 2024, 13(8), 1574; https://doi.org/10.3390/electronics13081574 - 20 Apr 2024

Viewed by 1216

Abstract

In this paper, we provide a novel methodology for high-precision positioning that utilizes 1-bit additional information, which applies to various positioning techniques. The proposed approach leverages binary information to indicate if a user is within a specified space of interest and refines the [...] Read more.

In this paper, we provide a novel methodology for high-precision positioning that utilizes 1-bit additional information, which applies to various positioning techniques. The proposed approach leverages binary information to indicate if a user is within a specified space of interest and refines the estimated location information outside this area. By matching the estimated locations outside the area of interest with the valid location information within, this methodology corrects the positional data obtained through any arbitrary positioning technique, aligning the estimated positions with the intended spatial boundaries. Performance analysis metrics, such as Average Positioning Error (APE) and Cumulative Distribution Function for positioning coverage, were employed to assess the effectiveness of the proposed methods. Numerical simulations demonstrate how the proposed method enhances the averaged positioning accuracy, significantly outperforming the conventional time of arrival method. Furthermore, the proposed positioning correction methodology demonstrates validated feasibility applicable to an arbitrary existing positioning method. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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15 pages, 644 KiB

Open AccessArticle

Enhancing Small-Cell Capacity with Wireless Backhaul

by Ran Tao and Wuling Liu

Electronics 2024, 13(4), 797; https://doi.org/10.3390/electronics13040797 - 19 Feb 2024

Viewed by 1198

Abstract

Recently, hyperdense small cells have been proposed to meet the challenge of the tremendous increment in cellular data service requirements. To reduce the deployment cost, as well as operated cost, these small cells are usually connected to limited backhauls, in which case the [...] Read more.

Recently, hyperdense small cells have been proposed to meet the challenge of the tremendous increment in cellular data service requirements. To reduce the deployment cost, as well as operated cost, these small cells are usually connected to limited backhauls, in which case the backhaul capacity may become a bottleneck in busy hours. In this paper, we propose an optimal scheme for the small cells to utilize the macrocell links as its wireless backhaul. Based on stochastic geometry, the analytical expressions of network capacity with in-band and out-band wireless backhaul are derived and validated using simulation results. The optimized results show that our proposed scheme can significantly improve the network performance in scenarios with a high traffic load. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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13 pages, 498 KiB

Open AccessArticle

An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

by Xuan Zhou

Electronics 2024, 13(4), 791; https://doi.org/10.3390/electronics13040791 - 18 Feb 2024

Cited by 1 | Viewed by 1237

Abstract

With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, [...] Read more.

With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, can be promisingly enhanced by reconfigurable intelligent surfaces (RIS). Under this context, we explore a system comprising a small base-station (SBS) with limited cache capacity, two users, and one RIS. The SBS transmits the contents from the cache or fetches them from the remote backhaul hub to communicate with users through directional and possibly reflective channels. In this point-to-multipoint connection, non-orthogonal multiple access (NOMA) is applied, improving the capacity of the system. To minimize the outage probability, we first propose a caching policy from entropy perspective, based on which we investigate the beamforming and power allocation problem. The issue, however, is non-convex and involves multi-dimensional optimization. To address this, we introduce an efficient block successive upper-bound minimization algorithm, grounded in Gershgorin’s circle theorem. This algorithm aims to find the globally optimal solution for power allocation and RIS beamformer, considering both the channel condition and content popularity. Numerical studies are performed to verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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15 pages, 1697 KiB

Open AccessArticle

A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems

by Hyun Woo Lee and Sang Won Choi

Electronics 2023, 12(23), 4740; https://doi.org/10.3390/electronics12234740 - 22 Nov 2023

Cited by 1 | Viewed by 1485

Abstract

In this paper, we provide a light-weighted Machine Learning (ML) approach to channel estimation for New-Radio (NR) systems. Specifically, based on the equivalence between the Channel Impulse Response (CIR) in the time domain and its corresponding Channel Frequency Response (CFR) in the frequency [...] Read more.

In this paper, we provide a light-weighted Machine Learning (ML) approach to channel estimation for New-Radio (NR) systems. Specifically, based on the equivalence between the Channel Impulse Response (CIR) in the time domain and its corresponding Channel Frequency Response (CFR) in the frequency domain, the light-weighted ML model for the channel estimation is shown to be established in comparison to the existing ML-based channel estimator. Furthermore, for practical use, the quantized weights for the light-weighted ML-based estimator are shown to be feasible without significant performance degradation in the sense of mean square error (MSE), which shows the effectiveness of the proposed approach from the perspective of memory overhead. Consequently, we show that there exists Signal to Noise Ratio (SNR) gain in comparison with the existing ML-based estimator, which is validated by numerical results considering the Sounding Reference Signal (SRS) for NR in the 3rd Generation Partnership Project (3GPP). Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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22 pages, 6977 KiB

Open AccessArticle

Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach

by Radwa Ahmed Osman

Electronics 2023, 12(19), 4023; https://doi.org/10.3390/electronics12194023 - 24 Sep 2023

Cited by 5 | Viewed by 3226

Abstract

Autonomous intelligent transportation systems consistently require effective and secure communication through vehicular networks, enabling autonomous vehicle communication. The reduction of traffic congestion, the alerting of approaching emergency vehicles, and assistance in low visibility traffic are all made possible by effective communication between autonomous [...] Read more.

Autonomous intelligent transportation systems consistently require effective and secure communication through vehicular networks, enabling autonomous vehicle communication. The reduction of traffic congestion, the alerting of approaching emergency vehicles, and assistance in low visibility traffic are all made possible by effective communication between autonomous vehicles and everything (AV2X). Therefore, a new adaptive AV2X model is proposed in this paper to improve the connectivity of vehicular networks. This proposed model is based on the optimization method and a distributed deep learning model. The presented approach optimizes the inter-vehicle location if required for ensuring effective communication between the autonomous vehicle (AV) and everything (X) using the Lagrange optimization algorithm. Furthermore, the system is evaluated in terms of energy efficiency and achievable data rate based on the optimal inter-vehicle position to show the significance of the proposed approach. To meet the stated goals, the ideal inter-vehicle position is predicted using a distributed deep learning model by learning from mathematically generated data and defined as a restricted optimization problem using the Lagrange optimization technique to improve communication between AV2X under various environmental conditions. To demonstrate the efficiency of the suggested model, the following characteristics are considered: vehicle dispersion, vehicle density, vehicle mobility, and speed. The simulation results show the significance of the proposed model in terms of energy efficiency and achievable data rate compared with other proposed models. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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Review

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19 pages, 1239 KiB

Open AccessReview

Channel Estimation in 5G-and-Beyond Wireless Communication: A Comprehensive Survey

by Pulok Tarafder, Chanjun Chun, Arif Ullah, Yonggang Kim and Wooyeol Choi

Electronics 2025, 14(4), 750; https://doi.org/10.3390/electronics14040750 - 14 Feb 2025

Viewed by 1357

Abstract

The next generation of mobile networks is predicted to deliver high data speeds, lower latency, and increase the spectral and energy efficiency of wireless communication systems. Several technologies are being investigated for usage in 5G networks. Massive multiple-input multiple-output (mMIMO) systems are one [...] Read more.

The next generation of mobile networks is predicted to deliver high data speeds, lower latency, and increase the spectral and energy efficiency of wireless communication systems. Several technologies are being investigated for usage in 5G networks. Massive multiple-input multiple-output (mMIMO) systems are one of the most promising technologies for enabling 5G. Even after the recent advancements and research, numerous challenges still exist for channel estimation for mMIMO systems. In the context of pilot contamination and feedback overhead, this study tracks the most recent developments in research on mMIMO system difficulties. The primary goals of this study are to identify the problems with channel estimation, provide a summary of the cutting-edge solutions suggested in the literature, and then discuss newly emerging open research issues that must be taken into account for the implementation of beyond-5G networks. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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21 pages, 1059 KiB

Open AccessReview

A Comprehensive Survey on Machine Learning Methods for Handover Optimization in 5G Networks

by Senthil Kumar Thillaigovindhan, Mardeni Roslee, Sufian Mousa Ibrahim Mitani, Anwar Faizd Osman and Fatimah Zaharah Ali

Electronics 2024, 13(16), 3223; https://doi.org/10.3390/electronics13163223 - 14 Aug 2024

Cited by 4 | Viewed by 3623

Abstract

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a [...] Read more.

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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25 pages, 2482 KiB

Open AccessReview

Key Technologies for 6G-Enabled Smart Sustainable City

by Nahyun Kim, Gayeong Kim, Sunghoon Shim, Sukbin Jang, Jiho Song and Byungju Lee

Electronics 2024, 13(2), 268; https://doi.org/10.3390/electronics13020268 - 7 Jan 2024

Cited by 10 | Viewed by 3460

Abstract

With the advancement of information and communication technologies (ICTs), the way we live and communicate with each other is changing rapidly. As urban environments continue to evolve, the smart sustainable city (SSC) has sparked considerable attention. We are hoping for a new era [...] Read more.

With the advancement of information and communication technologies (ICTs), the way we live and communicate with each other is changing rapidly. As urban environments continue to evolve, the smart sustainable city (SSC) has sparked considerable attention. We are hoping for a new era in which numerous devices and machines including vehicles, sensors, and robots are all connected to communicate, respond, and operate in real time. The next-generation communication system, the sixth generation (6G), is expected to play a crucial role in improving the efficiency of urban operations and services. In this paper, we first provide the recent trends and key features of standardization in the SSC. To make the future SSC, we highlight key candidate technologies of 6G such as non-terrestrial networks, advanced mobile edge computing, vision-aided wireless communication, artificial intelligence (AI)-based wireless communication, and integrated sensing and communication. We put forth the main technical challenges given to each prime technology along with the potential benefits to pave the way for 6G-enabled SSC. We further address how the potential benefits of prime technologies enable various urban practice cases for 6G-enabled SSC. Full article

(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)

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