Advances in Sixth Generation and Beyond (6G&B)

Topic Information

Dear Colleagues,

Emerging Internet of Things (IoT) applications aim to bring people, data, processes, and things together, to fulfill the needs of our everyday lives. It is expected that upcoming Sixth-Generation and Beyond (6G&B) wireless networks, known as more than an extension of 4G/5G, will be the backbone of IoT and other services and will support IoT systems by expanding their coverage, reducing latency, and enhancing data rate. Next-Generation (NextG) wireless systems will require a paradigm shift in how they are networked, organized, configured, optimized, and recovered automatically, based on their operating situations.

We invite authors to submit original contributions on all areas of NextG (6G&B) networks.

Topics of interest include, but are not limited to, the following:

• 6G and Beyond security standardization;
• 6G applications and use cases;
• 6G architectures and visions;
• 6G core networks;
• 6G direct links;
• 6G green networking and sustainability;
• 6G migration strategies;
• 6G physical layers and frequencies;
• 6G QoS and slicing;
• 6G RAN;
• 6G satellites and 3D networks;
• Al and machine learning for NextG wireless security;
• AI-/ML-driven network automation/orchestration techniques;
• Authentication, authorization, and accounting for 6G and Beyond security;
• Dynamic inter-domain service/slice design and provisioning;
• Edge and embedded edge cloud computing;
• Global 6G network automation;
• Localization in 6G;
• Physical layer security for 6G and Beyond;
• Secure, privacy-aware, and trustworthy IoT communications;
• Softwarization and virtualization in fixed and mobile networks;
• Trust models and trust handling/propagation for 6G and Beyond security;
• Zero-touch networks.

We look forward to receiving your contributions.

Prof. Dr. Luis Javier García Villalba
Dr. Ana Lucila Sandoval Orozco
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Computers computers	4.2	7.5	2012	16.3 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Entropy entropy	2.0	5.2	1999	21.8 Days	CHF 2600	Submit
Future Internet futureinternet	3.6	8.3	2009	17 Days	CHF 1600	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
IoT IoT	2.8	8.7	2020	25.7 Days	CHF 1400	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Telecom telecom	2.4	5.4	2020	26.3 Days	CHF 1200	Submit

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Published Papers (3 papers)

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All AI Applied Sciences Computers Electronics Entropy Future Internet Information IoT Sensors Telecom

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26 pages, 564 KB  

Open AccessArticle

6G-Oriented Joint Optimization of Semantic Compression and Transmission Power for Reliable IoV Emergency Communication

by Yuchen Zhou, Jianjun Wei, Mofan Luo, Bingtao He and Jian Chen

Electronics 2025, 14(24), 4937; https://doi.org/10.3390/electronics14244937 - 16 Dec 2025

Viewed by 183

Abstract

Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framework for semantic communication to address these challenges. We introduce a knowledge graph-based [...] Read more.

Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framework for semantic communication to address these challenges. We introduce a knowledge graph-based approach to represent information as semantic triples (structured entity-relation-attribute representations), whose importance is quantified using a Zipf distribution, enabling prioritized transmission. At the physical layer, a semantic-aware cooperative communication scheme is proposed to combat fading and enhance transmission reliability. The joint optimization of the number of transmitted triples and node power allocation is formulated as a cross-layer problem. To tackle this Mixed-Integer Nonlinear Programming (MINLP) problem with a hybrid action space, we employ the Multi-Pass Deep Q-Network (MP-DQN) algorithm, which is specifically designed for problems with hybrid discrete-continuous action spaces. Simulation results demonstrate that our framework dynamically adapts to channel states and semantic value, achieving up to 85% end-to-end success rate and improving convergence speed by approximately 40% compared to conventional methods. Full article

(This article belongs to the Topic Advances in Sixth Generation and Beyond (6G&B))

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22 pages, 4609 KB  

Open AccessArticle

Statistical CSI-Based Beamspace Transmission for Massive MIMO LEO Satellite Communications

by Qian Dong, Yafei Wang, Nan Hu, Yiming Zhu, Wenjin Wang and Li Chai

Entropy 2025, 27(12), 1214; https://doi.org/10.3390/e27121214 - 28 Nov 2025

Viewed by 350

Abstract

In multibeam low-Earth-orbit (LEO) satellite systems, precoding has emerged as a key technology for mitigating co-channel interference (CCI) and for improving spectral efficiency (SE). However, its practical implementation is challenged by the difficulty of acquiring reliable instantaneous channel state information (iCSI) and by [...] Read more.

In multibeam low-Earth-orbit (LEO) satellite systems, precoding has emerged as a key technology for mitigating co-channel interference (CCI) and for improving spectral efficiency (SE). However, its practical implementation is challenged by the difficulty of acquiring reliable instantaneous channel state information (iCSI) and by the high computational complexity induced by large-scale antenna arrays, making it incompatible with fixed codebook-based beamforming schemes commonly adopted in operational systems. In this analysis, we propose a beamspace transmission framework leveraging statistical CSI (sCSI) and achieves reduced computational complexity compared with antenna-domain precoding designs. Specifically, we first propose a low-complexity beam selection algorithm that selects a small subset of beams for each user terminal (UT) from a fixed beamforming codebook, using only the UTs’ two-dimensional (2D) angular information. To suppress CCI among beams, we then derive a beamspace weighted minimum mean square error (WMMSE) precoding scheme based on the equivalent beamspace channel matrix. The derivation employs an sCSI-based WMMSE (sWMMSE) formulation derived from an upper bound approximation of the ergodic sum rate, which provides a tighter estimate than the expected mean square error (MSE)-based lower bound approximation. Simulation results demonstrate that the proposed sCSI-based beamspace transmission scheme achieves a favorable trade-off between performance and computational complexity. Full article

(This article belongs to the Topic Advances in Sixth Generation and Beyond (6G&B))

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36 pages, 738 KB  

Open AccessArticle

Activity Detection and Channel Estimation Based on Correlated Hybrid Message Passing for Grant-Free Massive Random Access

by Xiaofeng Liu, Xinrui Gong and Xiao Fu

Entropy 2025, 27(11), 1111; https://doi.org/10.3390/e27111111 - 28 Oct 2025

Viewed by 499

Abstract

Massive machine-type communications (mMTC) in future 6G networks will involve a vast number of devices with sporadic traffic. Grant-free access has emerged as an effective strategy to reduce the access latency and processing overhead by allowing devices to transmit without prior permission, making [...] Read more.

Massive machine-type communications (mMTC) in future 6G networks will involve a vast number of devices with sporadic traffic. Grant-free access has emerged as an effective strategy to reduce the access latency and processing overhead by allowing devices to transmit without prior permission, making accurate active user detection and channel estimation (AUDCE) crucial. In this paper, we investigate the joint AUDCE problem in wideband massive access systems. We develop an innovative channel prior model that captures the dual correlation structure of the channel using three state variables: active indication, channel supports, and channel values. By integrating Markov chains with coupled Gaussian distributions, the model effectively describes both the structural and numerical dependencies within the channel. We propose the correlated hybrid message passing (CHMP) algorithm based on Bethe free energy (BFE) minimization, which adaptively updates model parameters without requiring prior knowledge of user sparsity or channel priors. Simulation results show that the CHMP algorithm accurately detects active users and achieves precise channel estimation. Full article

(This article belongs to the Topic Advances in Sixth Generation and Beyond (6G&B))

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